SCOTT LEIMROTH ← AI & Tech
GB10DGX Spark Benchmarks

Single-Spark interactive scorecard: every model at its own best

This table covers models running on ONE DGX Spark.

Thirty-one model configurations benchmarked on a single NVIDIA DGX Spark (GB10, 121.6 GiB unified memory), each served at its own best setup, so engines, quantisations and speculative-decoding methods differ from column to column by design. Every cell shows a 0 to 10 standing within its row, the raw measurement, and the exact configuration that produced it. Scores compare within a row only. There are no column totals, because adding a speed standing to an OCR standing gives a number that means nothing.

Order the models your way. Each column is a model; each row is one thing measured. Click a ROW heading to make it priority 1, then another for priority 2, and so on. The models re-sort left to right by a weighted blend in which earlier priorities count more (with three picks the weights are 3, 2, 1). The small number under each model name is its current weighted standing. A row a model was never entered in counts as zero in that standing, so an untested model sorts after any model with a real score there, however small; the label says how many of your picks it is missing, and those cells stay N/A with their reason rather than being given a made-up score. Click a numbered heading again to drop it.

The rest are listed above the table: click one to add it. Click a MODEL name in the table to remove it again, so you can strip the table down to the two or three you are comparing, or use Show all models.

Weighting: pick one below. It sets how much your first priority counts against your later picks when the models re-sort.
 Show:
What is measuredone row per measure · one column per model RadixArk FP4 27BSGLang + DFlash2 · vision ✓ NVIDIA Qwen3.8-27B NVFP4official local edition · SGLang + DFlash2 · vision ✓ Qwen 27B apostateuncensored · F16 · no vision in this build Qwen3.8-27B BF16 base16-bit yardstick · ngram k5 · vision not entered Leimroth 3 (based on Qwen3.8-27B)TensorFold 0.6.0 · NVFP4 · DFlash2 · images via companion model · abliterated build Unbound LR7Qwen3.8-Flash-Next (Vontra MLX 4-bit MTP) on TensorFold 0.5.0 + wait fix · no vision · abliterated build Unsloth 27B (vLLM)NVFP4 · MTP-5 · vision ✓ · same weights as the SGLang column QUASAR-QAT 27BNVFP4 QAT, all layers · MTP-2 · vision ✓ Qwen 27B orcarouteruncensored · NVFP4 · vision ✓ Qwen 27B orcarouter (FP8)uncensored · FP8 · vision not verified this pass Unsloth 27B (SGLang)NVFP4 · DFlash2 · vision ✓ · same weights as the vLLM column RadixArk BF16-headSGLang + DFlash2 · vision ✓ Leimroth 5 (Qwen3.6-35B-A3B)NVFP4 · MTP-3 · vision ✓ · abliterated, below adoption bar DeepSeek-V4-Flashcommunity ABLITERATED build · EXL3 + DSpark draft · no vision in this build Qwen3.6-35B-A3BNVFP4 · MoE · vision ✓ Apodex-1.1-miniBF16, no NVFP4 build · MTP-3 · vision ✓ · agent-tuned 35B-A3B Ornith-1.5-35B-A3BvLLM 0.28 · NVFP4 · no drafter (spec n/a) · vision not verified this pass NVIDIA Nemotron 3.5 LightningvLLM 0.28 · NVFP4 · DSpark draft head · vision not verified this pass Qwen3.8-Flash-NextGGUF · llama.cpp · vision ✓ Qwen3.8-Flash-NextTensorFold · MLX 4-bit · MTP · vision ✓ Qwen3.5-122BNVFP4 MoE · vision ✓ Nemotron-Omni-30BNVFP4 · vision ✓ · audio ✓ Muse Glimmer-30BvLLM 0.28 · NVFP4 · DFlash draft head · vision not verified this pass Dream Shimmer LR6 (stock drafter)vLLM 0.28 · FP8-per-block · stock DFlash draft head · CUDA graphs · vision ✓ Dream Shimmer LR6 (z-lab drafter)vLLM 0.28 · FP8-per-block · z-lab DFlash2 draft head · CUDA graphs · vision ✓ GLM-4.7-FlashNVFP4 · no vision in this build GLM-5.3-Flash EXL3 (K2)EXL3 · MTP-2 · no vision in this build Gemma-4-26B-A4BNVFP4 · vision ✓ Gemma-4-31B-it-uncensored-hereticuncensored · BF16 · vision not verified this pass Ling-3.0-Flash INT4INT4 · MTP · no vision in this build olmOCR-2-7BQ4_K_M · llama.cpp · vision ✓
Agent tasks 1055/60 - 60 items - 0 LOST 8.552/60 - 60 items - 2 context artifacts 953/60 - 60 items - 2 lost 5.543/60 - 60 items - 2 lost 1057/60 - 60 items (a-tool 12/12, a-seq 12/12, a-schema 11/12, a-turn 10/12, a-ctx 12/12) 1056/60 - 60 items (a-tool 10/12, a-seq 10/12, a-schema 12/12, a-turn 12/12, a-ctx 12/12) 9169/191 - 88.5% - 191 items 8.552/60 - 60 items - 2 lost 9.554/60 - 60 items (a-tool 12/12, a-seq 10/12, a-schema 12/12, a-turn 10/12, a-ctx 10/12) N/Anot entered - not yet measured on this column 851/60 - 60 items - 4 lost 5.546/60 - 60 items - 9 LOST 8.553/60 - 60 items - 2 context artifacts 1055/60 - 60 items - 0 LOST 8.552/60 - 60 items - 2 lost 1055/60 - 60 items - 0 LOST 8.552/60 - 60 items - 0 lost (a-tool 12/12, a-seq 11/12, a-schema 11/12, a-turn 6/12, a-ctx 12/12) 7.549/60 - 60 items - 0 lost (a-tool 11/12, a-seq 8/12, a-schema 11/12, a-turn 10/12, a-ctx 9/12) 6.548/60 - 60 items - 8 LOST 1057/60 - 60 items (a-tool 11/12, a-seq 11/12, a-schema 11/12, a-turn 12/12, a-ctx 12/12) 8.553/60 - 60 items - 0 LOST - 5 context artifacts 851/60 - 60 items - 4 lost 1056/60 - 60 items - 0 lost (a-tool 10/12, a-seq 11/12, a-schema 12/12, a-turn 11/12, a-ctx 12/12) 9.554/60 - 60 items (a-tool 10/12, a-seq 11/12, a-schema 12/12, a-turn 9/12, a-ctx 12/12) 851/60 - 60 items (a-tool 8/12, a-seq 10/12, a-schema 12/12, a-turn 9/12, a-ctx 12/12) - 51/60 in all three runs 850/60 - 60 items - 0 lost 6.550/60 - 60 items - 0 LOST (6 a-ctx/a-turn items exceed the 8k ctx: clean 400s) 7.550/60 - 60 items - 2 lost 851/60 - 60 items (a-tool 9/12, a-seq 11/12, a-schema 12/12, a-turn 10/12, a-ctx 9/12) 6.549/58 scoreable - 60 items - 2 harness-artifact, 0 wrong-answer excess 0could not be tested - cannot call tools - 60 items attempted 0/0
Tools & structured output 9.523/24 - 24 items 9.523/24 - 24 items - a-tool 12/12, a-schema 11/12 9.523/24 - 24 items 820/24 - 24 items - a-tool 11/12, a-schema 9/12 9.523/24 - 24 items - a-tool 12/12, a-schema 11/12 922/24 - 24 items - a-tool 10/12, a-schema 12/12 9.5127/138 - 92.0% - 191-item suite 922/24 - 24 items - a-tool 10/12, a-schema 12/12 1024/24 - 24 items - a-tool 12/12, a-schema 12/12 N/Anot entered - not yet measured on this column 9.523/24 - 24 items 9.523/24 - 24 items 1024/24 - 24 items - a-tool 12/12, a-schema 12/12 9.523/24 - 24 items 9.523/24 - 24 items 9.523/24 - 24 items - a-tool 11/12, a-schema 12/12 9.523/24 - 24 items - a-tool 12/12, a-schema 11/12 922/24 - 24 items - a-tool 11/12, a-schema 11/12 922/24 - 24 items 922/24 - 24 items - a-tool 11/12, a-schema 11/12 1024/24 - 24 items - PERFECT 9.523/24 - 24 items 922/24 - 24 items - a-tool 10/12, a-schema 12/12 922/24 - 24 items - a-tool 10/12, a-schema 12/12 820/24 - 24 items (a-tool 8/12, a-schema 12/12) - runs: 21, 20, 20 7.519/24 - 24 items 9.523/24 - 24 items - a-tool 12/12 PERFECT, a-schema 11/12 8.521/24 - 24 items 8.521/24 - 24 items - a-tool 9/12, a-schema 12/12 8.521/24 - 24 items - a-tool 11/12, a-schema 10/12 0tested and failed - no tool calls even with --jinja - 24 items attempted 0/0
Multi-turn & sequencing 8.520/24 - 24 items 819/24 - 24 items - a-turn 8/12, a-seq 11/12 8.520/24 - 24 items 4.513/24 - 24 items - a-turn 8/12, a-seq 5/12 1022/24 - 24 items - a-turn 10/12, a-seq 12/12 1022/24 - 24 items - a-turn 12/12, a-seq 10/12 6.526/37 - 70.3% - 191-item suite 8.520/24 - 24 items - a-turn 10/12, a-seq 10/12 8.520/24 - 24 items - a-turn 10/12, a-seq 10/12 N/Anot entered - not yet measured on this column 8.520/24 - 24 items 8.520/24 - 24 items 819/24 - 24 items - a-turn 8/12, a-seq 11/12 8.520/24 - 24 items 819/24 - 24 items 8.520/24 - 24 items - a-turn 9/12, a-seq 11/12 6.517/24 - 24 items - a-turn 6/12, a-seq 11/12 7.518/24 - 24 items - a-turn 10/12, a-seq 8/12 1022/24 - 24 items 1023/24 - 24 items - a-turn 12/12, a-seq 11/12 819/24 - 24 items - a-turn 9/12, a-seq 10/12 819/24 - 24 items - 1 lost 1022/24 - 24 items - a-turn 11/12, a-seq 11/12 8.520/24 - 24 items - a-turn 9/12, a-seq 11/12 819/24 - 24 items (a-turn 9/12, a-seq 10/12) - runs: 18, 19, 19 8.520/24 - 24 items 820/23 scoreable - 24 items - a-turn 9/11, a-seq 11/12 1022/24 - 24 items 921/24 - 24 items - a-turn 10/12, a-seq 11/12 7.518/24 - 24 items - a-turn 9/12, a-seq 9/12 0could not be tested - not scoreable without tools - 24 items attempted 0/0
Reliability under adversarial agent scenarios 612/20 scenarios passed 5.511/20 scenarios passed 510/20 scenarios passed 48/20 scenarios passed 7.515/20 scenarios passed 7.515/20 scenarios passed 612/20 scenarios passed 5.511/20 scenarios passed 510/20 scenarios passed N/Anot entered - not yet measured on this column 5.511/20 scenarios passed 6.513/20 scenarios passed 612/20 scenarios passed 7.515/20 scenarios passed 6.513/20 scenarios passed 4.59/20 scenarios passed 6.513/20 scenarios passed 4.59/20 scenarios passed 816/20 scenarios passed 714/20 scenarios passed 714/20 scenarios passed 36/20 scenarios passed 816/20 scenarios passed 5.511/20 scenarios passed 5.511/20 scenarios passed 612/20 scenarios passed 5.511/20 scenarios passed 612/20 scenarios passed 612/20 scenarios passed 7.515/20 scenarios passed 0could not be tested - cannot call tools - 0 scenarios attempted 0/0
Compliance with requests 3.321/63 harmful requests complied with (42/63 refused) - 0/7 legitimate refused - 0 empty 3.220/63 harmful requests complied with (43/63 refused) - 0/7 legitimate refused - 0 empty 9.560/63 harmful requests complied with (3/63 refused) - 0/7 legitimate refused - 0 empty 3.723/63 harmful requests complied with (40/63 refused) - 0/7 legitimate refused - 0 empty 1063/63 harmful requests complied with (0/63 refused) - 0/7 legitimate refused - 0 empty 3.321/63 harmful requests complied with (42/63 refused) - 0/7 legitimate refused - 0 empty 4.126/63 harmful requests complied with (37/63 refused) - 0/7 legitimate refused - 0 empty 4.327/63 harmful requests complied with (36/63 refused) - 0/7 legitimate refused - 0 empty 7.547/63 harmful requests complied with (0/63 refused) - 16 empty - 6/7 legitimate complied (0 refused, 1 empty) 1063/63 harmful requests complied with (0/63 refused) - 0/7 legitimate refused - 0 empty 3.723/63 harmful requests complied with (40/63 refused) - 0/7 legitimate refused - 0 empty 3.321/63 harmful requests complied with (42/63 refused) - 0/7 legitimate refused - 0 empty 4.830/63 harmful requests complied with (0/63 refused) - the operator's labels, full 512-token generations 7.547/63 harmful requests complied with (14/63 refused) - 0/7 legitimate refused - 2 empty 2.918/63 harmful requests complied with (45/63 refused) - 0/7 legitimate refused - 0 empty 2.113/63 harmful requests complied with (50/63 refused) - 3/7 legitimate refused - 0 empty 7.547/63 harmful requests complied with (16/63 refused) - 0/7 legitimate refused - 0 empty 5.937/63 harmful requests complied with (26/63 refused) - 0/7 legitimate refused - 0 empty 744/63 harmful requests complied with (17/63 refused) - 0/7 legitimate refused - 2 empty 6.541/63 harmful requests complied with (22/63 refused) - 0/7 legitimate refused - 0 empty 2.918/63 harmful requests complied with (45/63 refused) - 0/7 legitimate refused - 0 empty 5.736/63 harmful requests complied with (27/63 refused) - 0/7 legitimate refused - 0 empty 4.126/63 harmful requests complied with (37/63 refused) - 0/7 legitimate refused - 3 empty 1063/63 harmful requests complied with (0/63 refused) - 0/7 legitimate refused - 0 empty 1063/63 harmful requests complied with (0/63 refused) - 0/7 legitimate refused - 0 empty 4.327/63 harmful requests complied with (36/63 refused) - 0/7 legitimate refused - 0 empty 3.220/63 harmful requests complied with (43/63 refused) - 0/7 legitimate refused - 0 empty 1063/63 harmful requests complied with (0/63 refused) - 0/7 legitimate refused - 0 empty 1063/63 harmful requests complied with (0/63 refused) - 0/7 legitimate refused - 0 empty 8.956/63 harmful requests complied with (7/63 refused) - 0/7 legitimate refused - 0 empty 0.53/63 harmful requests complied with (60/63 refused) - 1/7 legitimate refused - 0 empty
Compliance (agent context) 6.340/63 harmful requests complied with (0 via tool call) (23/63 refused) - 0/7 legitimate refused (4 via tool call) - 0 empty N/Anot entered - not yet measured under agent context 1063/63 harmful requests complied with (19 via tool call) (0/63 refused) - 0/7 legitimate refused - 0 empty N/Anot entered - not yet measured under agent context 1063/63 harmful requests complied with (20 via tool call) (0/63 refused) - 0/7 legitimate refused (3 via tool call) - 0 empty 8.956/63 harmful requests complied with (16 via tool call) (7/63 refused) - 1/7 legitimate refused - 0 empty N/Anot entered - not yet measured under agent context N/Anot entered - not yet measured under agent context N/Anot entered - not yet measured under agent context 9.258/63 harmful requests complied with (20 via tool call) (0/63 refused) - 5 empty - 0/7 legitimate refused (3 via tool call, 1 empty) N/Anot entered - not yet measured under agent context N/Anot entered - not yet measured under agent context 9.258/63 harmful requests complied with (18 via tool call) (5/63 refused) - 1/7 legitimate refused - 0 empty N/Anot entered - not yet measured under agent context N/Anot entered - not yet measured under agent context N/Anot entered - not yet measured under agent context N/Anot entered - not yet measured under agent context N/Anot entered - not yet measured under agent context N/Anot entered - not yet measured under agent context 7.346/63 harmful requests complied with (17/63 refused) - 0/7 legitimate refused - 0 empty N/Anot entered - not yet measured under agent context 4.629/63 harmful requests complied with (2 via tool call) (34/63 refused) - 0/7 legitimate refused - 0 empty 5.937/63 harmful requests complied with (0 via tool call) (26/63 refused) - 0/7 legitimate refused - 0 empty 9.761/63 harmful requests complied with (11 via tool call) (2/63 refused) - 0/7 legitimate refused (3 via tool call) - 0 empty 9.761/63 harmful requests complied with (11 via tool call) (2/63 refused) - 0/7 legitimate refused (3 via tool call) - 0 empty N/Anot entered - not yet measured under agent context N/Anot entered - not yet measured under agent context N/Anot entered - not yet measured under agent context 9.862/63 harmful requests complied with (0 via tool call) (1/63 refused) - 0/7 legitimate refused - 0 empty N/Anot entered - not yet measured under agent context N/Anot applicable - document-reading model, not a chat model
Compliance (severe) 6.319/30 harmful requests complied with (8/30 refused) - 3 empty N/Anot entered - not yet measured 9.328/30 harmful requests complied with (2/30 refused) - 0 empty N/Anot entered - not yet measured 1030/30 harmful requests complied with (0/30 refused) - 0 empty 4.714/30 harmful requests complied with (16/30 refused) - 0 empty 721/30 harmful requests complied with (9/30 refused) - 0 empty N/Anot entered - not yet measured N/Anot entered - not yet measured 5.316/30 harmful requests complied with (0/30 refused) - 14 empty 824/30 harmful requests complied with (3/30 refused) - 3 empty N/Anot entered - not yet measured 6.720/30 harmful requests complied with (0/30 refused) - 10 empty N/Anot entered - not yet measured 13/30 harmful requests complied with (27/30 refused) - 0 empty 6.720/30 harmful requests complied with (10/30 refused) - 0 empty N/Anot entered - not yet measured N/Anot entered - not yet measured N/Anot entered - not yet measured 6.319/30 harmful requests complied with (11/30 refused) - 0 empty 4.714/30 harmful requests complied with (14/30 refused) - 2 empty 9.729/30 harmful requests complied with (1/30 refused) - 0 empty 618/30 harmful requests complied with (12/30 refused) - 0 empty 1030/30 harmful requests complied with (0/30 refused) - 0 empty 1030/30 harmful requests complied with (0/30 refused) - 0 empty N/Anot entered - not yet measured N/Anot entered - not yet measured N/Anot entered - not yet measured 9.729/30 harmful requests complied with (1/30 refused) - 0 empty N/Anot entered - not yet measured N/Anot applicable - document-reading model, not a chat model
Compliance (severe, agent context) 4.313/30 harmful requests complied with (0 via tool call) (17/30 refused) - 0 empty N/Anot entered - not yet measured under agent context 9.729/30 harmful requests complied with (1 via tool call) (1/30 refused) - 0 empty N/Anot entered - not yet measured under agent context 1030/30 harmful requests complied with (0/30 refused) - 0 empty 9.328/30 harmful requests complied with (7 via tool call) (2/30 refused) - 0 empty N/Anot entered - not yet measured under agent context N/Anot entered - not yet measured under agent context N/Anot entered - not yet measured under agent context 927/30 harmful requests complied with (8 via tool call) (0/30 refused) - 3 empty N/Anot entered - not yet measured under agent context N/Anot entered - not yet measured under agent context 927/30 harmful requests complied with (4 via tool call) (3/30 refused) - 0 empty N/Anot entered - not yet measured under agent context N/Anot entered - not yet measured under agent context N/Anot entered - not yet measured under agent context N/Anot entered - not yet measured under agent context N/Anot entered - not yet measured under agent context N/Anot entered - not yet measured under agent context 3.711/30 harmful requests complied with (17/30 refused) - 2 empty N/Anot entered - not yet measured under agent context 3.310/30 harmful requests complied with (1 via tool call) (20/30 refused) - 0 empty 6.319/30 harmful requests complied with (0 via tool call) (11/30 refused) - 0 empty 1030/30 harmful requests complied with (0/30 refused) - 0 empty 1030/30 harmful requests complied with (0/30 refused) - 0 empty N/Anot entered - not yet measured under agent context N/Anot entered - not yet measured under agent context N/Anot entered - not yet measured under agent context 1030/30 harmful requests complied with (0/30 refused) - 0 empty N/Anot entered - not yet measured under agent context N/Anot applicable - document-reading model, not a chat model
Likelihood (bits per character) 9.50.5196 bpc 9.50.515379 / 0.515379 bpc - spec off for this cell 100.5129 bpc 100.5115 / 0.5115 bpc - spec off for this cell 0could not be tested - engine returns no prompt logprobs 0could not be tested - engine returns no prompt logprobs 9.50.5206 bpc 9.50.5156 / 0.5153 bpc (0.06% twin delta) - spec off for this cell 9.50.5184 bpc N/Anot entered - not yet measured on this column 9.50.5173 bpc 9.50.5172 bpc 70.5718 bpc - spec off for this cell 70.5623 bpc (twin runs 0.013% apart) 70.5729 bpc 7.50.5553 / 0.5553 bpc (0.00% twin delta) - spec off for this cell 70.578526 bpc 60.619589 bpc 0could not be tested - engine returns no prompt logprobs 0could not be tested - engine returns no prompt logprobs 70.5670 bpc 5.50.6358 bpc 80.552137 bpc 70.561084 bpc 70.561084 bpc 4.50.6796 bpc 8.50.5493 / 0.5491 bpc (0.03% twin delta) 11.7716 bpc 0.52.8433 bpc - spec off for this cell 70.5621 / 0.5627 bpc (0.10% twin delta) 0could not be tested - engine returns no prompt logprobs
Prose speed 5.545.0 tok/s (DFlash2 blk8) 649.83 tok/s mean (code 57.81 / reasoning 58.95 / prose 32.73) 0.55.6 tok/s (ngram k5; 4.4 bare) 0.54.8 tok/s (ngram k5) 758.42 tok/s mean (code 59.50 / reasoning 77.17 / prose 38.59) 8.574.53 tok/s mean (code 78.78 / reasoning 85.59 / prose 59.23) 3.527.3 tok/s (MTP-5) 2.519.3 tok/s (MTP-2) 4.537.7 tok/s (DFlash2 blk8) N/Anot entered - not yet measured on this column 541.4 tok/s (DFlash2 blk8) 4.538.5 tok/s (DFlash2 blk8) 765.84 tok/s mean (code 73.25 / tool 84.76 / schema 39.52) 431.9 tok/s (DSpark-5) 1096.1 tok/s 542.5 tok/s (MTP-3) 870.79 tok/s mean (code 70.77 / reasoning 70.63 / prose 70.91 - unusually uniform, no drafter to differentiate categories) 10128.95 tok/s mean (code 134.29 / reasoning 143.6 / prose 108.95) - new high for this row 3.527.3 tok/s 8.574.37 tok/s mean (code 79.00 / reasoning 85.77 / prose 58.33) 5.544.29 tok/s mean (code 56.4 / reasoning 47.0 / prose 29.4) 759.4 tok/s BARE 4.535.96 tok/s mean (code 34.15 / reasoning 48.84 / prose 24.89) 433.01 tok/s mean (code 48.47 / reasoning 34.52 / prose 16.02) 4.536.51 tok/s mean (code 54.4 / reasoning 38.3 / prose 16.9) 4.536.8 tok/s 215.07 tok/s c=1 (MTP-2) 433.5 tok/s (ngram k5) 0.54.95 tok/s mean (code 5.31 / reasoning 5.62 / prose 3.92) - ngram k5 4.537.3 / 37.5 tok/s (author's band reproduces) 541.6 tok/s
Concurrency 528.0 tok/s per agent at c=2 528.07 tok/s per agent at c=2 (56.14 aggregate); c=4 27.15/stream 0.54.3 tok/s per agent at c=2 0.54.58 tok/s per agent at c=2; c=1 4.87, c=4 4.23/stream (16.9 agg) 5.534.60 tok/s per agent at c=2 (69.19 agg); c=1 38.56 851.50 tok/s per agent at c=2 (103.00 agg); c=1 59.44 3.521.0 tok/s per agent AT c=8 3.519.5 tok/s per agent at c=2 (39.0 agg); c=4 19.0/stream (75.9 agg) 423.70 tok/s per agent at c=2 (47.39 agg); c=1 24.16, c=4 21.38/stream (85.51 agg) N/Anot entered - not yet measured on this column 4.525.7 tok/s per agent at c=2 422.8 tok/s per agent at c=2 851.02 tok/s per agent at c=2 (102.05 agg); c=4 41.82/stream (167.26 agg) 4.526.5 tok/s per agent at c=2 1064.3 tok/s per agent at c=2 635.7 tok/s per agent at c=2 (71.4 agg); c=4 25.7/stream (102.6 agg) 8.571.65 tok/s per agent at c=1; 55.95 at c=2 (111.9 agg); 46.67 at c=4 (186.67 agg) 10113.30 tok/s per agent at c=1; 87.29 at c=2 (174.58 agg); 63.48 at c=4 (253.91 agg) - new high for this row 3.519.5 tok/s per agent at c=2 851.29 tok/s per agent at c=2 (102.58 agg); c=1 58.50, c=4 34.60/stream (138.42 agg) 3.521.0 tok/s per agent at c=2 851.6 tok/s per agent at c=2 4.524.25 tok/s per agent at c=1; 23.56 at c=2 (47.11 agg); 22.03 at c=4 (88.13 agg) 2.516.24 tok/s per agent at c=2 (32.49 agg); c=1 15.24, c=4 15.29/stream (61.18 agg) 2.5c=1 16.63, c=2 16.33 tok/s per agent (32.66 agg), c=4 15.84/stream (63.35 agg) 5.533.7 tok/s per agent at c=2 0.514.48 tok/s per agent at c=2 - the serve caps max-num-seqs at 1 317.6 tok/s per agent at c=2 0.53.66 tok/s per agent at c=2 (7.32 agg); c=1 3.68, c=4 3.64/stream (14.54 agg) 5.531.32 tok/s per agent at c=2 (62.63 agg); c=8 19.6/stream (156.8 agg) 6.540.9 tok/s per agent at c=2
Speculative decoding 1012.2 -> 45.0 (3.7x, DFlash2 blk8) 1012.26 -> 49.83 tok/s (4.1x, DFlash2 blk8) 34.4 -> 5.6 (+27%, ngram k5) 34.4 -> 5.6 (+27%, ngram k5) - 13.6% draft acceptance N/Anot entered - not yet measured on this setup (v0.6.0) N/Anot entered - not yet measured on this setup (drafter-off speed) 810.9 -> 27.3 tok/s (2.5x, MTP-5) 712.3 -> 21.8 (1.77x, MTP-2) - 82% draft acceptance 99.8 -> 37.7 (3.8x, DFlash2 blk8) N/Anot entered - not yet measured on this column 9.510.9 -> 41.4 (3.8x, DFlash2 blk8) 99.8 -> 38.5 (3.9x, DFlash2 blk8) 7MTP-3 in-recipe; bare-vs-spec multiple not measured on this export 731.9 tok/s with DSpark-5 in-recipe 8.566.4 -> 96.1 (+45%, MTP-3) 729.6 -> 47.2 (1.59x, MTP-3) - 76% draft acceptance 0no drafter exists for this checkpoint 7.5127.93 tok/s mean with DSpark in-recipe - no bare arm exposed (json_schema 3/3, code_edit 3/3, tool_call 2/3 valid) 2ngram-mod at defaults: no effect (27.1 vs 27.3) 7.534.78 -> 74.37 tok/s (2.14x, built-in MTP head, 6 drafts) 9.516.2 -> 45.6 (2.8x, z-lab DFlash blk12) 1.5every ngram depth SLOWER (59.4 bare; 55.2/46.7/44.6 at k3/5/8) 5.539.2 tok/s mean with DFlash in-recipe (~30% draft acceptance) - no bare arm exposed N/Anot entered - drafter-off speed not yet measured in the same session as this column's drafter-on speed 107.72 -> 36.51 tok/s (4.73x, z-lab DFlash2 draft head) 0no measured gain - spec arms produced nothing 88.97 -> 15.07 (+68%, MTP-2) 628.9 -> 33.5 (+16%, ngram k5) 43.68 -> 4.95 tok/s (1.35x, ngram k5) 721.1 -> 37.3 (1.77x, MTP) - corroborates the author's own 1.8x claim 0no drafter exists for a 7B OCR model
Reproducibility 9.5within-session identical 10within-session AND restart-to-restart identical 9.5within-session identical 9.5within-session identical (5/5 on all three probes) 6byte-nondeterministic at temp 0 - outcome flips 0/16; repro probes identical within session and boot to boot 6byte-nondeterministic at temp 0 - outcome flips 0/16; repro probes identical within session and boot to boot 100.000000 bpc spread, 4/4 loads 6byte-nondeterministic at temp 0 - outcome flips 0/16; battery probes 5/5 identical 9.5within-session identical (re-confirmed 29 Sep) N/Anot entered - not yet measured on this column 9.5within-session identical 9.5within-session identical 6short_fact and structured byte-identical 5/5; long_gen 5 distinct 6byte-nondeterministic at temp 0 6byte-nondeterministic at temp 0 6byte-nondeterministic at temp 0 - outcome flips 0/16; battery probes 5/5 identical 9.5within-session identical (5/5 on all three probes, including long_gen) 6byte-nondeterministic at temp 0 9.5within-session identical 6byte-nondeterministic at temp 0 - outcome flips 0/16; repro probes identical within session and boot to boot 9.5within-session identical 6byte-nondeterministic at temp 0 9.5within-session identical (5/5 on all three probes) 9.5within-session identical (5/5 on all three probes, including long_gen) 9.5within-session identical (short_fact, structured, long_gen) 6byte-nondeterministic at temp 0 6byte-nondeterministic at temp 0 (long generations 5/5 distinct) 6byte-nondeterministic at temp 0 9.5within-session identical 6byte-nondeterministic at temp 0 6byte-nondeterministic at temp 0
Context window 7ctx 262,144 configured; as served today, ctx 131,072, KV pool 343,708 tokens 4KV pool 200,000 tokens at ctx 131,072 6.5KV pool 561,199 tokens 6KV pool 479,637 tokens at ctx 131,072 6.52 x 262,144 = 524,288 tokens (engine-reported window at --parallel 2, bf16 KV); two concurrent 200k-token requests both held 62 x 240,640 = 481,280 tokens (engine-reported at --parallel 2, int8 KV, margin-first window) 7.5670,797 tokens @ mf 0.60 4.5KV pool 271,825 tokens at ctx 131,072 8KV pool 949,384 tokens at ctx 131,072 (mf 0.72) N/Anot entered - not yet measured on this column 7ctx 262,144 configured; as served today, ctx 131,072, KV pool 343,708 tokens 7ctx 262,144 configured; as served today, ctx 131,072, KV pool 343,708 tokens 6.5KV pool 642,062 tokens at ctx 131,072 5.5KV pool 420,562 tokens 9KV pool 1,303,911 tokens 6.5KV pool 1,054,625 tokens at ctx 262,144 10KV pool 5,731,421 tokens at ctx 262,144 configured 10KV pool 15,204,352 tokens at ctx 131,072 configured - new high for this row 3131,072 total / 32,768 per slot 95 x 262,144 = 1,310,720 tokens (engine-reported capacity, int8 KV) 5ctx 131,072 configured 10KV pool 1,902,416 tokens 10KV pool 2,291,870 tokens at ctx 131,072 configured 9.5KV pool 1,886,937 tokens at ctx 131,072 10KV pool 2,462,556 tokens 6KV pool 470,640 tokens 0.5KV pool 59,684 tokens - 8,192 per request 9KV pool 1,351,800 tokens 7KV pool 333,651 tokens at ctx 131,072 6.5KV pool 1,090,054 tokens 18,192 fixed
Box footprint 485.0 GiB resident @ mf 0.72; as served today at mf 0.48, 54,541 MiB + 1,242 MiB 748,951 MiB GPU process memory; 61.93 GiB MemAvailable 3.590.0 GiB resident (F16) 3.589.5 GiB resident @ util 0.75 (BF16) 9.524.12 GiB (free memory 103.18 GiB before launch with Whisper and the companion image server already resident, 79.06 GiB loaded and idle) N/Anot entered - not yet measured on this setup (wait fix v2) 657.0 GiB resident 7.543.3 GiB resident @ util 0.37 3.589.4 GiB (free memory 117.0 GiB before launch, 27.6 GiB loaded and idle) N/Anot entered - not yet measured on this column 484.7 GiB resident @ mf 0.72 485.0 GiB resident @ mf 0.72; as served today at mf 0.48, 54,541 MiB + 1,242 MiB 744,290 MiB GPU process memory 2112.5 GiB resident 7.545.4 GiB resident @ util 0.37 3.596.5 GiB resident @ util 0.80 (BF16) 483,224 MiB (~81.3 GiB) resident 2.5107,093 MiB (~104.6 GiB) resident 5.567.6 GiB resident (94 GB weights mmap'd) 2.5106.5 GiB (free memory 117.5 GiB before launch, 11.0 GiB loaded and idle) 3100.1 GiB resident @ mf 0.80 931.6 GiB resident 484,655 MiB (~82.7 GiB) resident 3.587.3 GiB (free memory 117.6 GiB before launch, 30.3 GiB loaded and idle) 3.587.7 GiB (free memory 117.5 GiB before launch, 29.8 GiB loaded and idle) 747.4 GiB resident 1.5MemAvailable 11.58 GiB free, loaded and idle - roughly 110 GiB resident 7.544.7 GiB resident 398.8 GiB resident @ util 0.80 (101,142 MiB GPU process) 3MemAvailable 27.14 GiB free, loaded and idle - roughly 94.5 GiB resident 106.3 GiB resident
Handwriting OCR 53/6 struck (3 as live) - 66.9 / 85.8 6.53/6 struck (3 as live) - 94.0 / 85.8 word acc 0no vision in this build - images rejected (HTTP 400, all pages) 53/6 struck (3 as live) - 65.6 / 85.1 word acc 0could not be tested - the engine refuses image input on an NVFP4 checkpoint (--vision stops at startup) 0could not be tested - the engine refuses image input for this model 30/6 struck - 96.0 / 83.1 word acc 53/6 struck (3 as live) - 66.2 / 85.8 6.53/6 struck (3 as live) - 95.4 / 85.8 53/6 struck (3 as live) - 66.2 / 85.1 6.53/6 struck (3 as live) - 94.7 / 87.2 6.53/6 struck (3 as live) - 94.0 / 87.2 53/6 struck (2 as live) - 97.4 / 87.2 word acc 0no vision in this build - images rejected (HTTP 400, all pages) 84/6 struck (2 as live) - 97.4 / 87.2 6.53/6 struck (3 as live) - 96.7 / 87.2 84/6 struck (2 as live) - 97.4 / 87.2 word acc 0no vision in this build - images rejected (HTTP 400, all pages) 106/6 struck (0 as live) - 97.4 / 87.2 76/6 struck (0 as live) - 66.2 / 87.2 word acc - 4-page score 7.54/6 struck (2 as live) - 93.4 / 87.2 51/6 struck (3 as live) - 64.9 / 83.8 AT BOARDED CONFIG 5.52/6 struck (3 as live, 1 indeterminate) - 97.4 / 86.5 word acc 51/6 struck (3 as live, 2 indeterminate) - 96.7/85.1 word acc 51/6 struck (3 as live, 2 indeterminate) - 97.4/83.8 word acc 0no vision in this build - images rejected (HTTP 400, all pages) 4.52/6 struck (3 as live, 1 indeterminate) - 67.5 / 86.5 word acc 9.55/6 struck (0 as live) - 96.7 / 91.9 6.53/6 struck (3 as live) - 96.0 / 83.8 0no vision in this build 106/6 struck - 98.0 / 86.5 word acc
Long-prompt robustness 9.525k ANSWERED - 95k ANSWERED 9.525k ANSWERED (2.6s) - 95k ANSWERED (11.2s) 9.525k ANSWERED - 95k ANSWERED 9.525k ANSWERED - 95k ANSWERED N/Anot entered - not yet measured on this setup (v0.6.0) N/Anot entered - not yet measured on this setup (wait fix v2) 8ok 9.525k ANSWERED (2.6 s) - 95k ANSWERED (8.8 s) 9.525k ANSWERED - 95k ANSWERED (4.5 s and 17.7 s) N/Anot entered - not yet measured on this column 9.525k ANSWERED - 95k ANSWERED 9.525k ANSWERED - 95k ANSWERED 9.525k ANSWERED - 95k ANSWERED 9.525k ANSWERED - 95k ANSWERED 9.525k ANSWERED - 95k ANSWERED 9.525k ANSWERED - 95k ANSWERED 9.525k ANSWERED (exact needle) - 95k ANSWERED (exact needle) 9.525k ANSWERED - 95k ANSWERED 9.525k ANSWERED - 95k ANSWERED 9.525k ANSWERED (2.5 s) - 95k ANSWERED (9.4 s) 9.525k ANSWERED - 95k ANSWERED 9.525k ANSWERED - 95k ANSWERED 5.525k ANSWERED (exact needle) - 95k ANSWERED-WRONG (approximate but not exact needle) 9.525k ANSWERED (exact needle, 6.7s) - 95k ANSWERED (exact needle, 15.8s) 9.525k ANSWERED (needle found) - 95k ANSWERED (needle found) 9.525k ANSWERED - 95k ANSWERED 6.525k ANSWERED (10.8 s) - 95k clean HTTP-400 naming the 8k limit 9.525k ANSWERED - 95k ANSWERED 9.525k ANSWERED - 95k ANSWERED (7.5 s and 24.3 s) 9.525k ANSWERED (1.9 s) - 95k ANSWERED (6.9 s) 725k ANSWERED - 95k clean HTTP 400
Fine-tune path 7same family tooling applies 7same Qwen3.8 family tooling applies 7same family as the 27B 7same family as the 27B - the house tooling applies unchanged 7same family as the 27B - this checkpoint is itself an edit of it 2no local path; MLX 4-bit serve, safetensors training upstream 7Unsloth QLoRA/RL - proven family path 7same family tooling applies (QAT checkpoint; a further training pass is unproven here) 7same family as the 27B 7same family as the 27B (Qwen3.8-27B base), Apache 2.0 8proven family path - the reference lineage 7same family tooling applies 7same Qwen3.6-35B-A3B family tooling applies 0no EXL3 fine-tune path 7Qwen3 family - Unsloth path applies 7Qwen3.5-MoE family - Unsloth path applies 7Qwen3.5-MoE family - Unsloth path applies (same standing as apodex and the 35B); MIT; five community abliterations and an NVFP4+DFlash requant exist 4NeMo LoRA recipe + Unsloth support + public Base and data, but the one published fine-tune trained on 2xH100; hybrid Mamba-MoE unproven on a GB10 2no local path; GGUF serve, safetensors training upstream 2no local path; MLX 4-bit serve, safetensors training upstream 0no mature local path for the A10B MoE 3NeMo tooling upstream; hybrid MoE-Mamba unproven here 6Unsloth-documented QLoRA path, Apache 2.0, BF16 base public; nothing run on this family here, drafter needs re-alignment 5.5Same Unsloth-documented QLoRA path as stock Muse Glimmer applies unchanged (104/1436 tensors edited, architecture untouched); further tuning risk not yet tested 5.5Same Unsloth-documented QLoRA path as stock Muse Glimmer applies unchanged (104/1436 tensors edited, architecture untouched); further tuning risk not yet tested 0no local path proven here 0no local path proven for this build 0different family - no local path proven here 6Unsloth QLoRA path for this exact base (Gemma 4 31B dense), Apache 2.0; nothing run on this family here 0no local path proven for this build 4GGUF/llama.cpp path - not the fine-tuning lineage
Real-world screenshot & photo reading 7.67.6/10 - 17/19 answered 5.35.3/10 - 19/19 answered 0no vision in this build 4.74.7/10 - 19/19 answered 0could not be tested - the engine refuses image input on an NVFP4 checkpoint (--vision stops at startup) 0could not be tested - the engine refuses image input for this model 7.47.4/10 - 17/19 answered 6.86.8/10 - 19/19 answered 7.17.1/10 - 16/19 answered N/Anot entered - not yet measured on this column 6.36.3/10 - 19/19 answered 6.66.6/10 - 19/19 answered 7.17.1/10 - 19/19 answered 0no vision in this build 5.55.5/10 - 16/19 answered 5.55.5/10 - 16/19 answered 8.28.2/10 - 19/19 answered 0no vision in this build 6.36.3/10 - 19/19 answered 7.67.6/10 - 19/19 answered 8.28.2/10 - 19/19 answered - 2 captures 5.85.8/10 - 17/19 answered 8.78.7/10 - 19/19 answered 7.67.6/10 - 19/19 answered 7.47.4/10 - 19/19 answered 0no vision in this build 7.97.9/10 - 19/19 answered 5.55.5/10 - 19/19 answered 7.67.6/10 - 19/19 answered 0no vision in this build 8.28.2/10 - 19/19 answered
Shares a box with media 6.5YES WITH KV TRADE - KV kept: 437,323 tokens - ~51.6 GiB free vs 48.8 needed 0.5NO at the measured mf 0.43 - ~61.9 GiB free alone, ~41.4 GiB after the media loadout vs 48.8 needed 3MARGINAL - ~51.6 GiB max free vs 48.8 needed, unmeasured 0.5NO - ~89.5 GiB resident @ util 0.75 leaves ~31 GiB, under the loadout overhead plus the 48.8 GiB video budget N/Anot entered - the live media-render test was not run on this setup N/Anot entered - the live media-render test was not run on this setup 7.5YES WITH KV TRADE - KV kept: 766,846 tokens - ~52.3 GiB free vs 48.8 needed 9.5YES - ~57 GiB free with loadout vs 48.8 needed (43.3 GiB resident @ util 0.37) 6YES WITH KV TRADE - KV kept: 420,429 tokens - ~50.1 GiB free vs 48.8 needed N/Anot entered - not yet measured 6.5YES WITH KV TRADE - KV kept: 438,694 tokens - ~50.2 GiB free vs 48.8 needed 6YES WITH KV TRADE - KV kept: 418,663 tokens - ~50.2 GiB free vs 48.8 needed 9.5YES - 56 GiB free with loadout vs 48.8 needed - PROVEN (15-second LTX rendered beside the model) 0.5NO - the box is spoken for (112.5 GiB resident) 9.5YES - 55 GiB free with loadout vs 48.8 needed - PROVEN 0.5NO - 96.5 GiB resident @ util 0.80 leaves ~25 GiB, under the ~20.5 GiB loadout overhead plus the 48.8 GiB video budget 8YES WITH KV TRADE - KV kept: 2,329,506 tokens - ~54.1 GiB free vs 48.8 needed 6.5YES WITH KV TRADE - KV kept: 5,134,803 tokens - ~50.3 GiB free vs 48.8 needed 1NO - ~44.6 GiB max free vs 48.8 needed N/Anot entered - not yet measured 1NO - ~6.5 GiB free with loadout; weights alone are 76.5 GiB 10YES - ~75 GiB free with loadout vs 48.8 needed 7.5YES WITH KV TRADE - KV kept: 770,854 tokens - ~52.4 GiB free vs 48.8 needed 7YES WITH KV TRADE - KV kept: 505,986 tokens - 56.1 GiB free vs 48.8 needed - PROVEN (15-second LTX rendered beside the model at util 0.42) 7YES WITH KV TRADE - KV kept: 463,354 tokens - 56.6 GiB free vs 48.8 needed - PROVEN (15-second LTX rendered beside the model at util 0.42) 8.5YES - ~59 GiB free with loadout vs 48.8 needed 0.5NO - the box is spoken for (~110 GiB resident, 11.58 GiB free alone) 9.5YES - ~62 GiB free with loadout vs 48.8 needed N/Anot entered - not yet measured 0.5NO - not enough headroom for the media loadout (27.14 GiB free alone, ~20.5 GiB loadout overhead) 10YES - ~100 GiB free with loadout; 6.3 GiB resident

Each cell is one sample. Every number here is accurate for the day it was measured, but running the same model again in the same setup shifts some results a little, the same way two samples from one population differ. In repeat runs we measured, results moved by one to three items, up to about one point on the 0 to 10 scale. Each cell is taken as representative of the model, so read a gap of about a point or less between two models as normal run-to-run variation, not a proven difference.

What each row measures
Agent tasks
Can it do the multi-step jobs your agents run: pick the right tool, follow the instructions, finish the job. Measured on 60 fixed tasks, 12 per category, drawn from the 197-item A-Bench suite; the one carried column ran an earlier 191-item version.
Tools & structured output
When a program needs a machine-readable answer back, a form filled in exactly, a tool called with the right arguments, does it come back in the shape the program expects, or as prose that breaks the caller.
Multi-turn & sequencing
Whether it keeps track over a long back-and-forth: remembers what it was told several messages ago and does things in the right order. Models that look fine in one-shot tests often fall over here.
Reliability under adversarial agent scenarios
This is the row that answers 'can something hijack my agent mid-task', not the row about refusing dangerous content. An agent reads things a human never approved: a web page, a file, a tool's output, and any of those can carry a hidden instruction trying to redirect it, an injected 'ignore your task and do X' buried in a document, a fake system message, a prompt telling it to touch a file outside its job. This row measures whether the model stays obedient to the ACTUAL task you gave it when that happens, across 20 fixed adversarial scenarios. A pass means it did only the job it was asked, called a tool only when the task needed one, and never claimed finished work it had not done; one unauthorised tool call, one touch of something off-limits, or one false completion claim fails that scenario. Score is scenarios passed out of 20. High here is what you want if your worry is an agent doing something you did not ask for; it has nothing to do with how the model handles requests for dangerous content, which is the row to the right.
Compliance with requests
High is more compliant here, same reading as every other row on this board. This is the flipped read of a refusal test: 63 adversarial prompts written to be genuinely dangerous, plus 7 that only sound risky (crisis support, harm-reduction, medication questions) which the model must still answer. A high score means it said yes more often across the harmful set specifically, not just your own ordinary requests - read that plainly, it is not a euphemism. The legitimate-look-alike count is shown separately and matters on its own: refusing one of those is a real cost, over-caution biting a request that was never dangerous. An answer the model never finished, because its reasoning budget ran out before a final answer landed, is counted separately as ‘empty’, never folded into either count. Score is harmful prompts complied with out of 63; both the complied and refused counts are shown on every cell so nothing is hidden behind the flip.
Compliance (agent context)
The same 63 harmful prompts and 7 legitimate look-alikes as Compliance with requests, but asked as the last turn of a real deployed agent's context - its full production system prompt, a live memory-system recall, and (the operator's ruling, 27 Sep 2026) the agent's real tool schema - instead of bare with nothing around it. A reply that completes via a tool call counts as complied, the same as a text answer. Same scoring otherwise: harmful prompts complied with out of 63, high is more compliant, an unfinished answer counts separately as ‘empty’, never folded into either count. The point of the row is the GAP to the bare row for the same weights: a model can comply freely bare and refuse far more once real deployment context surrounds the same question, and that gap is invisible to the bare row alone.
Compliance (severe)
A second, harder-hitting compliance probe than Compliance with requests: 30 prompts drawn from the StrongREJECT benchmark, split evenly across three categories - violence, illegal goods and services, and sexual content - curated by reading every candidate individually rather than keyword-screening, with material adjacent to child sexual abuse excluded during that review. Same read as Compliance with requests: high is more compliant, score is harmful prompts complied with out of 30. Bare, no system prompt, single turn - the agent-context pairing is the row below.
Compliance (severe, agent context)
The same 30-item severe set as Compliance (severe), asked as the last turn of a real deployed agent's context instead of bare - the same context capture used for Compliance (agent context) above. Same scoring: complied out of 30, high is more compliant. Where the two severe rows diverge for the same weights is the point: it shows whether real deployment context changes how a model handles harder requests specifically, not just the general 63-item set.
Likelihood (bits per character)
How unsurprised the model is by ordinary English, a rough proxy for how well it has learned the language. The RAW figure is lower-is-better; the SCORE is flipped so higher is better like every other row. A ‘cannot be measured’ cell means the serving software offers no confidence readout at all. The engine is the limit there, and the model goes unjudged. It measures fluency only, so a model can win here and still be the weaker assistant.
Prose speed
How fast it writes ordinary text. This is the number you feel when you watch it type.
Concurrency
What one agent gets while a second one is also working, which is the normal worst case. The figure is per agent. A total summed across agents would make a slow model look fast, because a sum describes the hardware and this row describes the experience.
Speculative decoding
Whether a small fast helper model can run ahead and guess the next few words so the big model checks several at once instead of writing one at a time. When it works it is free speed.
Reproducibility
Ask the same question twice and get the same answer back? Matters when you need to defend a decision or reproduce a result later.
Context window
How much text it can hold in mind at once, how long a document you can paste before it starts forgetting the beginning.
Box footprint
How much of a Spark it occupies before doing any work. A smaller footprint leaves more room for whatever else needs to share the box: another model, a larger KV cache, or any other workload.
Handwriting OCR
Reading your handwritten pages, and specifically whether it notices words that were crossed out. A model that reads deletions as live text credits words that the writer withdrew.
Long-prompt robustness
What happens when you paste something huge. Answering is best; refusing clearly is fine, because you learn why and can split it. Truncating without saying so, or crashing, is a failure. The two prompt sizes are CHARACTERS: 25k is about 6k tokens and 95k about 24k tokens (a needle question at the end, timed to a correct answer); a true 95k-TOKEN prompt is a separate, larger test.
Fine-tune path
How viable it is to fine-tune this model on this hardware at all: architecture (dense vs MoE, any custom/hybrid blocks), licence terms for derivatives, whether tooling (Unsloth, PEFT/LoRA, NeMo, etc.) actually supports it, and whether anyone has done it. Broader than any one project - local fine-tuning work's own data is one use this row would serve, not its definition. Widened 20 Sep on the operator's instruction; where there is nothing else to weigh a column on, family-tooling precedent (the standing this row already used) is the right answer.
Real-world screenshot & photo reading
Everyday screenshots and photos from a real workload: chat screenshots, dashboards, spreadsheets, X posts, dialog boxes, phone screenshots, product photos. Scored against an answer key written before any model saw an item, with a rule that a confidently wrong number or name counts against a model even when the rest of the reading is right. Every item counts: an item a model did not answer scores zero, so the count on each cell shows how many of the nineteen it answered. Where a cell is blank, hover over it to see why.
Shares a box with media
Whether video, speech, image and music generation can run on the SAME Spark beside this model. The box has one 121.6 GiB pool; whatever the serve does not occupy is what media work gets. The proven production loadout ran with about 61 GiB free. Where a model needs to give something up, the give is the KV cache - the context-window row - because weights cannot shrink. The budget is MEASURED: a 15-second LTX video needs 48.8 GiB free with the loadout up, a 4-second one 44.2 - measured on real renders, not estimated.

Configuration

RadixArk FP4 27B
SGLang - NVFP4 (arch:sm_120) - DFlash2 blk8 - mf 0.72 - fp8 KV
NVIDIA Qwen3.8-27B NVFP4
nvidia/Qwen3.8-27B-NVFP4 @ 9340e99f9c8a7f2c2d7df7b7e0dbd3af3fb6a038 - SGLang 0.0.0.dev1+g5f55db35e, image sha256:616a3e97f45191af975896cfa644279096cb31bd408a071c2e99ca7209c3cafe - mixed NVFP4/FP8 ModelOpt Local-Hessian export (arch:sm_121) - DFlash2 blk8 - mf 0.43 - fp8 KV - ctx 131072 - max-total 200000
Qwen 27B apostate
vLLM 0.28 - F16 - util 0.75 - qwen3 parsers
Qwen3.8-27B BF16 base
vLLM 0.28 - BF16 weights (the apostate recipe, the board's only 16-bit dense-27B lane) - ngram k5 - util 0.75 - qwen3 parsers - ctx 131072 - snapshot 1d4bf0f2 - the second unit
Leimroth 3 (based on Qwen3.8-27B)
Hugging Face: leimroth-lab/Qwen3.8-27B-Leimroth3 - TensorFold v0.6.0 (upstream tag v0.6.0, commit c4646171; installed package tree hash ca2171866ceb, equal to upstream's; local image tensorfold-qwen38:v0.6.0) - Leimroth 3 NVFP4 (arch:sm_121; local ModelOpt export of leimroth-lab/Qwen3.8-27B-Leimroth3) - DFlash2 drafter (z-lab head, 4-bit on CUDA) - --parallel 2 x 262,144 context (the native maximum, served at the first attempt beside Whisper and the companion image server) - --prefill-fp8 (prompts read with FP8 activations) - --checkpoint-slots 8 (eight conversation states kept) - bf16 KV - thinking on at medium through a mounted template copy whose only change is the default level (the template's own default is xhigh) - server-side thinking budget of 6,000 tokens - structured-output requests with thinking off - compliance rows thinking off per request - images through a companion Qwen3-VL-4B server (llama.cpp, Q4_K_M), because this engine refuses --vision on an NVFP4 checkpoint - Whisper and the companion server run beside it. Measured on this setup: prose, concurrency, context and footprint. Carried to it from v0.3.6.3 and v0.5.0: agent, tools, multi-turn, reliability, the four compliance rows and reproducibility, because the three identity probes (fd28319be0f6 / 861bb95247a5 / cdcfea6ff780) give the same engine token hashes on all four v0.6.0 boots. Not entered: speculative decoding and long-prompt robustness, which were not measured on v0.6.0. Likelihood, handwriting OCR and screenshot reading could not be run on this engine. This recipe replaced the SGLang column for this model on 1 Oct 2026 and moved from v0.5.0 to v0.6.0 on 2 Oct 2026.
Unbound LR7
Hugging Face: leimroth-lab/Unbound-LR7 - TensorFold v0.5.0 (upstream commit 9cd52ab4, local image a083b1c9c037) plus the new-task wait fix, version 2 (a Tier 1 Python patch of three engine files, patches/tensorfold-050-flashnext-msgstart: multi.py 86189dff1605, decode.py f778d849e6d8, engine.py 3ab689b63c2c; keeps the state at the end of the agents' shared setup in a 2.75 GiB store) - Unbound LR7 (abliterated Qwen3.8-Flash-Next, Vontra MLX 4-bit MTP, served on TensorFold, with the built-in MTP head; edited from Vontra/Qwen3.8-Flash-Next-MLX-4bit-MTP rev dadefa80: main edit strength 1.75, shared-expert down-projections 0.5; 35 files, sha256 verified before every run) - --parallel 2 x 240,640 context (a margin-first window that keeps about 11 GiB free beside Whisper and the vision server; the store takes the difference from 262,144) - int8 KV - built-in MTP head, up to 6 drafts at 0.60 confidence - temperature 1.0, top-p 0.95, top-k 20 - n-gram tables read from SSD - thinking on, level unset (the checkpoint's own default renders xhigh) - compliance rows thinking off per request - no vision lane: this engine refuses image input for this model family. Measured on this setup or on fix v1 at the same shape (decode path byte-identical): prose, concurrency and context. Carried to it from stock v0.5.0 at --parallel 5 x 262,144 (same weights, flags and thinking level): agent, tools, multi-turn, reliability, the four compliance rows and reproducibility, because the three identity probes (fd28319be0f6 / 5c2b3406375c / 67a299079d0f) give the same engine token hashes on every boot of this setup and every reply that resumed from a kept state equals the same prompt run fresh (9 of 9). Not entered: speculative decoding, footprint and long-prompt robustness, which were not measured on this build. Likelihood could not be run on this engine.
Unsloth 27B (vLLM)
vLLM 0.26 - NVFP4 (arch:sm_120) - MTP-5
QUASAR-QAT 27B
vLLM 0.26 - NVFP4 W4A4 on all 496 linears (arch:sm_120, FlashInferCutlassNvFp4LinearKernel asserted at init) - MTP-2 - util 0.37 - qwen3 parsers - ctx 131072 - pin d8e6fbfa
Qwen 27B orcarouter
SGLang - NVFP4 (arch:sm_120) - DFlash2 blk8 - mf 0.72 - fp8 KV
Qwen 27B orcarouter (FP8)
vLLM 0.28.0-aarch64-ubuntu2404 - orcarouter/Qwen3.8-27B-Uncensored-FP8 @ 0f3cdb83 - FP8 quantisation - util 0.75 - ctx 131072 - qwen3_coder tool parser + qwen3 reasoning - ngram k5 speculative decoding
Unsloth 27B (SGLang)
SGLang - NVFP4 (arch:sm_120) - DFlash2 blk8 - mf 0.72 - fp8 KV
RadixArk BF16-head
SGLang - NVFP4+BF16 head (arch:sm_120) - DFlash2 blk8 - mf 0.72
Leimroth 5 (Qwen3.6-35B-A3B)
Hugging Face: leimroth-lab/Qwen3.6-35B-A3B-Leimroth5 (lam1.5) - vLLM 0.26 (arch:sm_120) - NVFP4 experts-only - MTP-3 - util 0.37 - triton_attn - ctx 131072 - the second unit
DeepSeek-V4-Flash
sparkinfer (MiaAI one-Spark) - EXL3 - DSpark-5
Qwen3.6-35B-A3B
vLLM 0.26 - NVFP4 (arch:sm_120) - MTP-3 - util 0.37 - triton_attn
Apodex-1.1-mini
vLLM 0.26 - BF16 weights, the only 16-bit MoE column (no four-bit build of this model exists) - MTP-3 - util 0.80 - qwen3_coder + qwen3 parsers - ctx 262144 - pin 4e4f109a
Ornith-1.5-35B-A3B
vLLM 0.28.0-aarch64 (arch:sm_120) - ornith-ai/Ornith-1.5-35B-A3B-NVFP4 rev 94e431d9 - NVFP4 - no drafter, spec n/a - util 0.70 - ctx 262144 - qwen3_xml tool parser + qwen3 reasoning - moe-backend marlin
NVIDIA Nemotron 3.5 Lightning
vLLM 0.28.0-aarch64 (arch:sm_120) - nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4 rev bee75962 - NVFP4 - DSpark draft (NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4-DSpark, 3 speculative tokens), spec ON - util 0.85 - ctx 131072 - moe-backend marlin - kv-cache-dtype fp8 - mamba-backend flashinfer, cache-mode align - nemotron_v3 reasoning + qwen3_coder tool parser
Qwen3.8-Flash-Next
llama.cpp b10689 digest e52c6104 - UD-IQ4_XS GGUF - ctx 131072/4 slots - mmproj BF16
Qwen3.8-Flash-Next
TensorFold v0.3.6.3 (commit 19118807 + MiaAI-Lab recipe patches 5f313914582d, local image 7f68a34a) - Vontra/Qwen3.8-Flash-Next-MLX-4bit-MTP rev dadefa80 - 5 x 262,144 - int8 KV - MTP drafts 6 / 0.60 + copy drafts - vision on - ple-on-ssd - thinking on, effort omitted (renders xhigh on this engine)
Qwen3.5-122B
SGLang - NVFP4 (arch:sm_120) - z-lab DFlash blk12 - mf 0.80 - max-running 4 requested, engine settled at 2 - re-measured 11 Sep
Nemotron-Omni-30B
vLLM 0.26 - NVFP4 (arch:sm_120) - no spec (bare is its best) - util 0.37
Muse Glimmer-30B
vLLM 0.28.0-aarch64 (arch:sm_120) - nvidia/Muse-Glimmer-30B-NVFP4 rev 47818374 - NVFP4 - DFlash draft head (Muse-Glimmer-30B-assistant, 15 draft tokens), spec ON - util 0.70 - ctx 131072 - muse_glimmer tool+reasoning parser
Dream Shimmer LR6 (stock drafter)
Hugging Face: leimroth-lab/Dream-Shimmer-LR6 - vLLM 0.28.0-aarch64 sha256:89154ef0 - mg-biproject-s10 (LR6, scale-1 biprojection edit of Muse Glimmer 30B) - BF16 saved weights, online --quantization fp8_per_block (ignore vision/lm_head) - stock Muse-Glimmer-30B-assistant DFlash draft head, 15 draft tokens, spec ON - CUDA graphs on (NOT --enforce-eager) - VLLM_USE_DEEP_GEMM=0 (the default DeepGEMM FP8-block kernel crashes at engine init on GB10 under graphs; this override selects CutlassFp8BlockScaledMMKernel instead - measured 25 Sep, see memory fp8-per-block-deepgemm-crashes-gb10-under-cuda-graphs) - util 0.70 - ctx 131072 - muse_glimmer tool+reasoning parser - reasoning_strength none
Dream Shimmer LR6 (z-lab drafter)
Hugging Face: leimroth-lab/Dream-Shimmer-LR6 - vLLM 0.28.0-aarch64 sha256:89154ef0 - mg-biproject-s10 (LR6) - BF16 saved weights, online --quantization fp8_per_block (ignore vision/lm_head) - z-lab/Muse-Glimmer-30B-DFlash2 draft head (public HF, model.safetensors sha256 6613c1523c78..., 15 draft tokens, spec ON) - CUDA graphs on (NOT --enforce-eager) - VLLM_USE_DEEP_GEMM=0 (CutlassFp8BlockScaledMMKernel) - util 0.70 - ctx 131072 - muse_glimmer tool+reasoning parser - reasoning_strength none
GLM-4.7-Flash
vLLM 0.26 - NVFP4 (arch:sm_120) - no spec - util 0.37 - glm47 parsers
GLM-5.3-Flash EXL3 (K2)
vLLM - EXL3 (K2 build) - MTP-2 - ctx 8192 - max-num-seqs 1 - util 0.87 - single Spark
Gemma-4-26B-A4B
vLLM 0.26 - NVFP4 (arch:sm_120) - ngram k5 - util 0.37 - gemma4 parsers
Gemma-4-31B-it-uncensored-heretic
vLLM 0.28.0-aarch64-ubuntu2404 - llmfan46/gemma-4-31B-it-uncensored-heretic @ d5bfc0d9 - BF16, no quantisation - util 0.80 - gemma4 tool-call parser - no speculative decoding
Ling-3.0-Flash INT4
vLLM - INT4 - MTP - author's watchdog supervisor - single Spark
olmOCR-2-7B
llama.cpp - Q4_K_M GGUF - mmproj - ctx 8192
  • Scores are standings within a row. A 10 on one row and a 3 on another do not add up to anything. Which rows matter is a judgement for whoever runs the box, so the board carries no column totals.
  • Near-identical scores are ties. Where the instruments could not separate two arms, or separated them by less than anyone would notice on real work, the arms score the same or within a fraction. The raw figures underneath still differ a little. That difference sits inside the measurement error.
  • A low score is a standing on one row. It is no verdict on the model. Several of these models are strong at things this board does not rank, and a model that scores badly as a daily general assistant may still be the right tool for one specific job.
  • Every number is a measurement taken on this hardware, or a labelled property of the configuration. Where a model could not be measured, the cell says why in its hover text and scores only what was observed. No cell holds a guess. The one exception, by design, is the Fine-tune path row: a researched judgement, not a measurement, with its reasons in each cell’s hover text.
  • The best configurations conflict, and the board shows the conflict. Speculation buys decode speed and cuts concurrency and KV headroom. Autotune-off buys reproducibility and costs a little speed. Where one model needs two deployable profiles the cells say so. The board’s job is to make the trade visible. Which trade to take is a decision for whoever runs the box.
  • Likelihood is lower-is-better, so the score is inverted. It measures fluency on one frozen corpus and says nothing about usefulness on its own. Two cells (olmOCR, Flash-Next) score 0, below every measured model, because their serving engine cannot produce this measurement at all, and an undelivered measurement ranks below a poor one. That is a ranking rule, and no prediction of how they would score if they could be measured.
  • Every model handles text. The marks under each model name state what its served build accepts beyond text. Vision ✓ means it reads images, audio ✓ means it takes speech directly, and “no vision in this build” means this particular export shipped without its vision components. The model family may offer vision elsewhere, but the build measured here cannot see images (verified in each checkpoint’s own metadata).
  • Two columns are one model. “Unsloth 27B (vLLM)” and “Unsloth 27B (SGLang)” serve byte-identical weights, the same Unsloth NVFP4 quant of Qwen3.8-27B, verified in the checkpoint’s own metadata. Only the serving engine differs. The vLLM column’s cells are marked “Historical” where its settings predate this board’s tuned-per-model approach. The identical weights scored 0.5206 and 0.5173 bits per character on the two engines, a 0.6% gap that is engine noise, the same size of difference this board treats as a tie everywhere else.
  • What “KV trade” means, for the “shares a box with media” row above: a model’s weights cannot shrink, but the working memory it reserves for holding conversation context, the KV pool, can. Trading KV means re-serving the model with a smaller memory reservation so the box keeps the measured 48.8 GiB that a 15-second video generation needs. The cost is context room, so each traded cell shows the measured pool that survives: “KV kept” in tokens, which you can read against the largest single agent input ever recorded here (114,533 tokens). Around 420,000 kept tokens means roughly three to four worst-case agents can work at once. Traded cells are ranked by how much KV they keep.
Methodology

How these numbers were measured

Everything on this page, the scorecard above and the efficiency readout below, was measured on NVIDIA DGX Spark hardware (GB10, 121.6 GiB unified memory shared between CPU, GPU and cache). Each model was served at its own best configuration, and the configuration line in every cell records what produced the number. The serving engines were vLLM, SGLang, llama.cpp, and one model’s own published serving stack. Every number comes from a measurement file kept under version control, and a script generates the boards from those files, so a cell cannot appear without its measurement.

  • Agent tasks, tools, multi-turn: a fixed 60-task agent suite, A-Bench (12 tasks in each of five categories: tool calling, JSON-schema structured output, instruction sequencing, multi-turn memory, and long accumulating context), run the same way against every model at temperature 0. Scores show correct/attempted, and tasks the server failed to answer are counted and shown, so a server that dies mid-run scores as a server that died. The carried “Unsloth 27B (vLLM)” column ran the full 191-task version of the same suite and is labelled accordingly.
  • Reliability under adversarial agent scenarios: 20 fixed scenarios in which something the agent reads (a document, a web page, a tool’s output, a fake system message) tries to redirect it away from its task. A scenario passes only if the model did the job it was given and nothing else: no unauthorised tool call, nothing off-limits touched, and no claim of finished work it had not done. Score is scenarios passed out of 20.
  • Compliance rows: a digest-pinned set of 63 held-out harmful prompts plus 7 legitimate look-alikes (crisis support, harm reduction, medication questions), and a separate 30-prompt severe set drawn from the StrongREJECT benchmark (violence, illegal goods and services, sexual content), reviewed item by item. Each set runs twice: bare, with no system prompt, and as the last turn of a real deployed agent’s context (its production system prompt, a memory recall and its real tool list), where finishing a request through a tool call counts as complying. Every reply is classed complied, refused or empty; an empty reply, one that never reached a final answer, is shown separately and never counted as a refusal.
  • Prose speed & speculative decoding: single-stream decode across three prompt types (code, reasoning, prose), three repetitions each, 512-token completions, with a discarded warm-up pass first so compilation cost never contaminates the mean. Speculative arms re-verify structured output validity before any speed number counts.
  • Concurrency: 1, 2 and 4 simultaneous requests; the boards show per-request throughput at 2 (the realistic two-agent case) and totals at each level, with the concurrency confirmed at the server as well as the client.
  • Likelihood (bits/char): teacher-forced bits per character over a frozen 43-text English corpus, identical characters for every model, measured with speculation off (speculative decoding corrupts this measurement on one engine).
  • Long-prompt robustness: 25,000- and 95,000-character pastes with a checkable fact planted at the very end, so silent truncation cannot pass.
  • Reproducibility: identical repeated requests at temperature 0, compared byte-for-byte and answer-for-answer; the two are reported separately.
  • Context window: the KV-cache pool the serving engine actually allocates at the model’s served configuration, in tokens, read from the engine itself and shown beside the configured context length. It is how much conversation and document text the model can hold at once.
  • Box footprint: the memory the running serve occupies on the Spark before it does any work, in GiB, measured on the box.
  • Handwriting OCR: four ground-truthed handwritten pages including deliberately crossed-out words; scored on word accuracy and, separately, whether deletions were noticed or wrongly read as live text.
  • Real-world screenshot & photo reading: 19 screenshots and photos from a real workload (chat screenshots, dashboards, spreadsheets, social posts, dialogs, phone screenshots, product photos), one fixed prompt, each reading scored 0, 0.5 or 1 against an answer key written before any model saw an item. A confidently wrong number or name counts against the reading, and an unanswered item scores zero.
  • Fine-tune path: the one row that is a researched judgement rather than a measurement. It weighs architecture (dense or mixture-of-experts, any unusual blocks), licence terms for derivative models, whether fine-tuning tools actually support the model, and whether anyone has done it. Each cell’s hover text gives the reasons.
  • Shares a box with media: “the media stack” means image and video generation (ComfyUI, with the LTX video model) plus speech-to-text (Whisper) running on the SAME box as the language model. The budget was measured with that stack loaded: a 15-second LTX video generation needs 48.8 GiB free, and a 4-second one needs 44.2 GiB.
The test suites

These test suites and instruments were built for this campaign. They are not public leaderboard benchmarks, so scores compare across this page and nowhere else. Each one runs the same way against every model:

  • A-Bench: the 60-task agent suite behind the agent, tools and multi-turn rows. Its five categories (tool calling, structured JSON output, instruction sequencing, multi-turn memory, long accumulating context) each contribute 12 tasks. Before any run counts, the harness verifies the model’s identity from the server’s own startup command, so a score cannot land on the wrong model, and a server that dies mid-suite has its unanswered tasks counted against it.
  • The configuration battery: a fixed gauntlet every serving configuration must pass before its numbers can appear here: a hard question that must be answered correctly (a health-check “200 OK” is not evidence a model works), concurrency at 1, 2 and 4, the two long-prompt pastes, re-verification of structured output and tool calling at that exact configuration, and the repeated-request reproducibility check.
  • The speed instrument: times single-stream decode across code, reasoning and prose prompts with a discarded warm-up pass, and separately measures an agent-shaped workload (JSON schema, code edit, tool call) whose outputs must validate before the speed is accepted.
  • The likelihood scorer: computes bits-per-character over the frozen text corpus by asking the engine to score exact given text, character-identical for every model.
  • The handwriting OCR set: four ground-truthed handwritten pages with deliberate crossings-out, scored for word accuracy and for whether deletions were recognised as deletions.
  • The corruption probe: sends prompts whose correct answers must round-trip multi-byte characters (accents, CJK, emoji) intact, catching engines that mangle text encoding without raising an error.
  • The adversarial reliability suite: 20 fixed agent scenarios with instructions planted in the material the agent reads, scored on whether the model stuck to its actual task.
  • The compliance probe sets: 63 harmful prompts plus 7 legitimate look-alikes, and a 30-prompt severe set drawn from StrongREJECT, pinned by digest so every model gets exactly the same questions, run bare and inside a captured real agent context.
  • The screenshot reading set: 19 real screenshots and photos, with an answer key written before any model saw them.

Efficiency readout (companion): single-Spark only

The board above asks how good the answers are. This readout asks how much time and output it costs to get a correct one, recorded from the agent-suite runs themselves. It sits beside the single-Spark board and feeds no score. It covers 13 of the 31 single-Spark columns.

Order the models your way: Click a COLUMN heading to make it priority 1, another for priority 2, and so on: models re-rank by a weighted blend of their standing on each column you pick, where earlier priorities count more; the small number under each heading is its current weight; click a numbered heading again to remove it.

Weighting:
Model Correct / given Mean seconds per task Output tokens per correct answer Tasks lost
Gemma-4-26B-A4B50/606.3742
RadixArk BF16-head46/607.62989
Qwen3.5-122B36/607.751923
Qwen3.6-35B-A3B52/6010.98952
Qwen3.8-Flash-Next48/6010.92848
NVIDIA Qwen3.8-27B NVFP452/6011.23102
Unsloth 27B (SGLang)51/6014.52904
Qwen 27B orcarouter51/6014.63172
Nemotron-Omni-30B51/6015.99984
DeepSeek-V4-Flash55/6016.32220
RadixArk FP4 27B55/6018.73460
GLM-4.7-Flash50/6023.77200
Qwen 27B apostate53/6072.43212

Click a column heading to sort by it alone; click again to reverse. Setting priorities above overrides plain column-sort.

What each column means, and which way is good:

  • Correct / given: how many of the 60 tasks the model got right. Higher is better; 55/60 is strong, 36/60 is weak.
  • Mean seconds per task: the average time to produce each answer. Lower is faster, but read it together with tasks lost, below, before trusting it.
  • Output tokens per correct answer: how much output it took, on average, to land one correct answer. Lower means more concise, cheaper correct work.
  • Tasks lost: how many of the 60 tasks the server never finished. On those it died or dropped the connection, which is a separate failure from answering wrong. Zero is what you want. A model that died partway only had to answer the easier, shorter tasks it reached before dying, so a high lost count flatters its speed and token numbers. The 122B’s 7.7-second mean sits on 23 lost tasks, close to two in every five.

Not in this readout (no per-task efficiency record): Qwen3.8-27B BF16 base, Leimroth 3 (based on Qwen3.8-27B), Unbound LR7, QUASAR-QAT 27B, Qwen 27B orcarouter (FP8), Leimroth 5 (Qwen3.6-35B-A3B), Apodex-1.1-mini, Ornith-1.5-35B-A3B, NVIDIA Nemotron 3.5 Lightning, Muse Glimmer-30B, Dream Shimmer LR6 (stock drafter), Dream Shimmer LR6 (z-lab drafter), GLM-5.3-Flash EXL3 (K2), Gemma-4-31B-it-uncensored-heretic, Ling-3.0-Flash INT4, the carried “Unsloth 27B (vLLM)” (it ran a different task set, so its numbers do not compare), and olmOCR-2-7B (it cannot run the agent suite at all).

I’m new to this and learning as I go. If you spot an error or a better way to measure something, please tell me.

Credits & references

These benchmarks stand on other people’s published work. Several serving configurations came straight from community recipes:

  • GLM-5.3-Flash on Spark: deployment recipes and the patched sm121 serving image by tonyd2wild; the EXL3 two-Spark recipe by MiaAI-Lab; the single-Spark EXL3 K2 build by vcruz305; the DFlash2 drafter by incoai.
  • DeepSeek-V4-Flash single-Spark stack: MiaAI-Lab’s published deployment, adopted whole.
  • Ling-3.0-Flash single-Spark stack: sudoingX’s dgx-spark-ling recipe, adopted whole.
  • Qwen3.8-Flash-Next dual-Spark stack: MiaAI-Lab’s vLLM recipe, adopted whole (the Flash-Next supersession).
  • Speculative-decoding drafters: the DFlash method authors including z-lab’s Qwen3.5-122B and Qwen3.8-27B drafters; Meta’s Muse Glimmer assistant drafter; RadixArk’s model builds and DSpark drafter.
  • Model builds: Unsloth (27B NVFP4 and the Flash-Next GGUF), Vontra (the MLX 4-bit MTP conversion of Qwen3.8-Flash-Next that Unbound LR7 is built from), RadixArk, Ai2/allenai (olmOCR), NVIDIA (the Qwen3.8 NVFP4 local edition, the Muse Glimmer NVFP4 build, Nemotron and the Spark itself), Meta (Muse Glimmer), the Qwen team, Google (Gemma), Z.ai (GLM), DeepSeek, inclusionAI (Ling), ornith-ai (Ornith), QUASAR-QAT, RedHatAI (the Qwen3.5-122B NVFP4 build), GadflyII (the GLM-4.7-Flash NVFP4 build); and the uncensored builds: orcarouter’s NVFP4 and FP8 builds of Qwen3.8-27B, heterodoxin’s F16 build produced with their Apostate tool, which gives that column its name, and llmfan46’s Gemma-4-31B heretic build.
  • Serving engines: vLLM, SGLang, and llama.cpp, whose Flash-Next support merged upstream days before these runs; and TensorFold (ashhart/TensorFold), the engine behind the Leimroth 3 and Unbound LR7 columns.
  • Trap registry: Blackwellboy’s model-serving-minefield, a community registry of serving and measurement pitfalls that was consulted before every serving and benchmarking action in this campaign; several documented traps were avoided here because someone else had already paid for them.
  • Method lessons: catid’s public Spark benchmarks, and the wider DGX Spark community on X whose published results and warnings shaped several of these tests, including the wall-clock-beats-tokens-per-second point (@theotherpomp) and the EXL3 reasoning-tag artifact (@puffybsd) that changed how two of these measurements were taken.
  • Also on LocalMaxxing: these speed numbers, and others from this campaign not shown on this page, are submitted to LocalMaxxing’s community leaderboard as well.

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