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PixelML Cookbook

Reproducible serving recipes and customer-reference benchmarks, one folder per accelerator. Every number is labelled measured (our receipt), community-reported, or inferred.

Matrix (decode throughput, tok/s)

model GPU engine / image c=1 c=16 c=128 $/M output tokens receipt
Qwen3.8-Flash-Next NVFP4 (nvidia) 2x B200 TP2 vLLM nightly 2026-09-06 pending 1,501 5,256 0.66 b200/qwen3.8-flash-next
GLM-5.3-Flash NVFP4 (LibertAIDAI @11d73216) 2x B200 TP2 vLLM nightly 2026-09-06, Marlin MoE pending pending ~2,300 (thinking on, inferred) ~1.5 b200/glm-5.3-flash
DeepSeek-V4.1-Flash (bf16/fp8, 475.3 GiB) 4x B200 TP4+EP4 SGLang @c4ca6511, DSPARK spec 331.0 1,724.4 not reachable 4.03 (at c=16) b200/deepseek-v4.1-flash

Pricing basis: Modal list price $6.25/B200-hour. See docs/methodology.md.

Notes on the DeepSeek-V4.1-Flash row (all measured): the c=16 figure comes from the knee sweep at admission cap 256, not from the two boots at cap 32, which disagree by 1.46x at that level — see the recipe. c=16 is this model's throughput peak, not a mid-sweep point — aggregate falls to 667 tok/s at c=32, so $4.01/M is a floor rather than a trend. c=128 is not reachable on this node: --max-running-requests 512 will not boot because the sliding-window pool wants 31.56 GB against 18.88 GB free. A 262,144-token prompt kills the server on a model advertising a 1M context, while 120,177 tokens serve at 51,527 tok/s.

Layout

b200/  h100/  a100/  a10g/  b300/  vera-rubin/
  <model>/  notebook.ipynb  recipe.md  receipts/
docs/methodology.md

Related: club-170hx, club-dgx-spark (owned-hardware communities).

About

PixelML cookbook: reproducible LLM serving recipes and customer-reference benchmarks per cloud accelerator (B200, H100, A100, A10G, B300, Vera Rubin) — model x GPU x engine matrix with receipts

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