Can 2x Tesla V100 32GB run Qwen3.8 27B?

Q: Can 2x Tesla V100 32GB run Qwen3.8 27B?

Yes. Measured 104 tok/s generation on 2x Tesla V100 32GB, via bnk dense engine, tensor parallel, MTP drafting, Q8_0. Source: https://github.com/christ-pher/bnk

Yes. Measured 104 tok/s generation (bnk dense engine, tensor parallel, MTP drafting, Q8_0; source).

Every measured result for Qwen3.8 27B on 2x Tesla V100 32GB

Build	Quant / setup	Engine	tok/s
Dual Tesla V100 32 GB on EPYC 7402 (bnk engine)	Q8_0	bnk dense engine, tensor parallel, MTP drafting	104	source
Dual Tesla V100 32 GB on EPYC 7402 (bnk engine)	Q8_0	bnk dense engine, tensor parallel, no drafting	42.4	source

Memory: 2x Tesla V100 32GB has 64 GB. Qwen3.8 27B weights are about 16.5 GB at Q4_K_M (27B params × 4.89 bits ÷ 8, before context/KV cache). Arithmetic, not a measurement; long contexts need more. Check other quants in the VRAM calculator.

Speeds are decode (generation) tokens/sec as reported by each source; quant, engine and context differ, so small gaps are noise.

All measured speeds for Qwen3.8 27B: best home rig for Qwen3.8 27B.

Qwen3.8 27B on other hardware: 2x Apple M5 Ultra · 2x Intel Arc Pro B70 · 2x RTX 5070 Ti · 4x Apple M5 Ultra · Apple M3 Ultra · Apple M5 Ultra · DGX Spark (GB10) · Intel Arc Pro B70 · RTX 3060 12GB · RTX 3090 · RTX 4060 Ti 16GB · RTX 5070 Ti · RTX 5090 · RTX Pro 6000 Max-Q (Blackwell, 96 GB, 300 W) · RTX Spark N1X · Radeon AI Pro R9700 · Strix Halo (Radeon 8060S)

Other models measured on 2x Tesla V100 32GB: DeepSeek V4.1 Flash · Qwen3.8 Flash-Next

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