Can V100 16GB PCIe run DeepSeek-R1-0528-Qwen3-8B-layer-mix-bpw-3.8-mlx?

NVIDIA · 16 GB HBM2 · 900 GB/s bandwidth · GreenBitAI · 2.3B · model ctx up to NoneK

Fit & speed by quant and context

Fits = weights + KV(ctx) + 1 GB overhead ≤ 95% of VRAM. Measured anchors are context-agnostic; the fit verdict is recomputed per context. A missing tok/s means the model is far beyond this card (offload-only territory).

VRAM breakdown at 8K context

Other GPUs that run DeepSeek-R1-0528-Qwen3-8B-layer-mix-bpw-3.8-mlx

• B200 192GB SXM — ≈2213.4 tok/s Q4 est.
• MI300X 192GB — ≈1466.4 tok/s Q4 est.
• H200 141GB SXM5 — ≈1328.1 tok/s Q4 est.
• H100 80GB SXM5 — ≈926.9 tok/s Q4 est.
• Gaudi 3 128GB — ≈906.4 tok/s Q4 est.
• A100 80GB SXM4 — ≈564.2 tok/s Q4 est.
• RTX 5090 32GB — ≈495.8 tok/s Q4 est.
• RTX 4090 24GB — ≈278.9 tok/s Q4 est.

All GPUs for DeepSeek-R1-0528-Qwen3-8B-layer-mix-bpw-3.8-mlx →

Other models for the V100 16GB PCIe

• MiniCPM5-1B — ≈530.3 tok/s Q4 est.
• Qwen3.5-2B — ≈286.4 tok/s Q4 est.
• Llama-3.2-3B-Instruct — ≈179.0 tok/s Q4 est.
• gpt-oss-20b — ≈159.1 tok/s Q4 est.
• gemma-4-26B-A4B-it — ≈150.7 tok/s Q4 est.
• Qwen3-4B-Instruct-2507 — ≈143.2 tok/s Q4 est.
• NVIDIA-Nemotron-3-Nano-4B-BF16 — ≈143.2 tok/s Q4 est.
• Qwen3-4B-Thinking-2507 — ≈143.2 tok/s Q4 est.

All models on the V100 16GB PCIe →