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// RUN — FITS
Can the NVIDIA B200 run YaLM?
Yes — here's how fast and up to what context, with real measured numbers.
YaLM on the NVIDIA B200
| Quant | Fits? | Max context | Speed | Beyond context |
|---|---|---|---|---|
| FP16 | no | — | — | — |
| Q8 | yes | 229k | 58.4-69.6 t/s | ~0.3-0.3 (slow) |
| Q4 | yes | 364k | 104.3-124.3 t/s | ~0.5-0.6 (slow) |
Estimate (memory-bound). Beyond "max context" the KV cache spills to system RAM offload (depends on your system RAM) and speed drops to the "beyond" figure.
FAQ
Can the NVIDIA B200 run YaLM?▶
Yes. At Q4 it generates ~104.3-124.3 tok/s and fits up to 364k context; beyond that it spills to system RAM offload (depends on your system RAM) and slows to ~0.5-0.6 tok/s.
How much context fits?▶
At Q4, up to about 364k tokens stay in fast memory; longer context spills to system RAM offload (depends on your system RAM) and throughput collapses.