Compatibility check
Can you run Mistral Nemo Instruct on the Tesla V100 32GB?
Yes. Mistral Nemo Instruct fits on the Tesla V100 32GB (32 GB) in full FP16/BF16 precision, using about 28.8 GB including a 0.6 GB KV cache at 4,096 tokens. You have comfortable headroom for longer prompts and modest batching.
Memory breakdown
Weights plus a 0.6 GB KV cache at 4,096tokens, against the card's 32 GB. Verdicts leave ~10% headroom for activations and fragmentation.
| Precision | Weights | KV cache | Total | % of 32 GB | Fit |
|---|---|---|---|---|---|
| FP16 / BF16full quality | 28.2 GB | 0.6 GB | 28.8 GB | 90% | Fits |
| INT8 (8-bit)near-full quality | 14.1 GB | 0.6 GB | 14.7 GB | 46% | Fits |
| INT4 (4-bit)GPTQ / AWQ / GGUF Q4 | 7 GB | 0.6 GB | 7.6 GB | 24% | Fits |
Planning estimates, not a substitute for profiling. Real usage varies with the inference runtime, batch size, and how much context you actually use — the KV cache grows linearly with prompt length.
GPUs that run Mistral Nemo Instruct
Cards where this model fits (at its best precision):
Models that fit the Tesla V100 32GB
Other popular models that run on this card:
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Frequently asked questions
Can the Tesla V100 32GB run Mistral Nemo Instruct?
Yes. In FP16 it uses about 28.8 GB, which fits the Tesla V100 32GB's 32 GB.
How much VRAM does Mistral Nemo Instruct need?
Approximately 28.2 GB in FP16, 14.1 GB in INT8, and 7 GB in 4-bit for the weights, plus a KV cache of about 0.6 GB at 4,096 tokens.
Does quantization let Mistral Nemo Instruct fit on the Tesla V100 32GB?
Yes. Dropping to FP16 / BF16 brings total usage to about 28.8 GB, which fits the 32 GB card with headroom for the KV cache.
What happens to memory with longer context?
The KV cache grows linearly with prompt length. At 4,096 tokens it is about 0.6 GB here; doubling the context roughly doubles that term, so long-context use can push a tight fit over the edge.