Compatibility check
Can you run Qwen2.5 72B Instruct on the Tesla V100 32GB?
Not on a single Tesla V100 32GB. Even at 4-bit, Qwen2.5 72B Instruct needs about 43 GB, which exceeds the card's 32 GB. You would need roughly 2× Tesla V100 32GB with tensor parallelism, a larger GPU, or a smaller model.
Memory breakdown
Weights plus a 1.3 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 | 167.2 GB | 1.3 GB | 168.4 GB | 526% | No |
| INT8 (8-bit)near-full quality | 83.6 GB | 1.3 GB | 84.8 GB | 265% | No |
| INT4 (4-bit)GPTQ / AWQ / GGUF Q4 | 41.8 GB | 1.3 GB | 43 GB | 134% | No |
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 Qwen2.5 72B 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:
Go deeper
Frequently asked questions
Can the Tesla V100 32GB run Qwen2.5 72B Instruct?
No. Even 4-bit needs about 43 GB, more than the 32 GB available.
How much VRAM does Qwen2.5 72B Instruct need?
Approximately 167.2 GB in FP16, 83.6 GB in INT8, and 41.8 GB in 4-bit for the weights, plus a KV cache of about 1.3 GB at 4,096 tokens.
Does quantization let Qwen2.5 72B Instruct fit on the Tesla V100 32GB?
Not on a single Tesla V100 32GB — even 4-bit exceeds 32 GB. You would need multiple GPUs or a larger card.
What happens to memory with longer context?
The KV cache grows linearly with prompt length. At 4,096 tokens it is about 1.3 GB here; doubling the context roughly doubles that term, so long-context use can push a tight fit over the edge.