Compatibility check
Can you run Qwen2.5 32B Instruct on the RTX 3090?
Yes, with quantization. Qwen2.5 32B Instruct does not fit the RTX 3090 (24 GB) in FP16, but it runs at INT4 (4-bit) using about 19.9 GB (83% of VRAM). Use a GPTQ, AWQ, or GGUF build to get there, and keep prompts moderate to leave room for the KV cache.
Memory breakdown
Weights plus a 1 GB KV cache at 4,096tokens, against the card's 24 GB. Verdicts leave ~10% headroom for activations and fragmentation.
| Precision | Weights | KV cache | Total | % of 24 GB | Fit |
|---|---|---|---|---|---|
| FP16 / BF16full quality | 75.4 GB | 1 GB | 76.4 GB | 318% | No |
| INT8 (8-bit)near-full quality | 37.7 GB | 1 GB | 38.7 GB | 161% | No |
| INT4 (4-bit)GPTQ / AWQ / GGUF Q4 | 18.9 GB | 1 GB | 19.9 GB | 83% | 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 Qwen2.5 32B Instruct
Cards where this model fits (at its best precision):
Models that fit the RTX 3090
Other popular models that run on this card:
Go deeper
Frequently asked questions
Can the RTX 3090 run Qwen2.5 32B Instruct?
Not in FP16, but yes at INT4 (4-bit), where it uses about 19.9 GB versus the card's 24 GB.
How much VRAM does Qwen2.5 32B Instruct need?
Approximately 75.4 GB in FP16, 37.7 GB in INT8, and 18.9 GB in 4-bit for the weights, plus a KV cache of about 1 GB at 4,096 tokens.
Does quantization let Qwen2.5 32B Instruct fit on the RTX 3090?
Yes. Dropping to INT4 (4-bit) brings total usage to about 19.9 GB, which fits the 24 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 1 GB here; doubling the context roughly doubles that term, so long-context use can push a tight fit over the edge.