Compatibility check
Can you run Qwen2.5 32B Instruct on the RTX 4080?
Not on a single RTX 4080. Even at 4-bit, Qwen2.5 32B Instruct needs about 19.9 GB, which exceeds the card's 16 GB. You would need roughly 2× RTX 4080 with tensor parallelism, a larger GPU, or a smaller model.
Memory breakdown
Weights plus a 1 GB KV cache at 4,096tokens, against the card's 16 GB. Verdicts leave ~10% headroom for activations and fragmentation.
| Precision | Weights | KV cache | Total | % of 16 GB | Fit |
|---|---|---|---|---|---|
| FP16 / BF16full quality | 75.4 GB | 1 GB | 76.4 GB | 478% | No |
| INT8 (8-bit)near-full quality | 37.7 GB | 1 GB | 38.7 GB | 242% | No |
| INT4 (4-bit)GPTQ / AWQ / GGUF Q4 | 18.9 GB | 1 GB | 19.9 GB | 124% | 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 32B Instruct
Cards where this model fits (at its best precision):
Models that fit the RTX 4080
Other popular models that run on this card:
Go deeper
Frequently asked questions
Can the RTX 4080 run Qwen2.5 32B Instruct?
No. Even 4-bit needs about 19.9 GB, more than the 16 GB available.
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 4080?
Not on a single RTX 4080 — even 4-bit exceeds 16 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 GB here; doubling the context roughly doubles that term, so long-context use can push a tight fit over the edge.