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Run Qwen2.5 32B on RTX 4090

32.5B parameters on a 24 GB card. It fits at Q4_K_M — here's the VRAM math at every quantization, the speed to expect, and the quality you trade.

verdict · Qwen2.5 32B on RTX 4090
Fits at Q4_K_M
needs 21.8 GB at Q4_K_M · 22.8 GB usable · ~47 tokens/sec
VRAM needed and fit for Qwen2.5 32B on RTX 4090 by quantization.
quantization vram needed fits 24gb? tokens/sec quality
FP16 (full) 78.0 GB ✗ no — Reference quality — no quantization loss.
Q8_0 41.3 GB ✗ no — Near-lossless; rarely worth the extra space over Q6.
Q6_K 32.0 GB ✗ no — Virtually indistinguishable from full precision.
Q5_K_M 26.9 GB ✗ no — Minor loss; an excellent quality-vs-size balance.
Q4_K_Mrecommended 21.8 GB ✓ yes ~47 Small but measurable loss; the popular default.
Q3_K_M 16.8 GB ✓ yes ~61 Noticeable degradation; only when you're tight on VRAM.

Weights = params × bytes/weight, +20% for KV cache & runtime; usable VRAM is 95% of nameplate. Tokens/sec is a bandwidth ceiling (1008 GB/s) — real throughput is lower with long context. Try other combinations →

Running Qwen2.5 32B on a RTX 4090

At Q4_K_M, Qwen2.5 32B's weights take about 22 GB, inside the RTX 4090's 23 GB of usable memory. Small but measurable loss; the popular default. Generation speed is bound by memory bandwidth — the GPU reads the whole model once per token — so expect on the order of 47 tokens/sec before context overhead.

Quantization is the lever

Each step down in precision shrinks the model: FP16 needs about 78 GB, Q4_K_M about 22 GB — a 72% reduction for a small, usually acceptable quality cost. Q5_K_M and Q6_K are near-lossless if you have the headroom; drop to Q3 only when you're genuinely out of VRAM. The quantization guide covers the tradeoffs in detail.

Frequently asked questions

Can a RTX 4090 run Qwen2.5 32B?

Yes — Qwen2.5 32B fits on a RTX 4090 at Q4_K_M (22 GB of the 23 GB usable), with roughly 47 tokens/sec. Higher-precision quants need more VRAM; the table above shows each option.

How much VRAM does Qwen2.5 32B need?

Qwen2.5 32B is a 32.5B-parameter model. At FP16 that's about 78 GB; at Q4_K_M (the popular default) about 22 GB, including ~20% for the KV cache and runtime. Quantization is the main lever — see the per-quant table above.

Other combinations

Qwen2.5 32B on other GPUs: RTX 3060 12GB, RTX 4070 Ti, RTX 4080, RTX 3090, NVIDIA L4, RTX A6000

Other models on the RTX 4090: Gemma 2 9B, Phi-3 Medium 14B, Gemma 2 27B, Command R 35B, Mixtral 8x7B, Llama 3.3 70B

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