llmgauge

run it locally

Run Qwen2.5 32B on RTX 4080

32.5B parameters on a 16 GB card. It won't fit in this card's memory at any usable quantization — here's the math, and what will run it.

verdict · Qwen2.5 32B on RTX 4080
Won't fit
even Q3_K_M needs 16.8 GB · only 15.2 GB usable
too big for this card — run it in the cloud
Smallest GPU that fits at Q4_K_M: RTX 3090
Rent RTX 3090 by the hour
VRAM needed and fit for Qwen2.5 32B on RTX 4080 by quantization.
quantization vram needed fits 16gb? 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_M 21.8 GB ✗ no — Small but measurable loss; the popular default.
Q3_K_M 16.8 GB ✗ no — 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 (717 GB/s) — real throughput is lower with long context. Try other combinations →

Why Qwen2.5 32B won't fit a RTX 4080

Qwen2.5 32B has 32.5 billion parameters. Even quantized hard to Q3_K_M, its weights plus KV-cache overhead come to roughly 17 GB, well past the RTX 4080's 15 GB of usable VRAM. Spilling the overflow to system RAM (CPU offload) works but can cut throughput tenfold. To run it at the popular Q4_K_M default you'd want a RTX 3090 or larger — or rent one by the hour rather than buy.

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 4080 run Qwen2.5 32B?

Not in VRAM. Even at the smallest practical quantization (Q3_K_M), Qwen2.5 32B needs about 17 GB versus the RTX 4080's 15 GB usable. You'd need a larger GPU — a RTX 3090 fits it at Q4_K_M, or to rent one by the hour.

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 3090, RTX 4090, NVIDIA L4, RTX A6000

Other models on the RTX 4080: Llama 3.1 8B, Gemma 2 9B, Mistral 7B, Qwen2.5 7B, Phi-3 Medium 14B, Gemma 2 27B

Related