run it locally
Run Qwen2.5 7B on RTX 4080
7.6B parameters on a 16 GB card. It fits at Q8_0 — here's the VRAM math at every quantization, the speed to expect, and the quality you trade.
| quantization | vram needed | fits 16gb? | tokens/sec | quality |
|---|---|---|---|---|
| FP16 (full) | 18.2 GB | ✗ no | — | Reference quality — no quantization loss. |
| Q8_0recommended | 9.7 GB | ✓ yes | ~76 | Near-lossless; rarely worth the extra space over Q6. |
| Q6_K | 7.5 GB | ✓ yes | ~98 | Virtually indistinguishable from full precision. |
| Q5_K_M | 6.3 GB | ✓ yes | ~116 | Minor loss; an excellent quality-vs-size balance. |
| Q4_K_M | 5.1 GB | ✓ yes | ~143 | Small but measurable loss; the popular default. |
| Q3_K_M | 3.9 GB | ✓ yes | ~186 | 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 →
Running Qwen2.5 7B on a RTX 4080
At Q8_0, Qwen2.5 7B's weights take about 10 GB, inside the RTX 4080's 15 GB of usable memory. Near-lossless; rarely worth the extra space over Q6. Generation speed is bound by memory bandwidth — the GPU reads the whole model once per token — so expect on the order of 76 tokens/sec before context overhead.
Quantization is the lever
Each step down in precision shrinks the model: FP16 needs about 18 GB, Q4_K_M about 5 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 7B?
Yes — Qwen2.5 7B fits on a RTX 4080 at Q8_0 (10 GB of the 15 GB usable), with roughly 76 tokens/sec. Higher-precision quants need more VRAM; the table above shows each option.
How much VRAM does Qwen2.5 7B need?
Qwen2.5 7B is a 7.6B-parameter model. At FP16 that's about 18 GB; at Q4_K_M (the popular default) about 5 GB, including ~20% for the KV cache and runtime. Quantization is the main lever — see the per-quant table above.
Other combinations
Qwen2.5 7B on other GPUs: RTX 3060 12GB, RTX 4070 Ti
Other models on the RTX 4080: Llama 3.1 8B, Gemma 2 9B, Mistral 7B, Phi-3 Medium 14B, Gemma 2 27B, Qwen2.5 32B
Related
- VRAM calculator — any model, quant and GPU.
- Running models locally — the hardware reality.
- All run-locally combinations →