Qwen3 VRAM requirements
Memory needed to run Qwen3 at every published size and quant, with an 8k context. Each figure is weights plus KV cache plus runtime buffers, from the same memory model as the VRAM calculator.
Total memory by size and quant
GB at an 8k context. Bold is the Q4_K_M column, the usual starting point.
| Size | Q8_0 | Q6_K | Q5_K_M | Q4_K_M | Q3_K_M |
|---|---|---|---|---|---|
| 8B | 11 | 9.5 | 8.5 | 7.5 | 6.5 |
| 30B · 3B active | 34 | 28 | 25 | 21 | 17 |
| 235B · 22B active | 239 | 195 | 165 | 136 | 107 |
KV cache by context length
GB the context alone adds on top of the weights. Add this to a weights figure to size a longer session.
| Size | Weights at Q4_K_M | 4k ctx | 16k ctx | 32k ctx | 128k ctx |
|---|---|---|---|---|---|
| 8B | 4.5 | 0.6 | 2.4 | 4.8 | 19 |
| 30B · 3B active | 17 | 1.1 | 4.3 | 8.6 | 34 |
| 235B · 22B active | 132 | 1.1 | 4.3 | 8.6 | 34 |
What each size fits on at Q4_K_M
Hardware whose memory holds the model with headroom, smallest pool first. Fits means under 85% of the pool at an 8k context.
- 8Bneeds about 7.5 GB
Smallest: NVIDIA P102-100 (10 GB), RTX 2080 Ti (11 GB), D700 12GB (12 GB), Intel Arc B580 12GB (12 GB), and 93 more.
- 30B · 3B activeneeds about 21 GB
Smallest: AMD MI50 32GB (32 GB), M1 Max 32GB (32 GB), M1 Pro 32GB (32 GB), M2 Max 32GB (32 GB), and 53 more.
- 235B · 22B activeneeds about 136 GB
Smallest: Instinct MI300X 192GB (192 GB), M2 Ultra 192GB (192 GB), M3 Ultra 192GB (192 GB), AMD Threadripper 256GB (256 GB), and 4 more.
Frequently asked
How much VRAM does Qwen3 need?
From about 7.5 GB for the 8B model to about 136 GB for the 235B · 22B active model, at Q4_K_M with an 8k context and including the KV cache and runtime buffers. Q8 needs more and Q3 less; the table above lists every size and rung.
Can I run Qwen3 on a 16GB GPU?
Yes. The 8B model needs about 7.5 GB at Q4_K_M with an 8k context, which fits a 16 GB card with headroom. Larger sizes need a smaller quant or a bigger card.
Can I run Qwen3 on a 24GB GPU such as an RTX 3090 or 4090?
Yes. The 8B model needs about 7.5 GB at Q4_K_M with an 8k context, which fits a 24 GB card with headroom. Larger sizes need a smaller quant or more memory.
How are these numbers calculated?
Weights at the quant's bits per weight, plus a KV cache sized from the model's attention design for the chosen context, plus compute buffers and a fixed runtime allowance. It is the same memory model the VRAM calculator uses. A family whose attention profile is not on file is sized as plain grouped-query attention, which errs towards needing more memory.