Can I Run Qwen 3.7 on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)?
Written by Jakub Rusinowski · Last updated June 26, 2026
Yes, but it is tight
Yes, but it is tight — Qwen 3.7 35B-A3B at Q4_K_M needs about 23.8 GB of the 24 GB usable on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM), leaving only ~0.2 GB before the runtime starts swapping. Expect ~169.9 tok/s (estimated), with room for about 8,192 tokens of context.
Confidence: low · Recommended quantization: Q4_K_M · Estimated speed: ~169.9 tok/s
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RTX 4090 Desktop (24 GB VRAM, 64 GB RAM) — what it gives a model
| Usable memory for models | 24 GB |
| Memory bandwidth | 1008 GB/s |
| Form factor | Desktop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
Qwen 3.7 on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM): memory by quantization
| Quant | Memory needed | Fits 24 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 72.7 GB | ✗ No | — | — | 70 GB |
| Q8_0 | 39.9 GB | ✗ No | — | — | 37.2 GB |
| Q6_K | 31.4 GB | ✗ No | — | — | 28.7 GB |
| Q5_K_M | 27.5 GB | ✗ No | — | — | 24.8 GB |
| Q4_K_M | 23.8 GB | ✓ Yes | 8K | ~169.9 tok/s | 21.1 GB |
| Q3_K_M | 17.6 GB | ✓ Yes | 32K | ~193 tok/s | 14.9 GB |
| Q2_K | 14.2 GB | ✓ Yes | 32K | ~208.6 tok/s | 11.5 GB |
What to watch out for
- Only ~0.2 GB of headroom at Q4_K_M: a longer context or a second application can push this into swapping.
- This model does not publish its full attention configuration, so the KV-cache share of these figures is inferred from its parameter count rather than computed exactly.
RTX 4090 desktop limitations
- 24 GB is the sweet spot for 27–32B models at Q4; 70B needs offload or a second card.
Recommended setup
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
How these numbers are calculated
- Memory need = quantized weights + KV cache at 8,192 tokens (f16) + 0.8 GB runtime overhead.
- 24 GB of VRAM on the NVIDIA GeForce RTX 4090 at 1008 GB/s.
- 64 GB of system RAM available for CPU offload when a model exceeds VRAM.
- Throughput is a memory-bandwidth roofline estimate, not a measurement. It is labelled "estimated" everywhere it appears.
- KV cache is inferred from the parameter count because this model does not publish its full attention configuration.
FAQ
Can I run Qwen 3.7 on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)?
Yes, but it is tight — Qwen 3.7 35B-A3B at Q4_K_M needs about 23.8 GB of the 24 GB usable on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM), leaving only ~0.2 GB before the runtime starts swapping. Expect ~169.9 tok/s (estimated), with room for about 8,192 tokens of context.
Which quantization of Qwen 3.7 should I use on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)?
Q4_K_M — it needs about 23.8 GB of the 24 GB available, downloads as roughly 21.1 GB, and runs at an estimated 169.9 tokens/sec with up to 8K of context.
What limits Qwen 3.7 on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)?
Nothing binding — the model fits with headroom and generates at a usable speed on this hardware.
Which runtime should I use?
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
Other Computers
Other Models on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)
- Qwen3.8 on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)
- Qwen3-Coder on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)
- SmolLM2 on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)
- SmolLM3 on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)
- StarCoder 2 on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM)
Qwen 3.7 on GPUs
- Qwen 3.7 on NVIDIA GeForce RTX 5090
- Qwen 3.7 on NVIDIA GeForce RTX 5080
- Qwen 3.7 on NVIDIA GeForce RTX 5070 Ti
- Qwen 3.7 on NVIDIA GeForce RTX 5070