Can I Run Qwen 3.7 on RTX 3090 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 3090 Desktop (24 GB VRAM, 64 GB RAM), leaving only ~0.2 GB before the runtime starts swapping. Expect ~162.2 tok/s (estimated), with room for about 8,192 tokens of context.
Confidence: low · Recommended quantization: Q4_K_M · Estimated speed: ~162.2 tok/s
RTX 3090 Desktop (24 GB VRAM, 64 GB RAM) — what it gives a model
| Usable memory for models | 24 GB |
| Memory bandwidth | 936 GB/s |
| Form factor | Desktop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
Qwen 3.7 on RTX 3090 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 | ~162.2 tok/s | 21.1 GB |
| Q3_K_M | 17.6 GB | ✓ Yes | 32K | ~185 tok/s | 14.9 GB |
| Q2_K | 14.2 GB | ✓ Yes | 32K | ~200.4 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 3090 desktop limitations
- The cheapest route to 24 GB of VRAM, and the standard used-market recommendation for local LLMs.
- Older architecture: no FP8 acceleration, and higher idle power than a current 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 3090 at 936 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 3090 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 3090 Desktop (24 GB VRAM, 64 GB RAM), leaving only ~0.2 GB before the runtime starts swapping. Expect ~162.2 tok/s (estimated), with room for about 8,192 tokens of context.
Which quantization of Qwen 3.7 should I use on RTX 3090 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 162.2 tokens/sec with up to 8K of context.
What limits Qwen 3.7 on RTX 3090 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 3090 Desktop (24 GB VRAM, 64 GB RAM)
Qwen 3.7 on GPUs
What This Model Is Good At
Model & Tools
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