Written by Jakub Rusinowski · Last updated September 6, 2026
Yes
Yes — Qwen3.8 27B at Q6_K needs about 25.3 GB of the 32 GB usable on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM) (~6.7 GB spare), at ~50.4 tok/s (estimated), with room for about 32,768 tokens of context.
Confidence: medium · Recommended quantization: Q6_K · Estimated speed: ~50.4 tok/s
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| Usable memory for models | 32 GB |
| Memory bandwidth | 1792 GB/s |
| Form factor | Desktop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
| Quant | Memory needed | Fits 32 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 58.1 GB | ✗ No | — | — | 55.6 GB |
| Q8_0 | 32.1 GB | ✗ No | — | — | 29.5 GB |
| Q6_K | 25.3 GB | ✓ Yes | 32K | ~50.4 tok/s | 22.8 GB |
| Q5_K_M | 22.2 GB | ✓ Yes | 32K | ~57 tok/s | 19.7 GB |
| Q4_K_M | 19.3 GB | ✓ Yes | 64K | ~65 tok/s | 16.8 GB |
| Q3_K_M | 14.4 GB | ✓ Yes | 64K | ~85.4 tok/s | 11.8 GB |
| Q2_K | 11.7 GB | ✓ Yes | 64K | ~103.2 tok/s | 9.1 GB |
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| Qwen3.8-Max | 1457.5 GB | ✗ Too large | — |
| Qwen3.8 27B | 19.3 GB | ✓ Fits | ~65 tok/s |
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
Yes — Qwen3.8 27B at Q6_K needs about 25.3 GB of the 32 GB usable on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM) (~6.7 GB spare), at ~50.4 tok/s (estimated), with room for about 32,768 tokens of context.
Q6_K — it needs about 25.3 GB of the 32 GB available, downloads as roughly 22.8 GB, and runs at an estimated 50.4 tokens/sec with up to 32K of context.
Nothing binding — the model fits with headroom and generates at a usable speed on this hardware.
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving