Written by Jakub Rusinowski · Last updated September 6, 2026
Yes — comfortably
Yes, comfortably — SmolLM3 3B at Q8_0 needs about 4.9 GB of the 32 GB usable on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM), leaving ~27.1 GB spare and running at ~200.3 tok/s (estimated), with room for about 131,072 tokens of context.
Confidence: medium · Recommended quantization: Q8_0 · Estimated speed: ~200.3 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 | 7.8 GB | ✓ Yes | 128K | ~140 tok/s | 6.2 GB |
| Q8_0 | 4.9 GB | ✓ Yes | 128K | ~200.3 tok/s | 3.3 GB |
| Q6_K | 4.2 GB | ✓ Yes | 128K | ~225.4 tok/s | 2.5 GB |
| Q5_K_M | 3.8 GB | ✓ Yes | 128K | ~239.1 tok/s | 2.2 GB |
| Q4_K_M | 3.5 GB | ✓ Yes | 128K | ~253.7 tok/s | 1.9 GB |
| Q3_K_M | 3 GB | ✓ Yes | 128K | ~282.9 tok/s | 1.3 GB |
| Q2_K | 2.7 GB | ✓ Yes | 128K | ~302 tok/s | 1 GB |
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
Yes, comfortably — SmolLM3 3B at Q8_0 needs about 4.9 GB of the 32 GB usable on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM), leaving ~27.1 GB spare and running at ~200.3 tok/s (estimated), with room for about 131,072 tokens of context.
Q8_0 — it needs about 4.9 GB of the 32 GB available, downloads as roughly 3.3 GB, and runs at an estimated 200.3 tokens/sec with up to 128K 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