Written by Jakub Rusinowski · Last updated August 20, 2024
Yes — comfortably
Yes, comfortably — Phi 3.5 Mini at Q8_0 needs about 5.6 GB of the 32 GB usable on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM), leaving ~26.4 GB spare and running at ~180.2 tok/s (estimated), with room for about 131,072 tokens of context.
Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~180.2 tok/s
| 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 | 9.2 GB | ✓ Yes | 128K | ~121.9 tok/s | 7.6 GB |
| Q8_0 | 5.6 GB | ✓ Yes | 128K | ~180.2 tok/s | 4 GB |
| Q6_K | 4.7 GB | ✓ Yes | 128K | ~205.6 tok/s | 3.1 GB |
| Q5_K_M | 4.3 GB | ✓ Yes | 128K | ~219.8 tok/s | 2.7 GB |
| Q4_K_M | 3.9 GB | ✓ Yes | 128K | ~235.1 tok/s | 2.3 GB |
| Q3_K_M | 3.2 GB | ✓ Yes | 128K | ~266.6 tok/s | 1.6 GB |
| Q2_K | 2.9 GB | ✓ Yes | 128K | ~287.8 tok/s | 1.2 GB |
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
Yes, comfortably — Phi 3.5 Mini at Q8_0 needs about 5.6 GB of the 32 GB usable on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM), leaving ~26.4 GB spare and running at ~180.2 tok/s (estimated), with room for about 131,072 tokens of context.
Q8_0 — it needs about 5.6 GB of the 32 GB available, downloads as roughly 4 GB, and runs at an estimated 180.2 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
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