Written by Jakub Rusinowski · Last updated February 4, 2025
Yes — comfortably
Yes, comfortably — Phi-4 Mini (3.8B) at Q8_0 needs about 5.9 GB of the 24 GB usable on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM), leaving ~18.1 GB spare and running at ~120.5 tok/s (estimated), with room for about 65,536 tokens of context.
Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~120.5 tok/s
| Usable memory for models | 24 GB |
| Memory bandwidth | 1008 GB/s |
| Form factor | Desktop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
| Quant | Memory needed | Fits 24 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 9.5 GB | ✓ Yes | 64K | ~76.9 tok/s | 7.6 GB |
| Q8_0 | 5.9 GB | ✓ Yes | 64K | ~120.5 tok/s | 4 GB |
| Q6_K | 5 GB | ✓ Yes | 64K | ~141.3 tok/s | 3.1 GB |
| Q5_K_M | 4.6 GB | ✓ Yes | 64K | ~153.4 tok/s | 2.7 GB |
| Q4_K_M | 4.2 GB | ✓ Yes | 64K | ~166.9 tok/s | 2.3 GB |
| Q3_K_M | 3.5 GB | ✓ Yes | 64K | ~196.1 tok/s | 1.6 GB |
| Q2_K | 3.1 GB | ✓ Yes | 64K | ~217 tok/s | 1.2 GB |
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
Yes, comfortably — Phi-4 Mini (3.8B) at Q8_0 needs about 5.9 GB of the 24 GB usable on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM), leaving ~18.1 GB spare and running at ~120.5 tok/s (estimated), with room for about 65,536 tokens of context.
Q8_0 — it needs about 5.9 GB of the 24 GB available, downloads as roughly 4 GB, and runs at an estimated 120.5 tokens/sec with up to 64K 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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