Written by Jakub Rusinowski · Last updated January 6, 2025
Yes
Yes — Phi-4 (14B) at Q6_K needs about 14 GB of the 16 GB usable on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM) (~2 GB spare), at ~39.8 tok/s (estimated), with room for about 16,384 tokens of context.
Confidence: high · Recommended quantization: Q6_K · Estimated speed: ~39.8 tok/s
| Usable memory for models | 16 GB |
| Memory bandwidth | 717 GB/s |
| Form factor | Laptop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
| Quant | Memory needed | Fits 16 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 30.5 GB | ✗ No | — | — | 28 GB |
| Q8_0 | 17.4 GB | ✗ No | — | — | 14.9 GB |
| Q6_K | 14 GB | ✓ Yes | 16K | ~39.8 tok/s | 11.5 GB |
| Q5_K_M | 12.4 GB | ✓ Yes | 16K | ~44.9 tok/s | 9.9 GB |
| Q4_K_M | 10.9 GB | ✓ Yes | 16K | ~51.2 tok/s | 8.5 GB |
| Q3_K_M | 8.4 GB | ✓ Yes | 16K | ~67.1 tok/s | 6 GB |
| Q2_K | 7.1 GB | ✓ Yes | 16K | ~80.9 tok/s | 4.6 GB |
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
Yes — Phi-4 (14B) at Q6_K needs about 14 GB of the 16 GB usable on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM) (~2 GB spare), at ~39.8 tok/s (estimated), with room for about 16,384 tokens of context.
Q6_K — it needs about 14 GB of the 16 GB available, downloads as roughly 11.5 GB, and runs at an estimated 39.8 tokens/sec with up to 16K 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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