Can I Run Phi-4 Family on 32 GB system RAM?
Autor: Jakub Rusinowski · Ostatnia aktualizacja: 11 listopada 2026
Yes — comfortably
Yes, comfortably — Phi-4 Reasoning Vision 15B at Q6_K needs about 14.9 GB of the 25.6 GB usable on 32 GB system RAM, leaving ~10.7 GB spare and running at ~5 tok/s (estimated), with room for about 16,384 tokens of context.
Confidence: high · Recommended quantization: Q6_K · Estimated speed: ~5 tok/s
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32 GB system RAM — what it gives a model
| Usable memory for models | 25.6 GB |
| Memory bandwidth | 90 GB/s |
Phi-4 Family on 32 GB system RAM: memory by quantization
| Quant | Memory needed | Fits 25.6 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 32.7 GB | ✗ No | — | — | 30.2 GB |
| Q8_0 | 18.5 GB | ✓ Yes | 16K | ~3.9 tok/s | 16.1 GB |
| Q6_K | 14.9 GB | ✓ Yes | 16K | ~5 tok/s | 12.4 GB |
| Q5_K_M | 13.2 GB | ✓ Yes | 16K | ~5.7 tok/s | 10.7 GB |
| Q4_K_M | 11.6 GB | ✓ Yes | 16K | ~6.6 tok/s | 9.1 GB |
| Q3_K_M | 8.9 GB | ✓ Yes | 16K | ~9 tok/s | 6.4 GB |
| Q2_K | 7.4 GB | ✓ Yes | 16K | ~11.3 tok/s | 5 GB |
Which Phi-4 Family sizes fit
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| Phi-4 Reasoning Vision 15B | 11.6 GB | ✓ Fits | ~6.6 tok/s |
| Phi-4 (14B) | 10.9 GB | ✓ Fits | ~7.1 tok/s |
What to watch out for
- These figures assume CPU-only inference. Any discrete GPU, even an 8 GB one, will be several times faster for models that fit in its VRAM.
Recommended setup
llama.cpp (CPU build) or Ollama — both run without a GPU
How these numbers are calculated
- Memory need = quantized weights + KV cache at 8,192 tokens (f16) + 0.8 GB runtime overhead.
- 25.6 GB of the 32 GB is treated as usable for model weights (80% — the rest is the OS and running applications).
- DDR5-5600 dual channel at 89.6 GB/s peak. CPU decode is assumed to sustain 35% of that peak, because CPU inference is not purely bandwidth-bound — it also spends real time in compute and thread synchronisation. This figure is an assumption, not a fitted constant: no CPU measurement is in the calibration set.
- CPU-only inference: no GPU is assumed. A GPU of any size will beat these figures substantially.
- Throughput is a memory-bandwidth roofline estimate, not a measurement. It is labelled "estimated" everywhere it appears.
- KV cache is computed from this model's published attention configuration.
FAQ
Can I run Phi-4 Family on 32 GB system RAM?
Yes, comfortably — Phi-4 Reasoning Vision 15B at Q6_K needs about 14.9 GB of the 25.6 GB usable on 32 GB system RAM, leaving ~10.7 GB spare and running at ~5 tok/s (estimated), with room for about 16,384 tokens of context.
Which quantization of Phi-4 Family should I use on 32 GB system RAM?
Q6_K — it needs about 14.9 GB of the 25.6 GB available, downloads as roughly 12.4 GB, and runs at an estimated 5 tokens/sec with up to 16K of context.
What limits Phi-4 Family on 32 GB system RAM?
Nothing binding — the model fits with headroom and generates at a usable speed on this hardware.
Which runtime should I use?
llama.cpp (CPU build) or Ollama — both run without a GPU
Other RAM Capacities
Other Models on 32 GB system RAM
- Phi-4 Mini on 32 GB system RAM
- Poolside Laguna XS 2.1 on 32 GB system RAM
- Qwen 2.5 Family on 32 GB system RAM
- Qwen 2.5 VL on 32 GB system RAM
- Qwen 3 on 32 GB system RAM
Phi-4 Family on GPUs
- Phi-4 Family on NVIDIA GeForce RTX 5080
- Phi-4 Family on NVIDIA GeForce RTX 5070 Ti
- Phi-4 Family on NVIDIA GeForce RTX 5070
- Phi-4 Family on NVIDIA GeForce RTX 5060 Ti 16GB
What This Model Is Good At
Model & Tools
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