Can you run Phi 4 locally?

Phi family · · 14.7B parameters · Hugging Face model card

How much VRAM does Phi 4 need at each quantization?

Phi 4 needs 9.7 GB of VRAM at Q4_K_M. The table below lists weights-only size and total VRAM including overhead for each common quantization level with 4k and 32k token contexts.

Method: weights = parameters × bits-per-weight (Q4_K_M ≈ 4.8, Q5_K_M ≈ 5.7, Q6_K ≈ 6.6, Q8_0 ≈ 8.5, FP16 = 16), plus 10% loading overhead, plus KV cache from the verified architecture config (40 layers, 10 KV heads, head dim). Source: HF-verified 2026-09-12 via api (params=14.66B ctx=16384 license=mit); VRAM figures are documented calculations, not measurements

Which GPUs can run Phi 4 locally?

At Q4_K_M with a 4k context, Phi 4 needs 9.7 GB of VRAM. The lists below are computed live from our GPU database and grouped by how much headroom the card has. Disclosure: CompareAIHardware.com participates in the Amazon Associates program and earns from qualifying purchases through links on this page. Affiliate relationships do not influence our recommendations.

Runs Phi 4 comfortably (25%+ VRAM headroom)

Minimum GPUs that fit Phi 4 at Q4_K_M

These GPUs hold the model but leave little headroom — keep contexts short.

Needs 2+ GPUs to run Phi 4

One of these cards is too small on its own, but a pair (tensor or pipeline parallel, ~90% efficiency) covers the 9.7 GB requirement. See our multi-GPU guide for setup.

Can a Mac run Phi 4?

Yes — these Apple Silicon machines fit Phi 4 at Q4_K_M, since macOS lets the GPU use about 75% of unified memory. Generation speed is bound by memory bandwidth, so the GB/s column matters as much as capacity.

Can Phi 4 run on mini PCs, Jetson, or NPU devices?

These edge and NPU devices from our database have enough memory for Phi 4 at Q4_K_M. Their memory bandwidth is far below discrete GPUs, so expect a fraction of desktop generation speed.

Which pre-built systems can run Phi 4?

These mini PCs and workstations from our database fit Phi 4 at Q4_K_M. Usable-memory figures are conservative: Windows shares about half of system RAM with the GPU by default (Linux can expose more), macOS lets Apple Silicon GPUs use about 75% of unified memory, and Linux unified-memory systems such as GB10 expose roughly 90%. Generation speed is bound by memory bandwidth, so compare the GB/s column before buying.

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