What hardware runs Phi 4 14B?
Phi 4 14B is a 14.66B-parameter dense model (mit license). Dense reasoning-per-gigabyte champion, dry as toast. The 16K context ceiling is the real constraint.
All figures assume an f16 KV cache, a 0.6 GB display reserve on the GPU, and 64 GB of DDR5 system RAM for the offload tiers. Tune these in the calculator.
Minimum VRAM by quant and context
| Quant | File size | 8K | 16K |
|---|---|---|---|
| Q2_K * | 5.2 GB | 8.7 GB | 10.3 GB |
| Q3_K_M * | 6.7 GB | 10.1 GB | 11.8 GB |
| Q4_K_M | 8.3 GB | 11.7 GB | 13.4 GB |
| Q5_K_M | 9.7 GB | 13.1 GB | 14.8 GB |
| Q6_K | 11.2 GB | 14.6 GB | 16.3 GB |
| Q8_0 | 14.5 GB | 18 GB | 19.6 GB |
Full-GPU figures: weights + f16 KV cache + overhead. * below our recommended floor of Q4_K_M.
GPU compatibility
Fits on GPUExpert offloadPartial offloadCPU only
Quant guidance
Our floor for Phi 4 14B is Q4_K_M — below that, quality degrades faster than the VRAM savings are worth. Prefer the highest quant that still lands "Fits on GPU" at your context length in the table above.
Frequently asked questions
How much VRAM does Phi 4 14B need?
At Q4_K_M and 16K context, Phi 4 14B needs about 13.4 GB of VRAM to run fully on GPU (weights + KV cache + overhead).
What is the smallest GPU that can run Phi 4 14B?
No single consumer GPU in our set runs Phi 4 14B well — it's a cloud-rental or multi-GPU model.
What quantization should I use for Phi 4 14B?
We recommend Q4_K_M or higher. Q4_K_M weighs 8.3 GB (4.85 bits/weight); going below Q4_K_M costs noticeable quality on a model this size.
How long a context can Phi 4 14B handle?
Phi 4 14B supports up to 16K tokens. KV cache grows linearly with context.
Can I run Phi 4 14B without a GPU?
Yes, at reduced speed: on CPU with 64 GB of DDR5 it manages roughly 3 tokens/sec at 8K context (Q8_0).