Phi-4 14B quants compared
26 GGUF builds by real file size, probed from bartowski/Phi-4-GGUF on Hugging Face (2026-09-02). 14B params.
Download Phi-4 14B Q4_K_M (8.43 GB) — it fits 16 GB of memory with 16k context. With Ollama: ollama run phi4:14b-q4_K_M
Every Phi-4 14B quant by real file size
| Quant | Weights | + KV (16k) | Total | Fits comfortably in | Quality |
|---|---|---|---|---|---|
| F32 | 54.61 GB | 3.0 GB | 57.6 GB | 96 GB | Full precision (lossless) |
| F16 | 27.31 GB | 3.0 GB | 30.3 GB | 48 GB | Full precision (lossless) |
| Q8_0 | 14.51 GB | 3.0 GB | 17.5 GB | 24 GB | Near-lossless |
| Q6_K_L | 11.44 GB | 3.0 GB | 14.4 GB | 24 GB | Excellent |
| Q6_K | 11.2 GB | 3.0 GB | 14.2 GB | 16 GB | Excellent |
| Q5_K_L | 10.17 GB | 3.0 GB | 13.2 GB | 16 GB | Very high |
| Q5_K_M | 9.88 GB | 3.0 GB | 12.9 GB | 16 GB | Very high |
| Q5_K_S | 9.45 GB | 3.0 GB | 12.4 GB | 16 GB | Very high |
| Q4_K_M * | 8.43 GB | 3.0 GB | 11.4 GB | 16 GB | High — the default pick |
| Q4_K_L | 8.79 GB | 3.0 GB | 11.8 GB | 16 GB | High |
| Q4_1 | 8.63 GB | 3.0 GB | 11.6 GB | 16 GB | High |
| Q4_K_S | 7.86 GB | 3.0 GB | 10.9 GB | 16 GB | High |
| Q4_0 | 7.83 GB | 3.0 GB | 10.8 GB | 16 GB | High |
| IQ4_NL | 7.81 GB | 3.0 GB | 10.8 GB | 16 GB | High |
| IQ4_XS | 7.4 GB | 3.0 GB | 10.4 GB | 12 GB | High |
| Q3_K_XL | 7.8 GB | 3.0 GB | 10.8 GB | 12 GB | Acceptable — visible loss |
| Q3_K_L | 7.39 GB | 3.0 GB | 10.4 GB | 12 GB | Acceptable — visible loss |
| Q3_K_M | 6.86 GB | 3.0 GB | 9.9 GB | 12 GB | Acceptable — visible loss |
| IQ3_M | 6.44 GB | 3.0 GB | 9.4 GB | 12 GB | Acceptable — visible loss |
| Q3_K_S | 6.06 GB | 3.0 GB | 9.1 GB | 12 GB | Acceptable — visible loss |
| IQ3_XS | 5.82 GB | 3.0 GB | 8.8 GB | 12 GB | Acceptable — visible loss |
| Q2_K_L | 5.63 GB | 3.0 GB | 8.6 GB | 12 GB | Experimental — not ranked — never recommended |
| Q2_K | 5.17 GB | 3.0 GB | 8.2 GB | 12 GB | Experimental — not ranked — never recommended |
| IQ2_M | 4.76 GB | 3.0 GB | 7.8 GB | 12 GB | Experimental — not ranked — never recommended |
| IQ2_S | 4.41 GB | 3.0 GB | 7.4 GB | 12 GB | Experimental — not ranked — never recommended |
| IQ2_XS | 4.18 GB | 3.0 GB | 7.2 GB | 8 GB | Experimental — not ranked — never recommended |
* default pick. Weights = real GGUF file sizes from bartowski/Phi-4-GGUF (probed 2026-09-02). KV = fp16 estimate; a q8_0 cache roughly halves it. "Comfortable" = weights + KV within 90% of memory.
Best Phi-4 14B quant by memory
| Memory | Recommended quant | Total (16k ctx) |
|---|---|---|
| 12 GB | IQ4_XS | 10.4 GB |
| 16 GB | Q6_K | 14.2 GB |
| 24 GB | Q8_0 | 17.5 GB |
| 48 GB | F16 | 30.3 GB |
Why we don't rank Phi-4 14B's 2-bit quants
Quants at 2 bits per weight or below (Q2_K, IQ2, IQ1, TQ1) cut file size by roughly half versus Q4, but the quality collapse is steep and non-linear: perplexity spikes, instruction-following degrades, and hallucinations rise. A model that answers faster but wrong is not a smaller model — it is a worse one. ModelFit lists these builds for completeness but never ranks or recommends them.
Frequently asked questions
What is the best quantization of Phi-4 14B?
Q4_K_M is the default pick: 8.43 GB of weights, high — the default pick quality, fitting comfortably in 16 GB of memory (weights + 16k context KV-cache). Go Q6_K or Q8_0 if you have headroom.
How much memory does Phi-4 14B need?
At Q4_K_M, Phi-4 14B needs 8.43 GB for the weights plus ~3.0 GB of KV-cache at 16k context — about 11.4 GB total, so a 16 GB card or Mac (90% usable budget) runs it comfortably.
Should I use a Q2_K or IQ2 quant of Phi-4 14B?
No. Phi-4 14B at 2 bits per weight is a visibly worse model — quality collapse at that bitrate is steep, not gradual. If only a 2-bit build fits your memory, run a smaller model at Q4_K_M instead. ModelFit lists these builds but never recommends them.
Cite this page
ModelFit: Phi-4 14B quantization comparison (real GGUF file sizes). https://modelfit.io/quant-compare/phi4-14b/ (data probed 2026-09-02, CC BY 4.0).