About this model
Phi-4 14B is Microsoft's quality-focused 14B: trained on carefully curated data, it performs strongly in math and reasoning for its size. Its 16K context is shorter than peers, which matters for long documents.
What it is good for
- Math and step-by-step reasoning
- Structured outputs and data extraction
- Quality-focused chat on 12 GB cards
Limitations
- 16K context is shorter than the 32K–128K of many peers
- Less suited to long-document workflows
Memory by quantization (estimates)
Weights at a given bit-width plus estimated runtime and context overhead. These are estimates — see the methodology.
| Quantization | Approx. memory | Fits a 10 GB card | Recommended class |
|---|---|---|---|
| Q4 (GGUF) | ~8.6 GB | Good | 10 GB+ |
| Q5 (GGUF) | ~10.2 GB | Tight | 11 GB+ |
| Q8 (GGUF) | ~15 GB | Tight | 15 GB+ |
GPUs that can run it
FAQ
Is Phi-4 good at math?
Yes — it is one of the strongest 14B-class models for math and stepwise reasoning, competing with larger models on those tasks.
Why is its context only 16K?
It was trained for a 16K window. For long-document work, prefer models with 32K+ context such as the Qwen or Gemma families.
Can an RTX 4060 run it?
Only partially — Q4 needs 8–9 GB, so an 8 GB card must offload to CPU. A 12 GB card (4070, 3060 12 GB) runs it fully.
Phi-4 vs Qwen2.5 14B?
Phi-4 leans into reasoning and math; Qwen 14B is broader with longer context and stronger multilingual/tooling support. Pick by workload.