Phi-4 is a large language model (LLM) developed by Microsoft, boasting 14 billion parameters and an impressive context length of 16,384 tokens. This model is particularly adept at generating coherent and contextually rich text, making it suitable for tasks that require deep understanding and nuanced responses, such as content creation, chatbots, and natural language understanding. Phi-4's architecture, based on the phi3 framework, ensures that it can handle complex and lengthy inputs, which is crucial for applications that involve extensive dialogues or detailed document analysis.
In its size class, Phi-4 holds its own, offering a balance between performance and efficiency. While it may not outperform the largest models in terms of raw capability, it provides a more practical option for users with moderate hardware. The model is available in several quantized versions, including Q4_K_M, Q5_K_M, and Q8_0, which can significantly reduce the VRAM requirements, making it viable for systems with 8.9 to 15.0 GB of VRAM. This makes Phi-4 a compelling choice for developers and researchers who need a powerful LLM but may not have access to the most advanced hardware. Ideal users include those working on projects that demand high-quality text generation and understanding, but with a more pragmatic approach to resource management.
| Quantization | Bits | File Size | VRAM Needed | RAM Needed | Quality |
|---|---|---|---|---|---|
| Q4_K_M | 4.5 | 8.431 GB | 8.93 GB | 9.43 GB | 85% |
| Q5_K_M | 5.5 | 9.876 GB | 10.38 GB | 10.88 GB | 90% |
| Q8_0 | 8 | 14.51 GB | 15.01 GB | 15.51 GB | 98% |
Context window & KV cache
Adds 1.25 GB to VRAMLong chats and RAG inputs cost real memory. Drag to see how 32K vs 128K context shifts your grade.
Model native max: 16K tokens. KV-cache estimate is approximate (±30 %); real usage depends on attention layout.
How to run Phi-4
Pick a runtime — copy & paste. Commands are pre-filled with this model’s repo.
Easiest. Single command. OpenAI-compatible API on :11434.
Ollama home →- 1
Pull the model
ollama pull phi4 - 2
Chat
ollama run phi4 - 3
Use as API
curl http://localhost:11434/api/chat \ -d '{"model":"phi4","messages":[{"role":"user","content":"Hi"}]}'
Community benchmarks
Real tokens/sec reports from people running Phi-4 on actual hardware.
| GPU | Median tok/s | Reports | Typical setup |
|---|---|---|---|
| RTX 4090 | 76.8 | 1 | Q4_K_M · Ollama · Linux · 4K ctx |
| M2 Max | 28.5 | 1 | Q4_K_M · Ollama · macOS · 4K ctx |
| RTX 3060 12GB | 24.1 | 1 | Q4_K_M · Ollama · Windows · 4K ctx |
Self-host serving plan
Want to host Phi-4for many users? Or run it on a card that’s technically too small? Slide the knobs.
VRAM needed
10.4 GB
8.9 GB weights + 0.9 GB KV
Aggregate tok/s
18
across 1 user
Per-user tok/s
18
14 B dense
✅ Fits in 24 GB VRAM with 13.6 GB headroom. Pure-GPU inference — full speed.
Throughput is a sub-linear estimate: doubling users adds ~70 % of single-user TPS until ~8, then plateaus on memory bandwidth. MoE models scale concurrency much better because each user activates a different subset of experts.
See It In Action
Real model outputs generated via RunThisModel.com — watch responses stream in real time.
Outputs generated by real AI models via RunThisModel.com. Generation speed shown is from cloud inference. Local speeds vary by hardware — check your device.
how much VRAM do I need to run Phi-4?
Phi-4 requires 8.93 GB VRAM minimum with Q4_K_M quantization. For full precision you need 15.01 GB.
which quant should I pick?
Q4_K_M is the best quality/VRAM balance — ~92% of FP16 quality at ~25% the footprint. Q8_0 is near-lossless if you have the headroom.
What GPU do I need to run Phi-4?
To run Phi-4, you need a GPU with at least 8.9 GB of VRAM, but 15.0 GB is recommended for optimal performance.
Is Phi-4 good for coding?
Yes, Phi-4 is well-suited for coding tasks due to its strong reasoning capabilities and large context length of 16,384 tokens.
Phi-4 vs Llama 3.1 8B?
Phi-4 has 14 billion parameters compared to Llama 3.1's 8 billion, making it more powerful for complex tasks but requiring more VRAM.
Can I run Phi-4 on a Mac?
Yes, you can run Phi-4 on a Mac with a compatible GPU, such as an AMD or NVIDIA card with sufficient VRAM.
How much VRAM does Phi-4 need?
Phi-4 requires between 8.9 GB and 15.0 GB of VRAM, depending on the quantization level used.
Is Phi-4 censored?
Phi-4 is not inherently censored, but its outputs can be filtered based on the implementation and configuration settings.
Is Phi-4 commercial-use allowed?
Yes, Phi-4 is licensed under the MIT License, which allows for commercial use without restriction.
Phi-4 context length?
Phi-4 has a context length of 16,384 tokens, allowing it to handle longer sequences of text effectively.
Does Phi-4 support function calling?
Yes, Phi-4 supports function calling, enabling it to interact with external systems and APIs seamlessly.
Phi-4 quantization options?
Phi-4 supports various quantization options, including INT8 and INT4, which reduce VRAM usage and improve inference speed.
Can Phi-4 run on CPU?
While Phi-4 can technically run on a CPU, it will be significantly slower and less efficient compared to running on a GPU.
Phi-4 fine-tuning?
Yes, Phi-4 can be fine-tuned on specific datasets to improve performance on particular tasks, though this requires significant computational resources.
Phi-4 system requirements?
Phi-4 requires a powerful GPU with 8.9 GB to 15.0 GB of VRAM, ample RAM (at least 32 GB), and a multi-core CPU for optimal performance.
Phi-4 performance benchmark?
Phi-4 can process around 100-150 tokens per second on a high-end GPU like the RTX 3090, depending on the task complexity and quantization level.
Phi-4 for RAG?
Yes, Phi-4 is suitable for Retrieval-Augmented Generation (RAG) tasks, leveraging its large context length and strong reasoning abilities.
Phi-4 for agents?
Phi-4 can be used to create intelligent agents due to its ability to understand and generate complex, context-rich responses.
Phi-4 for coding vs general?
Phi-4 excels in both coding and general tasks, but its strong reasoning and context handling make it particularly effective for coding and technical applications.
Phi-4 vs ChatGPT?
Phi-4 has a larger context length (16,384 tokens) and is more customizable, while ChatGPT offers a more polished user experience and is optimized for conversational tasks.
Phi-4 download size?
The download size for Phi-4 varies based on the quantization level, typically ranging from 10 GB to 20 GB.
Best quant for Phi-4?
The best quantization for Phi-4 depends on your use case, but INT8 is a good balance between performance and VRAM efficiency, while INT4 is more VRAM-friendly but may have slightly reduced accuracy.