Phi-4 Mini 3.8B is a compact yet powerful language model developed by Microsoft, designed for efficient local deployment. With 3.8 billion parameters, this model excels in generating coherent and contextually rich text across a wide range of applications, including content creation, chatbot interactions, and summarization tasks. The model’s architecture, known as phi4, allows it to handle large context lengths up to 131,072 tokens, making it particularly useful for tasks that require deep contextual understanding, such as long-form writing or detailed document analysis. Despite its relatively modest size, Phi-4 Mini 3.8B punches well above its weight, offering performance and output quality that rival larger models while consuming significantly less computational resources.
In terms of efficiency, Phi-4 Mini 3.8B stands out in its size class. It requires only 2.8 to 4.3 GB of VRAM, making it accessible for users with mid-range GPUs. This efficiency, combined with the availability of quantizations like Q4_K_M and Q8_0, ensures that the model can be deployed on a variety of hardware setups, from high-end workstations to more modest consumer-grade systems. Ideal users include developers, content creators, and businesses looking to leverage advanced text generation capabilities without the need for expensive cloud services. For those with limited hardware resources, Phi-4 Mini 3.8B offers a compelling balance of performance and resource efficiency, making it a versatile choice for a broad spectrum of applications.
| Quantization | Bits | File Size | VRAM Needed | RAM Needed | Quality |
|---|---|---|---|---|---|
| Q4_K_M | 4.5 | 2.321 GB | 2.82 GB | 3.32 GB | 85% |
| Q8_0 | 8 | 3.804 GB | 4.3 GB | 4.8 GB | 98% |
Context window & KV cache
Adds 0.66 GB to VRAMLong chats and RAG inputs cost real memory. Drag to see how 32K vs 128K context shifts your grade.
Model native max: 128K tokens. KV-cache estimate is approximate (±30 %); real usage depends on attention layout.
How to run Phi-4 Mini 3.8B
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-mini - 2
Chat
ollama run phi4-mini - 3
Use as API
curl http://localhost:11434/api/chat \ -d '{"model":"phi4-mini","messages":[{"role":"user","content":"Hi"}]}'
Community benchmarks
Real tokens/sec reports from people running Phi-4 Mini 3.8B on actual hardware.
No community runs yet for this model. Be the first to submit your numbers.
Self-host serving plan
Want to host Phi-4 Mini 3.8Bfor many users? Or run it on a card that’s technically too small? Slide the knobs.
VRAM needed
3.8 GB
2.8 GB weights + 0.5 GB KV
Aggregate tok/s
66
across 1 user
Per-user tok/s
66
3.8 B dense
✅ Fits in 24 GB VRAM with 20.2 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 Mini 3.8B?
Phi-4 Mini 3.8B requires 2.82 GB VRAM minimum with Q4_K_M quantization. For full precision you need 4.3 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 Mini 3.8B?
To run Phi-4 Mini 3.8B, you need a GPU with at least 2.8 GB of VRAM, but 4.3 GB is recommended for optimal performance, especially with higher quantization levels.
Is Phi-4 Mini 3.8B good for coding?
Yes, Phi-4 Mini 3.8B is well-suited for coding tasks due to its strong reasoning capabilities and large context length of 131,072 tokens, which allows it to handle complex code snippets and documentation.
Phi-4 Mini 3.8B vs Llama 3.1 8B?
Phi-4 Mini 3.8B has fewer parameters (3.8B vs 8B) but is more efficient in terms of VRAM usage and performance, making it a better choice for systems with limited resources. It also offers a larger context length of 131,072 tokens compared to Llama 3.1 8B.
Can I run Phi-4 Mini 3.8B on a Mac?
Yes, you can run Phi-4 Mini 3.8B on a Mac, provided your Mac has a compatible GPU with at least 2.8 GB of VRAM. Ensure you have the necessary drivers and software installed for optimal performance.
How much VRAM does Phi-4 Mini 3.8B need?
Phi-4 Mini 3.8B requires between 2.8 GB and 4.3 GB of VRAM, depending on the quantization level used. Higher quantization levels generally require more VRAM but offer better performance.
Is Phi-4 Mini 3.8B censored?
Phi-4 Mini 3.8B is not inherently censored, but it may include content filters or safeguards to prevent the generation of harmful or inappropriate content, as is common in many AI models.
Is Phi-4 Mini 3.8B commercial-use allowed?
Yes, Phi-4 Mini 3.8B is licensed under the MIT License, which allows for both personal and commercial use without additional restrictions.
Phi-4 Mini 3.8B context length?
Phi-4 Mini 3.8B has a context length of 131,072 tokens, which is significantly larger than many other models, allowing it to process and generate longer sequences of text.
Does Phi-4 Mini 3.8B support function calling?
Yes, Phi-4 Mini 3.8B supports function calling, enabling it to interact with external APIs and perform actions based on user input or generated text.
Phi-4 Mini 3.8B quantization options?
Phi-4 Mini 3.8B supports various quantization options, including INT8, INT4, and FP16, which allow you to balance between model size, performance, and VRAM usage.
Can Phi-4 Mini 3.8B run on CPU?
While Phi-4 Mini 3.8B can run on a CPU, it will be significantly slower compared to running on a GPU. For optimal performance, a GPU with at least 2.8 GB of VRAM is recommended.
Phi-4 Mini 3.8B fine-tuning?
Yes, Phi-4 Mini 3.8B can be fine-tuned on custom datasets to improve its performance on specific tasks or domains. Fine-tuning typically requires a powerful GPU and a significant amount of data.
Phi-4 Mini 3.8B system requirements?
To run Phi-4 Mini 3.8B, you need a system with at least 8 GB of RAM, a GPU with 2.8 GB to 4.3 GB of VRAM, and a modern CPU. Additionally, ensure you have the latest drivers and necessary software libraries installed.
Phi-4 Mini 3.8B performance benchmark?
Phi-4 Mini 3.8B can process around 100-200 tokens per second on a mid-range GPU, with higher performance achievable on more powerful GPUs. The exact speed depends on the quantization level and system configuration.
Phi-4 Mini 3.8B for RAG?
Yes, Phi-4 Mini 3.8B is suitable for Retrieval-Augmented Generation (RAG) tasks, thanks to its large context length and ability to integrate external information effectively.
Phi-4 Mini 3.8B for agents?
Phi-4 Mini 3.8B can be used to create intelligent agents due to its strong reasoning capabilities and support for function calling, making it ideal for tasks that require interaction with the environment.
Phi-4 Mini 3.8B for coding vs general?
Phi-4 Mini 3.8B performs well in both coding and general tasks, but its large context length and strong reasoning capabilities make it particularly effective for coding, handling complex code snippets and documentation.
Phi-4 Mini 3.8B vs ChatGPT?
Phi-4 Mini 3.8B is smaller (3.8B parameters) and more resource-efficient than ChatGPT, but it offers a larger context length (131,072 tokens) and is more flexible in terms of deployment and customization.
Phi-4 Mini 3.8B download size?
The download size of Phi-4 Mini 3.8B varies depending on the quantization level. Typically, it ranges from 2 GB to 4 GB, with lower quantization levels resulting in smaller file sizes.
Best quant for Phi-4 Mini 3.8B?
The best quantization for Phi-4 Mini 3.8B depends on your specific needs. INT8 offers a good balance between performance and VRAM usage, while FP16 provides the highest accuracy but requires more VRAM.