Phi-3.5 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 strikes a balance between performance and resource consumption, making it suitable for a wide range of text generation tasks such as summarization, translation, and creative writing. Its architecture, based on the phi3 framework, allows it to handle context lengths up to 131,072 tokens, which is significantly larger than many models in its size class, enabling it to maintain coherence over long texts.
Compared to other models with similar parameter counts, Phi-3.5 Mini 3.8B punches above its weight in terms of efficiency and performance. It requires only 2.7 to 4.3 GB of VRAM, making it accessible on a variety of hardware, including mid-range GPUs. This makes it an excellent choice for developers and enthusiasts who need robust text generation capabilities without the need for high-end hardware. The model is available in several quantized versions (Q4_K_M, Q5_K_M, Q8_0), further enhancing its efficiency and reducing memory usage. Ideal users include those working on projects that require extensive text processing but have limited computational resources, such as small-scale applications, personal projects, or environments with strict resource constraints.
| Quantization | Bits | File Size | VRAM Needed | RAM Needed | Quality |
|---|---|---|---|---|---|
| Q4_K_M | 4.5 | 2.229 GB | 2.73 GB | 3.23 GB | 85% |
| Q5_K_M | 5.5 | 2.622 GB | 3.12 GB | 3.62 GB | 90% |
| Q8_0 | 8 | 3.782 GB | 4.28 GB | 4.78 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-3.5 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 phi3.5 - 2
Chat
ollama run phi3.5 - 3
Use as API
curl http://localhost:11434/api/chat \ -d '{"model":"phi3.5","messages":[{"role":"user","content":"Hi"}]}'
Community benchmarks
Real tokens/sec reports from people running Phi-3.5 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-3.5 Mini 3.8Bfor many users? Or run it on a card that’s technically too small? Slide the knobs.
VRAM needed
3.7 GB
2.7 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 20.3 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-3.5 Mini 3.8B?
Phi-3.5 Mini 3.8B requires 2.73 GB VRAM minimum with Q4_K_M quantization. For full precision you need 4.28 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-3.5 Mini 3.8B?
Phi-3.5 Mini 3.8B requires a GPU with at least 2.7 GB of VRAM, but 4.3 GB is recommended for optimal performance.
Is Phi-3.5 Mini 3.8B good for coding?
Phi-3.5 Mini 3.8B is capable of generating code and providing coding assistance, but its performance is best suited for simpler tasks due to its 3.8B parameters.
Phi-3.5 Mini 3.8B vs Llama 3.1 8B?
Phi-3.5 Mini 3.8B has 3.8B parameters, making it smaller and more resource-efficient than Llama 3.1 8B, which has 8B parameters and requires more VRAM and computational power.
Can I run Phi-3.5 Mini 3.8B on a Mac?
Yes, Phi-3.5 Mini 3.8B can run on a Mac, provided your Mac has a compatible GPU with at least 2.7 GB of VRAM.
How much VRAM does Phi-3.5 Mini 3.8B need?
Phi-3.5 Mini 3.8B requires a minimum of 2.7 GB of VRAM, but 4.3 GB is recommended for better performance, depending on the quantization level.
Is Phi-3.5 Mini 3.8B censored?
Phi-3.5 Mini 3.8B is not inherently censored, but it may include content filters to prevent harmful or inappropriate content.
Is Phi-3.5 Mini 3.8B commercial-use allowed?
Yes, Phi-3.5 Mini 3.8B is licensed under the MIT License, which allows for commercial use.
Phi-3.5 Mini 3.8B context length?
Phi-3.5 Mini 3.8B supports a context length of 131,072 tokens, which is quite large and allows for extensive context in conversations and tasks.
Does Phi-3.5 Mini 3.8B support function calling?
Yes, Phi-3.5 Mini 3.8B supports function calling, enabling it to interact with external systems and APIs.
Phi-3.5 Mini 3.8B quantization options?
Phi-3.5 Mini 3.8B can be quantized to 4-bit, 8-bit, and 16-bit precision, allowing for trade-offs between model size and performance.
Can Phi-3.5 Mini 3.8B run on CPU?
Yes, Phi-3.5 Mini 3.8B can run on a CPU, but it will be significantly slower compared to running on a GPU.
Phi-3.5 Mini 3.8B fine-tuning?
Phi-3.5 Mini 3.8B can be fine-tuned on specific datasets to improve performance on particular tasks, but this requires a moderate amount of computational resources.
Phi-3.5 Mini 3.8B system requirements?
Phi-3.5 Mini 3.8B requires at least 2.7 GB of VRAM, 8 GB of RAM, and a multi-core CPU. A GPU with 4.3 GB of VRAM is recommended for optimal performance.
Phi-3.5 Mini 3.8B performance benchmark?
Phi-3.5 Mini 3.8B can process around 100-200 tokens per second on a mid-range GPU, depending on the quantization level and other factors.
Phi-3.5 Mini 3.8B for RAG?
Phi-3.5 Mini 3.8B can be used for Retrieval-Augmented Generation (RAG) tasks, but its performance may be limited due to its smaller size compared to larger models.
Phi-3.5 Mini 3.8B for agents?
Phi-3.5 Mini 3.8B is suitable for creating conversational agents and chatbots, especially for tasks that do not require extensive context or deep understanding.
Phi-3.5 Mini 3.8B for coding vs general?
Phi-3.5 Mini 3.8B is versatile and can handle both coding and general tasks, but it may not be as specialized as larger models for specific domains like advanced coding.
Phi-3.5 Mini 3.8B vs ChatGPT?
Phi-3.5 Mini 3.8B has 3.8B parameters and is more lightweight, making it easier to run on lower-end hardware, while ChatGPT is a larger model with more parameters and better performance on complex tasks.
Phi-3.5 Mini 3.8B download size?
The download size of Phi-3.5 Mini 3.8B varies depending on the quantization level, ranging from approximately 1.5 GB (4-bit) to 7.6 GB (16-bit).
Best quant for Phi-3.5 Mini 3.8B?
The best quantization level for Phi-3.5 Mini 3.8B depends on your hardware. 4-bit quantization is ideal for lower-end GPUs, while 8-bit and 16-bit are better for more powerful GPUs and higher performance.