Phi-3.5 Vision, developed by Microsoft, is a 4.2 billion parameter multimodal model designed to convert images into descriptive text. It excels in generating detailed and contextually rich captions for a wide range of images, making it particularly useful for applications like automated image labeling, content moderation, and assistive technologies. The model’s large context length of 131,072 tokens allows it to handle complex scenes and provide nuanced descriptions, which is a significant advantage over smaller models.
In its size class, Phi-3.5 Vision stands out for its efficiency and performance. Despite having 4.2 billion parameters, it requires only 3.2 GB of VRAM, making it accessible on a variety of hardware setups. This balance between size and capability means it can punch above its weight, offering high-quality outputs without the need for top-tier GPUs. Users who need robust image-to-text capabilities but have limited computational resources will find this model particularly appealing. Realistic hardware for running Phi-3.5 Vision includes mid-range GPUs and even some high-end CPUs, making it a versatile choice for both developers and hobbyists.
| Quantization | Bits | File Size | VRAM Needed | RAM Needed | Quality |
|---|---|---|---|---|---|
| Q4_K_M | 4.5 | 2.5 GB | 3.2 GB | 5 GB | 85% |
Context window & KV cache
Adds 1.00 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 Vision
Pick a runtime — copy & paste. Commands are pre-filled with this model’s repo.
GUI. Browse → download → chat. MLX on Apple Silicon.
LM Studio home →- 1
Open LM Studio
Go to the 🔍 Search tab.
- 2
Search for
abetlen/Phi-3.5-vision-instruct-gguf - 3
Download
Pick the Q4_K_M quant — best balance of size vs. quality.
- 4
Chat
Hit ▶ Load Model and start chatting. Toggle 'Local Server' to expose an OpenAI-compatible API on :1234.
Community benchmarks
Real tokens/sec reports from people running Phi-3.5 Vision 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 Visionfor many users? Or run it on a card that’s technically too small? Slide the knobs.
VRAM needed
4.2 GB
3.2 GB weights + 0.5 GB KV
Aggregate tok/s
60
across 1 user
Per-user tok/s
60
4.2 B dense
✅ Fits in 24 GB VRAM with 19.8 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.
how much VRAM do I need to run Phi-3.5 Vision?
Phi-3.5 Vision requires 3.2 GB VRAM minimum with Q4_K_M quantization. For full precision you need 3.2 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 Vision?
To run Phi-3.5 Vision, you need a GPU with at least 3.2 GB of VRAM. Higher VRAM will improve performance, especially for larger tasks.
Is Phi-3.5 Vision good for coding?
Phi-3.5 Vision is primarily designed for vision and language tasks, such as understanding images and documents. It may not be as optimized for coding-specific tasks compared to models like Codex or CodeLlama.
Phi-3.5 Vision vs Llama 3.1 8B?
Phi-3.5 Vision has 4.2 billion parameters and is specialized for vision-language tasks, while Llama 3.1 8B is a text-only model with 8 billion parameters, making it more versatile for text generation but less suited for image understanding.
Can I run Phi-3.5 Vision on a Mac?
Yes, you can run Phi-3.5 Vision on a Mac, but ensure your Mac has a compatible GPU with at least 3.2 GB of VRAM. Apple Silicon GPUs may require additional drivers or software.
How much VRAM does Phi-3.5 Vision need?
Phi-3.5 Vision requires 3.2 GB of VRAM, which is consistent across different quantization levels. More VRAM can help with larger batch sizes and more complex tasks.
Is Phi-3.5 Vision censored?
Phi-3.5 Vision is not inherently censored, but it adheres to ethical guidelines and may have filters to prevent harmful content. Users can configure additional safety measures as needed.
Is Phi-3.5 Vision commercial-use allowed?
Yes, Phi-3.5 Vision is licensed under the MIT License, which allows for commercial use. However, always review the specific terms of the license to ensure compliance.
Phi-3.5 Vision context length?
Phi-3.5 Vision has a context length of 131,072 tokens, allowing it to process very long sequences of text and images effectively.
Does Phi-3.5 Vision support function calling?
Phi-3.5 Vision does not natively support function calling, but you can integrate it with external tools and APIs to extend its functionality for specific tasks.
Phi-3.5 Vision quantization options?
Phi-3.5 Vision supports quantization to reduce model size and improve inference speed. Common options include INT8 and FP16, which can significantly reduce VRAM usage while maintaining performance.
Can Phi-3.5 Vision run on CPU?
While Phi-3.5 Vision can technically run on a CPU, it is highly recommended to use a GPU for better performance and faster inference times due to the model's size and complexity.
Phi-3.5 Vision fine-tuning?
Phi-3.5 Vision can be fine-tuned on custom datasets to improve performance on specific tasks. This typically requires a powerful GPU and a significant amount of data.
Phi-3.5 Vision system requirements?
To run Phi-3.5 Vision, you need a system with at least 3.2 GB of VRAM, 16 GB of RAM, and a modern CPU. SSD storage is recommended for faster data loading.
Phi-3.5 Vision performance benchmark?
Performance benchmarks for Phi-3.5 Vision vary based on hardware, but a typical GPU like an RTX 3090 can achieve around 100-150 tokens per second for text generation and image understanding tasks.
Phi-3.5 Vision for RAG?
Phi-3.5 Vision can be used for Retrieval-Augmented Generation (RAG) tasks, where it can generate text based on retrieved information from a database or document corpus.
Phi-3.5 Vision for agents?
Phi-3.5 Vision can be integrated into autonomous agents to enhance their ability to understand and interact with visual and textual information, making it suitable for robotics and virtual assistants.
Phi-3.5 Vision for coding vs general?
Phi-3.5 Vision is more suited for general vision-language tasks rather than coding-specific tasks. For coding, consider models like Codex or CodeLlama, which are optimized for programming languages.
Phi-3.5 Vision vs ChatGPT?
Phi-3.5 Vision is a multimodal model that excels in understanding images and documents, while ChatGPT is a text-only model optimized for conversational tasks. Choose based on your specific use case.
Phi-3.5 Vision download size?
The download size for Phi-3.5 Vision is approximately 8 GB for the full model, but this can vary depending on the quantization level and additional dependencies.
Best quant for Phi-3.5 Vision?
The best quantization for Phi-3.5 Vision depends on your hardware and performance needs. INT8 is a good balance between speed and accuracy, while FP16 offers higher precision at the cost of more VRAM usage.