Phi 3.5 Vision Instruct — Hardware Requirements & GPU Compatibility
VisionCode
Phi 3.5 Vision Instruct is Microsoft's 4.1-billion-parameter multimodal model, pairing an image encoder and connector with the Phi-3 Mini language model to handle text and code alongside pictures. It suits visual question answering, chart and table reading, document OCR, and comparing details across multiple images in one prompt. Its small size suits laptops and modest consumer GPUs, running smoothly even on limited hardware once quantized. The model supports a 128K token context window, enough for lengthy documents. It is released under the MIT license, one of the most permissive options available, allowing unrestricted commercial and research use. Published in August 2024, it was trained on roughly 500 billion tokens of synthetic and filtered web data.
726.7K downloads
739 likesSpecifications
- Publisher
- Microsoft
- Family
- Phi 3
- Parameters
- 4.1B
- Architecture
- Phi3VForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 32,064
- Release Date
- 2024-08-16
- License
- MIT
Get Started
HuggingFace
How Much VRAM Does Phi 3.5 Vision Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 2.9 GB | 53.6 GB | 1.76 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 3.1 GB | 53.9 GB | 2.02 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 3.6 GB | 54.3 GB | 2.49 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 4.1 GB | 54.8 GB | 2.95 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 4.5 GB | 55.3 GB | 3.42 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 5.3 GB | 56.0 GB | 4.15 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 9.4 GB | 60.1 GB | 8.29 GB | Brain floating point 16 — preferred for training |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Phi 3.5 Vision Instruct?
Q4_K_M · 3.6 GBPhi 3.5 Vision Instruct (Q4_K_M) requires 3.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 5+ GB is recommended. Using the full 131K context window can add up to 50.7 GB, bringing total usage to 54.3 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Phi 3.5 Vision Instruct?
Q4_K_M · 3.6 GB59 devices with unified memory can run Phi 3.5 Vision Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, iPhone 17.
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does Phi 3.5 Vision Instruct need?
Phi 3.5 Vision Instruct requires 3.6 GB of VRAM at Q4_K_M, or 9.4 GB at BF16. Full 131K context adds up to 50.7 GB (54.3 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 4.1B × 4.8 bits ÷ 8 = 2.5 GB
KV Cache + Overhead ≈ 1.1 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 51.8 GB (at full 131K context)
VRAM usage by quantization
Q4_K_M3.6 GBQ4_K_M + full context54.3 GB- What's the best quantization for Phi 3.5 Vision Instruct?
For Phi 3.5 Vision Instruct, Q4_K_M (3.6 GB) offers the best balance of quality and VRAM usage. Q5_K_M (4.1 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 2.9 GB.
VRAM requirement by quantization
Q2_K2.9 GBQ4_K_M ★3.6 GBQ5_K_M4.1 GBQ6_K4.5 GBQ8_05.3 GBBF169.4 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Phi 3.5 Vision Instruct on a Mac?
Phi 3.5 Vision Instruct requires at least 2.9 GB at Q2_K, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Phi 3.5 Vision Instruct locally?
Yes — Phi 3.5 Vision Instruct can run locally on consumer hardware. At Q4_K_M quantization it needs 3.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Phi 3.5 Vision Instruct?
At Q4_K_M, Phi 3.5 Vision Instruct can reach ~1337 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~183 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 3.6 × 0.65 = ~1449 tok/s
Estimated speed at Q4_K_M (3.6 GB)
~1449 tok/s~183 tok/s~1449 tok/s~1337 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Phi 3.5 Vision Instruct?
At Q4_K_M, the download is about 2.49 GB. The full-precision BF16 version is 8.29 GB. The smallest option (Q2_K) is 1.76 GB.
- Which GPUs can run Phi 3.5 Vision Instruct?
52 consumer GPUs can run Phi 3.5 Vision Instruct at Q4_K_M (3.6 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Phi 3.5 Vision Instruct?
59 devices with unified memory can run Phi 3.5 Vision Instruct at Q4_K_M (3.6 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.