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.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 8K)Best on 24 GB (Q4_K_M · ~183 tok/s · 32K)Macs from 8 GB (Q4_K_M · ~20 tok/s)
MIT
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Phi 3.5 Vision Instruct
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
vllm serve microsoft/Phi-3.5-vision-instruct --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 3060 12GB (2 GB headroom) · Q4_K_M
- Generation speed
- ~65 tok/s
- generation speed
- Cost per 1M output tokens
- $0.25
- per 1M output tokens
How Much VRAM Does Phi 3.5 Vision Instruct Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 2.9 GB | 15 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~228 tok/s | |
| 3.1 GB | 15.2 GB | 3-bit medium — 3-bit medium quantization | Fits: 32K context · ~209 tok/s | |
| 3.6 GB | 15.7 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 32K context · ~183 tok/s | |
| 4.1 GB | 16.1 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 32K context · ~161 tok/s | |
| 4.5 GB | 16.6 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 32K context · ~145 tok/s | |
| 5.3 GB | 17.3 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 32K context · ~125 tok/s | |
| 9.4 GB | 21.5 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 32K context · ~70 tok/s |
Selected Q4_K_M
4-bit medium quantization9.2GBat 16K context
- Weights
- 2.5 GB
- KV cache
- 6.5 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 14.8 GB free · ~183 tok/s
≥50 tok/s needs ≥276 GB/s · RTX 5090 ~325, RTX 3090 Ti ~183
Which Hardware 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. 163 devices with unified memory can run Phi 3.5 Vision Instruct, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), MacBook Air 13" M3 (8 GB).
12 GB
12 GB tier →fits · 2.8 GB free · up to 16K ctx
10 GPUs · 1 device family
RTX 3080 Ti~165Simulate output on RTX 3080 TiRTX 5070~122Simulate output on RTX 5070RTX 4070~91Simulate output on RTX 4070iPhone~15Simulate output on Apple iPhone 17 Pro20 GB
fits · 10.8 GB free · up to 32K ctx
1 GPU
RX 7900 XT~134Simulate output on RX 7900 XT32–40 GB
fits · 22.8 GB free · up to 64K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~325Simulate output on RTX 5090M5 Max36 GB~90Simulate output on Mac Studio M5 Max (36 GB)M532 GB~30Simulate output on MacBook Air 13" M5 (32 GB)Orin~37Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~24Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Phi 3.5 Vision Instruct
Phi 3 family · 15 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Spark X2.6 4BXHToken | — | 4.3 GB | ~221 | same size |
| NVIDIA Nemotron Labs Dory1.0 4B BF16NVIDIA | — | 5.7 GB | ~214 | same size |
| Granite Vision 4.1 4BIBM | — | 4 GB | ~228 | same size |
| Translategemma 4B ITGoogle | — | 5 GB | ~231 | same size |
| Qwen3 VL 4B InstructAlibaba | — | 4.5 GB | ~208 | same size |
| MiniCPM V 4OpenBMB | — | 3.1 GB | ~236 | same size |
Fine-tunes & variants(1)
Specifications
- 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
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)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 51.8 GB (at full 131K context)
VRAM usage by quantization
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
★ Recommended — best balance of quality and VRAM usage.
Can I run Phi 3.5 Vision Instruct on a Mac?
Yes — Mac Mini M2 (8 GB) and 145 other Macs can run Phi 3.5 Vision Instruct. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 3.6 GB of usable unified memory (RAM minus macOS overhead).
Can I run Phi 3.5 Vision Instruct locally?
Yes — Phi 3.5 Vision Instruct can run locally on consumer hardware. 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 ÷ 3.5900 × 0.65 = ~1448 tok/s
Estimated speed at Q4_K_M (3.6 GB)
Real-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. 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?
163 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, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (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.