Phi-4 Multimodal 5.6B
By Microsoft · United States
Updated 2026-07-13
Overview
Microsoft's 5.6B multimodal model — text, image, and audio in, text out — using a Mixture-of-LoRAs design. Accepts roughly 2.8 hours of audio per request.
When to pick this model
- You're processing long audio recordings on a laptop or edge device
- You need lightweight multimodal in an English-first context
- You want an MIT-licensed multimodal model with no commercial restrictions
- You're prototyping voice + vision pipelines without server-class hardware
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 4 GB |
| Q5_K_M | 5 GB |
| Q8_0 | 7 GB |
| FP16 (no quantization) | 12 GB |
VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.
In practice, Phi-4 Multimodal 5.6B fits an 8 GB consumer card at Q4_K_M (4 GB). Stepping up to Q8_0 nearly doubles the footprint to 7 GB, and unquantized FP16 weights take 12 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, Phi-4 Multimodal 5.6B needs roughly 8 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 15 tokens/sec on entry-level GPUs, on the order of 45 tokens/sec on a mid-range card, and up to 110 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches Phi-4 Multimodal 5.6B to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.
| GPU memory | Example cards | Best fit for Phi-4 Multimodal 5.6B |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Q8_0 (7 GB used) |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | FP16 (12 GB used) |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | FP16 (12 GB used) |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | FP16 (12 GB used) |
| 32 GB | RTX 5090 | FP16 (12 GB used) |
Which GPU should you buy to run Phi-4 Multimodal 5.6B?
To run Phi-4 Multimodal 5.6B locally at Q4, you need ~4 GB of VRAM. The best value for this is a RTX 5060 (8 GB VRAM).
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Strengths
- Text, image, and audio input in a 5.6B footprint
- MIT license
- 128K context window
- Long audio handling (up to ~2.8 hours)
Limitations
- No official Ollama tag
- English-first — weaker on other languages
- Limited ecosystem tooling vs Qwen VL
Typical workloads
In our catalog grid, Phi-4 Multimodal 5.6B is filed under Laptop Multimodal, Transcription — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: vision-language work — screenshots, charts, scanned documents; audio understanding.
The 125k-token context window covers long chats and mid-sized documents, though very large retrieval workloads will need chunking. The MIT license is permissive, so shipping it inside a commercial product raises no special legal questions.
Architecture & training
Architecture: Dense · Mixture-of-LoRAs for multimodal · LongRoPE
Training: Up to ~2.8h of audio input.
The lightest credible audio-capable multimodal under MIT — ideal for transcription-adjacent pipelines on small hardware.
Quick start
# Via HuggingFace : microsoft/Phi-4-multimodal-instruct (pas d'Ollama officiel)Or use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
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Frequently asked questions
How much VRAM does Phi-4 Multimodal 5.6B need?
At the recommended Q4_K_M quantization, Phi-4 Multimodal 5.6B needs about 4 GB of VRAM. Q8_0 takes 7 GB, and unquantized FP16 weights take 12 GB.
Can Phi-4 Multimodal 5.6B run without a GPU?
Yes — with roughly 8 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.
What context window does Phi-4 Multimodal 5.6B support?
Phi-4 Multimodal 5.6B supports a 125k-token context window (128,000 tokens).
Can I use Phi-4 Multimodal 5.6B commercially?
Yes. Phi-4 Multimodal 5.6B is released under MIT, a permissive open-source license that allows commercial use, modification and redistribution.
How fast is Phi-4 Multimodal 5.6B on consumer hardware?
Our compatibility engine estimates on the order of 45 tokens/sec on a mid-range GPU and up to 110 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of Phi-4 Multimodal 5.6B should I download first?
Start with Q4_K_M (4 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. It already fits an 8 GB card at Q8_0.