Phi-4-multimodal-instruct

multimodal

Phi-4-multimodal-instruct is an open-source multimodal model from Microsoft that processes text, images, and audio in a unified architectural solution. It builds upon the Phi-3.5 and Phi-4.0 technologies, featuring an extended context window of 128K tokens and support for 23 languages in text (including Russian), 8 languages in audio, and English for visual tasks. The model is optimized for environments with limited computational resources and low-latency scenarios, demonstrating strong performance in mathematics, logic, speech recognition, translation, and image analysis.  

A single neural network handles text, images (OCR, tables, diagrams), and audio (recognition, translation, summarization). For example, in DocVQA benchmarks, the model achieves 93.2% accuracy, outperforming Gemini-2.0-Flash (92.1%).  

The model is ideal for multisensory applications—joint processing of audio and images (e.g., video analysis with subtitles). At the same time, thanks to optimization via Microsoft Olive and ONNX GenAI Runtime, it can be deployed on edge devices, including smartphones and IoT systems, even with limited computational resources.


Announce Date: 27.02.2025
Parameters: 6B
Context: 132K
Layers: 32
Attention Type: Full Attention
Developer: Microsoft
Transformers Version: 4.46.1
License: MIT

Public endpoint

Use our pre-built public endpoints for free to test inference and explore Phi-4-multimodal-instruct capabilities. You can obtain an API access token on the token management page after registration and verification.
Model Name Context Type GPU Status Link

API access to Phi-4-multimodal-instruct endpoints

There are no public endpoints for this model yet.

Private endpoint

An endpoint is a server with pre-installed software that automatically downloads neural network weights and launches an OpenAI-compatible API to handle incoming requests.

A private endpoint is created within the user's project, belongs exclusively to the user, and is billed per server configuration in accordance with the infrastructure pricing.

We recommend creating private endpoints when you need to:

  • achieve maximum endpoint performance,
  • access the full context window for processing large requests and conversations,
  • ensure maximum security by processing data in a dedicated, isolated environment.

Recommended server configurations for creating a private inference endpoint Phi-4-multimodal-instruct

Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa10-2.16.64.160
131,072.0
tensor
2 $0.93 1.414 Launch
teslaa2-3.32.128.160
131,072.0
pipeline
3 $1.06 1.075 Launch
teslaa2-4.32.128.160
131,072.0
tensor
4 $1.26 1.757 Launch
rtx3090-2.16.64.160
131,072.0
tensor
2 $1.56 1.536 Launch
rtx3090-2.16.64.160.nvlink
131,072.0
tensor
2 $1.56 1.536 Launch
rtx4090-2.16.64.160
131,072.0
tensor
2 $1.92 1.532 Launch
teslaa100-1.16.64.160
131,072.0
1 $2.37 3.597 Launch
rtx5090-2.16.64.160
131,072.0
tensor
2 $2.93 2.432 Launch
h100-1.16.64.160
131,072.0
1 $3.83 3.593 Launch
h100nvl-1.16.96.160
131,072.0
1 $4.11 4.393 Launch
teslaa100-2.24.96.160.nvlink
131,072.0
tensor
2 $4.61 7.941 Launch
h200-1.16.128.160
131,072.0
1 $4.74 7.079 Launch
h200-2.24.256.160.nvlink
131,072.0
tensor
2 $9.40 14.904 Launch

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