Phi-4-multimodal

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: 5.57B
Context: 131K
Attention Type: Full Attention
VRAM requirements: 18.6 GB using 4 bits quantization
Developer: Microsoft
Transformers Version: 4.46.1
License: MIT

Public endpoint

Use our pre-built public endpoints to test inference and explore Phi-4-multimodal capabilities.
Model Name Context Type GPU TPS Status Link
There are no public endpoints for this model yet.

Private server

Rent your own physically dedicated instance with hourly or long-term monthly billing.

We recommend deploying private instances in the following scenarios:

  • maximize endpoint performance,
  • enable full context for long sequences,
  • ensure top-tier security for data processing in an isolated, dedicated environment,
  • use custom weights, such as fine-tuned models or LoRA adapters.

Recommended configurations for hosting Phi-4-multimodal

Prices:
Name vCPU RAM, MB Disk, GB GPU Price, hour
teslaa10-1.16.32.160 16 32768 160 1 $0.53 Launch
teslaa2-2.16.32.160 16 32768 160 2 $0.57 Launch
rtx2080ti-2.12.64.160 12 65536 160 2 $0.69 Launch
teslat4-2.16.32.160 16 32768 160 2 $0.80 Launch
rtx3090-1.16.24.160 16 24576 160 1 $0.88 Launch
rtx4090-1.16.32.160 16 32768 160 1 $1.15 Launch
teslav100-1.12.64.160 12 65536 160 1 $1.20 Launch
rtx3080-3.16.64.160 16 65536 160 3 $1.43 Launch
rtx5090-1.16.64.160 16 65536 160 1 $1.59 Launch
teslaa100-1.16.64.160 16 65536 160 1 $2.58 Launch
teslah100-1.16.64.160 16 65536 160 1 $5.11 Launch
Prices:
Name vCPU RAM, MB Disk, GB GPU Price, hour
teslaa10-1.16.32.160 16 32768 160 1 $0.53 Launch
teslaa2-2.16.32.160 16 32768 160 2 $0.57 Launch
teslat4-2.16.32.160 16 32768 160 2 $0.80 Launch
rtx2080ti-3.12.24.120 12 24576 120 3 $0.84 Launch
rtx3090-1.16.24.160 16 24576 160 1 $0.88 Launch
rtx4090-1.16.32.160 16 32768 160 1 $1.15 Launch
teslav100-1.12.64.160 12 65536 160 1 $1.20 Launch
rtx3080-3.16.64.160 16 65536 160 3 $1.43 Launch
rtx5090-1.16.64.160 16 65536 160 1 $1.59 Launch
teslaa100-1.16.64.160 16 65536 160 1 $2.58 Launch
teslah100-1.16.64.160 16 65536 160 1 $5.11 Launch
Prices:
Name vCPU RAM, MB Disk, GB GPU Price, hour
teslaa2-2.16.32.160 16 32768 160 2 $0.57 Launch
teslat4-2.16.32.160 16 32768 160 2 $0.80 Launch
teslaa10-2.16.64.160 16 65536 160 2 $0.93 Launch
rtx2080ti-3.16.64.160 16 65536 160 3 $0.95 Launch
teslav100-1.12.64.160 12 65536 160 1 $1.20 Launch
rtx3080-3.16.64.160 16 65536 160 3 $1.43 Launch
rtx5090-1.16.64.160 16 65536 160 1 $1.59 Launch
rtx3090-2.16.64.160 16 65536 160 2 $1.67 Launch
rtx4090-2.16.64.160 16 65536 160 2 $2.19 Launch
teslaa100-1.16.64.160 16 65536 160 1 $2.58 Launch
teslah100-1.16.64.160 16 65536 160 1 $5.11 Launch

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