Phi-4

Phi-4 is a modern open-source language model with 14 billion parameters. Although the architectural changes compared to previous versions are minimal, the model demonstrates significant progress in tasks requiring logical thinking and analytical skills, made possible by an innovative training approach. Unlike traditional language model training methods, Phi-4 focuses not on the quantity of data but on its quality. The training of Phi-4 utilized diverse sources, including synthetic data specifically designed to develop reasoning skills, filtered documents from public sources, as well as acquired academic books and question-answer knowledge bases. This allows the model to achieve high performance even with a relatively small size.  

Phi-4 works exclusively with textual data. Its context window is relatively small at 16K tokens, but it supports more than 50 languages, including Russian.  

Overall, Phi-4 is a versatile lightweight model, but according to its developers, it is particularly effective in environments with limited memory and computational resources, as well as for tasks requiring instant response.


Announce Date: 12.12.2024
Parameters: 15B
Context: 17K
Layers: 40
Attention Type: Full Attention
Developer: Microsoft
Transformers Version: 4.47.0
License: MIT

Public endpoint

Use our pre-built public endpoints for free to test inference and explore Phi-4 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 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

Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa10-1.16.32.160
16,384.0
1 $0.53 32.82 2.752 Launch
teslaa2-2.16.32.160
16,384.0
tensor
2 $0.57 9.15 4.456 Launch
rtx3090-1.16.24.160
16,384.0
1 $0.83 50.09 3.064 Launch
rtx4090-1.16.32.160
16,384.0
1 $1.02 69.85 3.052 Launch
rtx3080-3.16.64.160
16,384.0
pipeline
3 $1.43 2.627 Launch
rtx3090-2.16.64.160.nvlink
16,384.0
tensor
2 $1.56 9.066 Launch
rtx5090-1.16.64.160
16,384.0
1 $1.59 83.03 5.361 Launch
teslaa100-1.16.64.160
16,384.0
1 $2.37 53.63 19.459 Launch
h100-1.16.64.160
16,384.0
1 $3.83 63.45 19.272 Launch
h100nvl-1.16.96.160
16,384.0
1 $4.11 91.22 23.358 Launch
teslaa100-2.24.96.160.nvlink
16,384.0
tensor
2 $4.61 41.856 Launch
h200-1.16.128.160
16,384.0
1 $4.74 37.109 Launch
h200-2.24.256.160.nvlink
16,384.0
tensor
2 $9.40 77.508 Launch
Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa10-1.16.32.160
16,384.0
1 $0.53 34.30 1.350 Launch
teslaa2-2.16.32.160
16,384.0
tensor
2 $0.57 22.23 3.131 Launch
rtx3090-1.16.24.160
16,384.0
1 $0.83 50.17 1.854 Launch
rtx4090-1.16.32.160
16,384.0
1 $1.02 43.48 1.574 Launch
rtx3080-3.16.64.160
16,384.0
pipeline
3 $1.43 43.33 2.527 Launch
rtx3090-2.16.64.160.nvlink
16,384.0
tensor
2 $1.56 8.407 Launch
rtx5090-1.16.64.160
16,384.0
1 $1.59 4.148 Launch
teslaa100-1.16.64.160
16,384.0
1 $2.37 80.23 18.042 Launch
h100-1.16.64.160
16,384.0
1 $3.83 96.69 17.941 Launch
h100nvl-1.16.96.160
16,384.0
1 $4.11 143.66 22.033 Launch
teslaa100-2.24.96.160.nvlink
16,384.0
tensor
2 $4.61 41.197 Launch
h200-1.16.128.160
16,384.0
1 $4.74 36.076 Launch
h200-2.24.256.160.nvlink
16,384.0
tensor
2 $9.40 76.849 Launch
Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa10-2.16.64.160
16,384.0
tensor
2 $0.93 29.00 3.290 Launch
teslaa2-3.32.128.160
16,384.0
pipeline
3 $1.06 5.77 2.053 Launch
rtx3090-2.16.64.160
16,384.0
tensor
2 $1.56 46.56 4.138 Launch
rtx3090-2.16.64.160.nvlink
16,384.0
tensor
2 $1.56 4.138 Launch
rtx4090-2.16.64.160
16,384.0
tensor
2 $1.92 55.00 4.057 Launch
teslaa100-1.16.64.160
16,384.0
1 $2.37 46.21 13.984 Launch
rtx5090-2.16.64.160
16,384.0
tensor
2 $2.93 73.39 8.468 Launch
h100-1.16.64.160
16,384.0
1 $3.83 53.09 13.797 Launch
h100nvl-1.16.96.160
16,384.0
1 $4.11 87.21 17.137 Launch
teslaa100-2.24.96.160.nvlink
16,384.0
tensor
2 $4.61 36.868 Launch
h200-1.16.128.160
16,384.0
1 $4.74 31.876 Launch
h200-2.24.256.160.nvlink
16,384.0
tensor
2 $9.40 72.520 Launch

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