Phi-3.5-mini-instruct

Phi-3.5-mini is the latest model from Microsoft’s Phi series of small language models, combining compactness with high performance. Built on an architecture with 3.8 billion parameters, it can run locally even on modern smartphones, making it one of the most accessible and efficient language models on the market. Thanks to its use of carefully curated and synthetic training data, Phi-3.5-mini delivers results comparable to much larger models such as GPT-3.5 and Mixtral 8x7B, while requiring significantly fewer computational resources.

The uniqueness of Phi-3.5-mini lies in its training approach: instead of simply increasing the model size, developers focused on the quality and relevance of the data. By using carefully filtered web sources and synthetic examples, the model achieves a “data optimal regime”—maximizing the effectiveness of each parameter. This enables Phi-3.5-mini to deliver outstanding performance in reasoning, mathematics, programming, and dialogue tasks, all while remaining compact and fast.

Phi-3.5-mini is particularly well-suited for edge devices, mobile applications, chatbots, educational platforms, and any scenarios where privacy and offline operation are important. The model is ideal for building multilingual assistants, text generation and analysis, solving mathematical and logical problems, and integration into products with limited computational resources.


Announce Date: 23.04.2024
Parameters: 4B
Context: 132K
Layers: 32
Attention Type: Full Attention
Developer: Microsoft
Transformers Version: 4.43.3
License: MIT

Public endpoint

Use our pre-built public endpoints for free to test inference and explore Phi-3.5-mini-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
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 server configurations for hosting Phi-3.5-mini-instruct

Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa10-1.16.32.160
44,000.0
1 $0.53 1.073 Launch
teslaa2-2.16.32.160
44,000.0
tensor
2 $0.57 1.391 Launch
rtx3090-1.16.24.160
44,000.0
1 $0.83 1.134 Launch
rtx4090-1.16.32.160
44,000.0
1 $1.02 1.131 Launch
rtx3090-2.16.64.160.nvlink
44,000.0
tensor
2 $1.56 2.415 Launch
rtx5090-1.16.64.160
44,000.0
1 $1.59 1.579 Launch
teslaa10-4.16.64.160
131,072.0
tensor
4 $1.62 1.589 Launch
rtx3080-4.16.64.160
44,000.0
tensor
4 $1.82 1.790 Launch
teslaa100-1.16.64.160
131,072.0
1 $2.37 149.60 1.448 Launch
rtx3090-4.16.64.160
131,072.0
tensor
4 $2.89 1.671 Launch
rtx5090-2.16.64.160
131,072.0
tensor
2 $2.93 1.109 Launch
rtx3090-4.16.128.160.nvlink
131,072.0
tensor
4 $3.01 1.671 Launch
rtx4090-4.16.64.160
131,072.0
tensor
4 $3.60 1.667 Launch
h100-1.16.64.160
131,072.0
1 $3.83 1.447 Launch
h100nvl-1.16.96.160
131,072.0
1 $4.11 1.713 Launch
teslaa100-2.24.96.160.nvlink
131,072.0
tensor
2 $4.61 2.945 Launch
h200-1.16.128.160
131,072.0
1 $4.74 2.609 Launch
h200-2.24.256.160.nvlink
131,072.0
tensor
2 $9.40 5.266 Launch
Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa2-2.16.32.160
44,000.0
tensor
2 $0.57 1.029 Launch
teslaa10-2.16.64.160
44,000.0
tensor
2 $0.93 1.931 Launch
rtx3090-2.16.64.160
44,000.0
tensor
2 $1.56 2.052 Launch
rtx3090-2.16.64.160.nvlink
44,000.0
tensor
2 $1.56 2.052 Launch
rtx5090-1.16.64.160
44,000.0
1 $1.59 1.249 Launch
teslaa10-4.16.64.160
131,072.0
tensor
4 $1.62 1.445 Launch
rtx3080-4.16.64.160
44,000.0
tensor
4 $1.82 1.362 Launch
rtx4090-2.16.64.160
44,000.0
tensor
2 $1.92 2.048 Launch
teslaa100-1.16.64.160
131,072.0
1 $2.37 148.86 1.338 Launch
rtx3090-4.16.64.160
131,072.0
tensor
4 $2.89 1.527 Launch
rtx3090-4.16.128.160.nvlink
131,072.0
tensor
4 $3.01 1.527 Launch
rtx4090-4.16.64.160
131,072.0
tensor
4 $3.60 1.524 Launch
h100-1.16.64.160
131,072.0
1 $3.83 176.94 1.336 Launch
h100nvl-1.16.96.160
131,072.0
1 $4.11 1.603 Launch
teslaa100-2.24.96.160.nvlink
131,072.0
tensor
2 $4.61 2.824 Launch
h200-1.16.128.160
131,072.0
1 $4.74 2.498 Launch
rtx5090-4.16.128.160
131,072.0
tensor
4 $5.74 2.124 Launch
h200-2.24.256.160.nvlink
131,072.0
tensor
2 $9.40 5.145 Launch

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