Qwen2.5-3B-Instruct

Qwen2.5-3B features 3 billion parameters, 36 layers, and a 16/2 attention head architecture, delivering a significant performance leap while maintaining reasonable resource requirements. The model supports a 32K-token context window and up to 8K-token generation, enabling it to handle moderately complex tasks with extended contexts.

The uniqueness of Qwen2.5-3B lies in its return to the product line after being absent from the Qwen2 series, effectively filling the crucial gap between 1.5B and 7B models. This size proves particularly valuable for resource-constrained scenarios where the 7B version might be excessive, yet higher performance than the 1.5B variant is required. The model demonstrates substantially improved capabilities in understanding complex instructions, multi-step reasoning, and working with structured data.

Notably, this model is distributed under the Qwen Research License, which may impose certain restrictions on commercial use. However, Qwen2.5-3B is ideally suited for research projects, prototyping, and developing specialized solutions where licensing flexibility for research purposes is essential. The model performs exceptionally well in data analysis tasks, technical documentation processing, educational applications, and serves as an excellent base for creating domain-specific models through fine-tuning.


Announce Date: 17.09.2024
Parameters: 4B
Context: 33K
Layers: 36
Attention Type: Full Attention
Developer: Qwen
Transformers Version: 4.43.1
License: qwen

Public endpoint

Use our pre-built public endpoints for free to test inference and explore Qwen2.5-3B-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 Qwen2.5-3B-Instruct

Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa2-1.16.32.160
32,768.0
1 $0.38 50.00 7.188 Launch
teslaa10-1.16.32.160
32,768.0
1 $0.53 130.44 13.648 Launch
rtx3080-1.16.32.160
32,768.0
1 $0.57 3.107 Launch
rtx3090-1.16.24.160
32,768.0
1 $0.83 308.00 14.515 Launch
rtx4090-1.16.32.160
32,768.0
1 $1.02 306.00 14.482 Launch
rtx3090-2.16.64.160.nvlink
32,768.0
tensor
2 $1.56 30.742 Launch
rtx5090-1.16.64.160
32,768.0
1 $1.59 20.886 Launch
teslaa100-1.16.64.160
32,768.0
1 $2.37 60.057 Launch
h100-1.16.64.160
32,768.0
1 $3.83 306.00 60.000 Launch
h100nvl-1.16.96.160
32,768.0
1 $4.11 306.00 71.375 Launch
teslaa100-2.24.96.160.nvlink
32,768.0
tensor
2 $4.61 121.826 Launch
h200-1.16.128.160
32,768.0
1 $4.74 109.574 Launch
h200-2.24.256.160.nvlink
32,768.0
tensor
2 $9.40 220.859 Launch
Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa2-1.16.32.160
32,768.0
1 $0.38 42.54 7.735 Launch
teslaa10-1.16.32.160
32,768.0
1 $0.53 93.33 14.169 Launch
rtx3080-1.16.32.160
32,768.0
1 $0.57 132.96 3.646 Launch
rtx3090-1.16.24.160
32,768.0
1 $0.83 143.16 15.027 Launch
rtx4090-1.16.32.160
32,768.0
1 $1.02 140.05 15.190 Launch
rtx3090-2.16.64.160.nvlink
32,768.0
tensor
2 $1.56 33.014 Launch
rtx5090-1.16.64.160
32,768.0
1 $1.59 21.424 Launch
teslaa100-1.16.64.160
32,768.0
1 $2.37 156.45 60.542 Launch
h100-1.16.64.160
32,768.0
1 $3.83 137.72 60.343 Launch
h100nvl-1.16.96.160
32,768.0
1 $4.11 187.37 71.691 Launch
teslaa100-2.24.96.160.nvlink
32,768.0
tensor
2 $4.61 124.098 Launch
h200-1.16.128.160
32,768.0
1 $4.74 110.112 Launch
h200-2.24.256.160.nvlink
32,768.0
tensor
2 $9.40 223.131 Launch
Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa2-1.16.32.160
32,768.0
1 $0.38 21.60 5.495 Launch
teslaa10-1.16.32.160
32,768.0
1 $0.53 59.19 11.956 Launch
rtx3080-1.16.32.160
32,768.0
1 $0.57 1.414 Launch
rtx3090-1.16.24.160
32,768.0
1 $0.83 12.822 Launch
rtx4090-1.16.32.160
32,768.0
1 $1.02 97.47 12.790 Launch
rtx3090-2.16.64.160.nvlink
32,768.0
tensor
2 $1.56 30.791 Launch
rtx5090-1.16.64.160
32,768.0
1 $1.59 19.193 Launch
teslaa100-1.16.64.160
32,768.0
1 $2.37 122.20 58.364 Launch
h100-1.16.64.160
32,768.0
1 $3.83 58.307 Launch
h100nvl-1.16.96.160
32,768.0
1 $4.11 69.682 Launch
teslaa100-2.24.96.160.nvlink
32,768.0
tensor
2 $4.61 121.876 Launch
h200-1.16.128.160
32,768.0
1 $4.74 107.881 Launch
h200-2.24.256.160.nvlink
32,768.0
tensor
2 $9.40 220.908 Launch

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