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
teslat4-1.16.16.160
32,768.0
1 $0.33 7.262 Launch
rtx2080ti-1.10.16.500
32,768.0
1 $0.38 3.991 Launch
teslaa2-1.16.32.160
32,768.0
1 $0.38 7.294 Launch
teslaa10-1.16.32.160
32,768.0
1 $0.53 13.755 Launch
rtx3080-1.16.32.160
32,768.0
1 $0.57 3.214 Launch
rtx3090-1.16.24.160
32,768.0
1 $0.83 14.622 Launch
rtx4090-1.16.32.160
32,768.0
1 $1.02 14.589 Launch
rtxa5000-2.16.64.160.nvlink
32,768.0
tensor
2 $1.23 29.222 Launch
rtx5090-1.16.64.160
32,768.0
1 $1.59 20.992 Launch
teslaa100-1.16.64.160
32,768.0
1 $2.37 60.164 Launch
h100-1.16.64.160
32,768.0
1 $3.83 60.107 Launch
h100nvl-1.16.96.160
32,768.0
1 $4.11 71.482 Launch
teslaa100-2.24.96.160.nvlink
32,768.0
tensor
2 $4.61 122.039 Launch
h200-1.16.128.160
32,768.0
1 $4.74 109.680 Launch
h200-2.24.256.160.nvlink
32,768.0
tensor
2 $9.40 221.072 Launch
Prices:
Name GPU Price, hour TPS Max Concurrency
teslat4-1.16.16.160
32,768.0
1 $0.33 6.104 Launch
rtx2080ti-1.10.16.500
32,768.0
1 $0.38 2.833 Launch
teslaa2-1.16.32.160
32,768.0
1 $0.38 42.540 6.137 Launch
teslaa10-1.16.32.160
32,768.0
1 $0.53 93.330 12.597 Launch
rtx3080-1.16.32.160
32,768.0
1 $0.57 2.056 Launch
rtx3090-1.16.24.160
32,768.0
1 $0.83 13.464 Launch
rtx4090-1.16.32.160
32,768.0
1 $1.02 13.432 Launch
rtxa5000-2.16.64.160.nvlink
32,768.0
tensor
2 $1.23 28.064 Launch
rtx5090-1.16.64.160
32,768.0
1 $1.59 19.835 Launch
teslaa100-1.16.64.160
32,768.0
1 $2.37 156.450 59.006 Launch
h100-1.16.64.160
32,768.0
1 $3.83 137.720 58.949 Launch
h100nvl-1.16.96.160
32,768.0
1 $4.11 187.370 70.324 Launch
teslaa100-2.24.96.160.nvlink
32,768.0
tensor
2 $4.61 120.882 Launch
h200-1.16.128.160
32,768.0
1 $4.74 108.523 Launch
h200-2.24.256.160.nvlink
32,768.0
tensor
2 $9.40 219.915 Launch
Prices:
Name GPU Price, hour TPS Max Concurrency
teslat4-1.16.16.160
32,768.0
1 $0.33 3.864 Launch
teslaa2-1.16.32.160
32,768.0
1 $0.38 3.897 Launch
teslaa10-1.16.32.160
32,768.0
1 $0.53 10.357 Launch
rtx2080ti-2.12.64.160
32,768.0
tensor
2 $0.69 6.295 Launch
rtx3090-1.16.24.160
32,768.0
1 $0.83 11.224 Launch
rtx3080-2.16.32.160
32,768.0
tensor
2 $0.97 4.742 Launch
rtx4090-1.16.32.160
32,768.0
1 $1.02 11.191 Launch
rtxa5000-2.16.64.160.nvlink
32,768.0
tensor
2 $1.23 25.824 Launch
rtx5090-1.16.64.160
32,768.0
1 $1.59 17.595 Launch
teslaa100-1.16.64.160
32,768.0
1 $2.37 56.766 Launch
h100-1.16.64.160
32,768.0
1 $3.83 56.709 Launch
h100nvl-1.16.96.160
32,768.0
1 $4.11 68.084 Launch
teslaa100-2.24.96.160.nvlink
32,768.0
tensor
2 $4.61 118.642 Launch
h200-1.16.128.160
32,768.0
1 $4.74 106.283 Launch
h200-2.24.256.160.nvlink
32,768.0
tensor
2 $9.40 217.675 Launch

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