Qwen2.5-32B-Instruct

Qwen2.5-32B features 32 billion parameters, 64 layers, and a 40/8 attention head architecture, representing a significant leap in computational power and model capabilities. With support for a 128K-token context window and 8K-token generation capacity, the model can handle exceptionally complex and large-scale tasks.

Qwen2.5-32B reintroduces the 32B parameter size to the Qwen series after its absence in Qwen2, offering users a powerful alternative to the flagship 72B model with lower resource requirements. Trained on 18 trillion high-quality tokens, the model demonstrates robust performance with large datasets, expert-level knowledge in specialized domains, superior abstract reasoning capabilities, and the ability to solve problems requiring deep contextual understanding and multi-step analysis.

Qwen2.5-32B is designed for organizations and research teams that need frontier-model capabilities without the full cost of the largest models. Ideal applications include scientific research, complex software development, high-quality content creation, expert support systems in medicine and law, and as a foundation for building highly specialized AI systems.


Announce Date: 17.09.2024
Parameters: 32B
Context: 33K
Layers: 64
Attention Type: Full Attention
Developer: Qwen
Transformers Version: 4.43.1
License: Apache 2.0

Public endpoint

Use our pre-built public endpoints for free to test inference and explore Qwen2.5-32B-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 Qwen2.5-32B-Instruct

Prices:
Name GPU Price, hour TPS Max Concurrency
teslat4-3.32.64.160
32,768.0
pipeline
3 $0.88 1.479 Launch
teslaa10-2.16.64.160
32,768.0
tensor
2 $0.93 2.113 Launch
teslat4-4.16.64.160
32,768.0
tensor
4 $0.96 2.811 Launch
teslaa2-3.32.128.160
32,768.0
pipeline
3 $1.06 1.493 Launch
rtxa5000-2.16.64.160.nvlink
32,768.0
tensor
2 $1.23 2.113 Launch
teslaa2-4.32.128.160
32,768.0
tensor
4 $1.26 2.829 Launch
rtx3090-2.16.64.160
32,768.0
tensor
2 $1.56 2.357 Launch
rtx4090-2.16.64.160
32,768.0
tensor
2 $1.92 2.348 Launch
teslaa100-1.16.64.160
32,768.0
1 $2.37 6.464 Launch
rtx5090-2.16.64.160
32,768.0
tensor
2 $2.93 4.149 Launch
h100-1.16.64.160
32,768.0
1 $3.83 6.456 Launch
h100nvl-1.16.96.160
32,768.0
1 $4.11 8.056 Launch
teslaa100-2.24.96.160.nvlink
32,768.0
tensor
2 $4.61 15.166 Launch
h200-1.16.128.160
32,768.0
1 $4.74 13.428 Launch
h200-2.24.256.160.nvlink
32,768.0
tensor
2 $9.40 29.092 Launch
Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa10-3.16.96.160
32,768.0
pipeline
3 $1.34 2.315 Launch
teslaa10-4.12.48.160
32,768.0
tensor
4 $1.57 4.617 Launch
teslaa2-6.32.128.160
32,768.0
pipeline
6 $1.65 3.389 Launch
rtx3090-3.16.96.160
32,768.0
pipeline
3 $2.29 2.681 Launch
rtxa5000-4.16.128.160.nvlink
32,768.0
tensor
4 $2.34 4.617 Launch
teslaa100-1.16.64.160
32,768.0
1 $2.37 38.260 4.619 Launch
rtx4090-3.16.96.160
32,768.0
pipeline
3 $2.83 2.667 Launch
rtx3090-4.16.64.160
32,768.0
tensor
4 $2.89 5.105 Launch
rtx5090-2.16.64.160
32,768.0
tensor
2 $2.93 2.303 Launch
rtx4090-4.16.64.160
32,768.0
tensor
4 $3.60 5.087 Launch
h100-1.16.64.160
32,768.0
1 $3.83 37.000 4.611 Launch
h100nvl-1.16.96.160
32,768.0
1 $4.11 58.780 6.210 Launch
teslaa100-2.24.96.160.nvlink
32,768.0
tensor
2 $4.61 13.320 Launch
h200-1.16.128.160
32,768.0
1 $4.74 11.582 Launch
h200-2.24.256.160.nvlink
32,768.0
tensor
2 $9.40 27.246 Launch
Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa10-4.16.128.240
32,768.0
tensor
4 $1.76 1.072 Launch
teslaa100-1.16.128.240
32,768.0
1 $2.51 1.073 Launch
rtx3090-4.16.96.320
32,768.0
tensor
4 $2.97 1.559 Launch
rtx4090-4.16.96.320
32,768.0
tensor
4 $3.68 1.541 Launch
h100-1.16.128.240
32,768.0
1 $3.96 1.065 Launch
h100nvl-1.16.96.240
32,768.0
1 $4.12 2.664 Launch
rtx5090-3.16.96.240
32,768.0
pipeline
3 $4.35 1.711 Launch
h200-1.16.128.240
32,768.0
1 $4.74 8.036 Launch
teslaa100-2.24.256.320.nvlink
32,768.0
tensor
2 $4.94 9.774 Launch
rtx5090-4.16.128.320
32,768.0
tensor
4 $5.76 5.143 Launch
h200-2.24.256.240.nvlink
32,768.0
tensor
2 $9.41 23.701 Launch

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