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

API access to Qwen2.5-32B-Instruct 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 Qwen2.5-32B-Instruct

Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa10-2.16.64.160
32,768.0
tensor
2 $0.93 2.508 Launch
teslaa2-3.32.128.160
32,768.0
pipeline
3 $1.06 2.037 Launch
teslaa2-4.32.128.160
32,768.0
tensor
4 $1.26 3.654 Launch
rtx3090-2.16.64.160
32,768.0
tensor
2 $1.56 61.17 2.751 Launch
rtx3090-2.16.64.160.nvlink
32,768.0
tensor
2 $1.56 2.751 Launch
rtx5090-1.16.64.160
32,768.0
1 $1.59 1.135 Launch
rtx3080-4.16.64.160
32,768.0
tensor
4 $1.82 1.358 Launch
rtx4090-2.16.64.160
32,768.0
tensor
2 $1.92 72.85 2.742 Launch
teslaa100-1.16.64.160
32,768.0
1 $2.37 59.41 6.644 Launch
h100-1.16.64.160
32,768.0
1 $3.83 59.02 6.636 Launch
h100nvl-1.16.96.160
32,768.0
1 $4.11 8.235 Launch
teslaa100-2.24.96.160.nvlink
32,768.0
tensor
2 $4.61 15.560 Launch
h200-1.16.128.160
32,768.0
1 $4.74 13.607 Launch
h200-2.24.256.160.nvlink
32,768.0
tensor
2 $9.40 29.487 Launch
Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa2-4.32.128.160
32,768.0
tensor
4 $1.26 17.88 1.766 Launch
teslaa10-3.16.96.160
32,768.0
pipeline
3 $1.34 2.812 Launch
teslaa10-4.12.48.160
32,768.0
tensor
4 $1.57 5.400 Launch
rtx3090-3.16.96.160
32,768.0
pipeline
3 $2.29 20.58 3.009 Launch
teslaa100-1.16.64.160
32,768.0
1 $2.37 39.51 4.709 Launch
rtx4090-3.16.96.160
32,768.0
pipeline
3 $2.83 24.10 2.989 Launch
rtx3090-4.16.64.160
32,768.0
tensor
4 $2.89 5.888 Launch
rtx5090-2.16.64.160
32,768.0
tensor
2 $2.93 2.691 Launch
rtx3090-4.16.128.160.nvlink
32,768.0
tensor
4 $3.01 5.888 Launch
rtx4090-4.16.64.160
32,768.0
tensor
4 $3.60 5.869 Launch
h100-1.16.64.160
32,768.0
1 $3.83 41.38 4.603 Launch
h100nvl-1.16.96.160
32,768.0
1 $4.11 58.78 6.202 Launch
teslaa100-2.24.96.160.nvlink
32,768.0
tensor
2 $4.61 13.708 Launch
h200-1.16.128.160
32,768.0
1 $4.74 11.772 Launch
h200-2.24.256.160.nvlink
32,768.0
tensor
2 $9.40 27.634 Launch
Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa10-4.16.128.240
32,768.0
tensor
4 $1.76 1.815 Launch
teslaa100-1.16.128.240
32,768.0
1 $2.51 22.26 1.209 Launch
rtx3090-4.16.96.320
32,768.0
tensor
4 $2.97 37.03 2.303 Launch
rtx4090-4.16.96.240
32,768.0
tensor
4 $3.67 2.284 Launch
h100-1.16.128.240
32,768.0
1 $3.96 1.201 Launch
h100nvl-1.16.96.240
32,768.0
1 $4.12 2.800 Launch
rtx5090-3.16.96.240
32,768.0
pipeline
3 $4.35 2.182 Launch
h200-1.16.128.240
32,768.0
1 $4.74 8.172 Launch
teslaa100-2.24.256.320.nvlink
32,768.0
tensor
2 $4.94 10.113 Launch
rtx5090-4.16.128.320
32,768.0
tensor
4 $5.76 5.886 Launch
h200-2.24.256.240.nvlink
32,768.0
tensor
2 $9.41 24.039 Launch

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