Qwen3-32B

reasoning

Qwen3-32B is the most powerful dense model in the series, featuring 32 billion parameters, a 64-layer architecture, and 64 attention heads, with support for a context window of 40K tokens. This model represents the pinnacle of dense architecture within the Qwen3 lineup, delivering performance comparable to leading proprietary solutions across most tasks. Developers emphasize that thanks to architectural innovations and training on 36 trillion tokens of high-quality data, Qwen3-32B achieves quality on par with Qwen2.5-72B, but with twice as few parameters.

The model demonstrates outstanding results across all benchmarks, particularly excelling in programming, mathematical problem-solving, and knowledge-intensive domains in science and engineering. Qwen3-32B is capable of handling tasks at the level of senior-level experts and delivers quality suitable for mission-critical commercial applications. Full support for all 119 languages at the highest quality makes this model a universal solution for applications requiring international reach.

This model is designed for flagship products from major technology companies, national research initiatives, mission-critical AI systems, and any application where quality is the top priority. Qwen3-32B is ideal for building premium-tier AI assistants, advanced analytical systems, professional-grade development tools, and any use cases demanding the highest level of natural language processing quality.


Announce Date: 29.04.2025
Parameters: 33B
Context: 41K
Layers: 64
Attention Type: Full or Sliding Window Attention
Developer: Qwen
Transformers Version: 4.51.0
License: Apache 2.0

Public endpoint

Use our pre-built public endpoints for free to test inference and explore Qwen3-32B 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 Qwen3-32B 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 Qwen3-32B

Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa10-2.16.64.160
40,960.0
tensor
2 $0.93 38.57 1.876 Launch
teslaa2-3.32.128.160
40,960.0
pipeline
3 $1.06 8.92 1.525 Launch
teslaa2-4.32.128.160
40,960.0
tensor
4 $1.26 2.780 Launch
rtx3090-2.16.64.160
40,960.0
tensor
2 $1.56 102.00 2.191 Launch
rtx3090-2.16.64.160.nvlink
40,960.0
tensor
2 $1.56 2.191 Launch
rtx5090-1.16.64.160
20,000.0
1 $1.59 1.788 Launch
rtx3080-4.16.64.160
20,000.0
tensor
4 $1.82 1.932 Launch
rtx4090-2.16.64.160
40,960.0
tensor
2 $1.92 58.92 2.162 Launch
teslaa100-1.16.64.160
40,960.0
1 $2.37 54.87 5.246 Launch
rtx5090-2.16.64.160
40,960.0
tensor
2 $2.93 74.38 3.668 Launch
h100-1.16.64.160
40,960.0
1 $3.83 60.72 5.168 Launch
h100nvl-1.16.96.160
40,960.0
1 $4.11 6.553 Launch
teslaa100-2.24.96.160.nvlink
40,960.0
tensor
2 $4.61 12.377 Launch
h200-1.16.128.160
40,960.0
1 $4.74 10.851 Launch
h200-2.24.256.160.nvlink
40,960.0
tensor
2 $9.40 23.518 Launch
Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa10-2.16.64.160
20,000.0
tensor
2 $0.93 1.146 Launch
teslaa2-4.32.128.160
40,960.0
tensor
4 $1.26 1.461 Launch
teslaa10-3.16.96.160
40,960.0
pipeline
3 $1.34 2.291 Launch
rtx3090-2.16.64.160
20,000.0
tensor
2 $1.56 1.545 Launch
rtx3090-2.16.64.160.nvlink
20,000.0
tensor
2 $1.56 1.545 Launch
teslaa10-4.12.48.160
40,960.0
tensor
4 $1.57 4.369 Launch
rtx4090-2.16.64.160
20,000.0
tensor
2 $1.92 1.530 Launch
rtx3090-3.16.96.160
40,960.0
pipeline
3 $2.29 24.26 2.575 Launch
teslaa100-1.16.64.160
40,960.0
1 $2.37 3.876 Launch
rtx4090-3.16.96.160
40,960.0
pipeline
3 $2.83 16.89 2.564 Launch
rtx3090-4.16.64.160
40,960.0
tensor
4 $2.89 4.759 Launch
rtx5090-2.16.64.160
40,960.0
tensor
2 $2.93 2.188 Launch
rtx3090-4.16.128.160.nvlink
40,960.0
tensor
4 $3.01 4.759 Launch
rtx4090-4.16.64.160
40,960.0
tensor
4 $3.60 4.744 Launch
h100-1.16.64.160
40,960.0
1 $3.83 3.869 Launch
h100nvl-1.16.96.160
40,960.0
1 $4.11 5.149 Launch
teslaa100-2.24.96.160.nvlink
40,960.0
tensor
2 $4.61 11.001 Launch
h200-1.16.128.160
40,960.0
1 $4.74 9.446 Launch
h200-2.24.256.160.nvlink
40,960.0
tensor
2 $9.40 22.143 Launch
Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa2-6.32.128.240
20,000.0
pipeline
6 $1.66 1.290 Launch
teslaa10-4.16.128.240
40,960.0
tensor
4 $1.76 1.404 Launch
teslaa100-1.16.128.240
20,000.0
1 $2.51 1.894 Launch
rtx3090-4.16.96.320
40,960.0
tensor
4 $2.97 38.31 1.794 Launch
rtx4090-4.16.96.240
40,960.0
tensor
4 $3.67 44.91 1.779 Launch
h100-1.16.128.240
20,000.0
1 $3.96 1.881 Launch
h100nvl-1.16.96.240
40,960.0
1 $4.12 2.198 Launch
rtx5090-3.16.96.240
40,960.0
pipeline
3 $4.35 1.700 Launch
h200-1.16.128.240
40,960.0
1 $4.74 6.496 Launch
teslaa100-2.24.256.240
40,960.0
tensor
2 $4.93 39.97 8.046 Launch
teslaa100-2.24.256.320.nvlink
40,960.0
tensor
2 $4.94 8.046 Launch
rtx5090-4.16.128.320
40,960.0
tensor
4 $5.76 4.661 Launch
h100-2.24.256.240
40,960.0
tensor
2 $7.85 48.05 8.033 Launch
h200-2.24.256.240.nvlink
40,960.0
tensor
2 $9.41 19.187 Launch

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