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
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 Qwen3-32B

Prices:
Name GPU Price, hour TPS Max Concurrency
teslat4-3.32.64.160
40,960.0
pipeline
3 $0.88 1.412 Launch
teslaa10-2.16.64.160
40,960.0
tensor
2 $0.93 38.570 1.840 Launch
teslat4-4.16.64.160
40,960.0
tensor
4 $0.96 2.558 Launch
teslaa2-3.32.128.160
40,960.0
pipeline
3 $1.06 8.920 1.424 Launch
rtx2080ti-4.16.32.160
40,960.0
tensor
4 $1.12 1.086 Launch
rtxa5000-2.16.64.160.nvlink
40,960.0
tensor
2 $1.23 1.840 Launch
teslaa2-4.32.128.160
40,960.0
tensor
4 $1.26 2.573 Launch
rtx3090-2.16.64.160
40,960.0
tensor
2 $1.56 49.810 2.035 Launch
rtx5090-1.16.64.160
20,000.0
1 $1.59 1.709 Launch
rtx3080-4.16.64.160
20,000.0
tensor
4 $1.82 1.509 Launch
rtx4090-2.16.64.160
40,960.0
tensor
2 $1.92 2.028 Launch
teslaa100-1.16.64.160
40,960.0
1 $2.37 54.180 5.241 Launch
rtx5090-2.16.64.160
40,960.0
tensor
2 $2.93 3.469 Launch
h100-1.16.64.160
40,960.0
1 $3.83 60.720 5.235 Launch
h100nvl-1.16.96.160
40,960.0
1 $4.11 6.514 Launch
teslaa100-2.24.96.160.nvlink
40,960.0
tensor
2 $4.61 12.282 Launch
h200-1.16.128.160
40,960.0
1 $4.74 10.812 Launch
h200-2.24.256.160.nvlink
40,960.0
tensor
2 $9.40 23.423 Launch
Prices:
Name GPU Price, hour TPS Max Concurrency
teslat4-4.16.64.160
40,960.0
tensor
4 $0.96 1.162 Launch
teslaa2-4.32.128.160
40,960.0
tensor
4 $1.26 1.176 Launch
teslaa10-3.16.96.160
40,960.0
pipeline
3 $1.34 2.164 Launch
rtx3090-2.16.64.160
20,000.0
tensor
2 $1.56 1.308 Launch
teslaa10-4.12.48.160
40,960.0
tensor
4 $1.57 4.083 Launch
rtx4090-2.16.64.160
20,000.0
tensor
2 $1.92 1.293 Launch
rtx3090-3.16.96.160
40,960.0
pipeline
3 $2.29 2.456 Launch
rtxa5000-4.16.128.160.nvlink
40,960.0
tensor
4 $2.34 4.083 Launch
teslaa100-1.16.64.160
40,960.0
1 $2.37 3.844 Launch
rtx4090-3.16.96.160
40,960.0
pipeline
3 $2.83 2.445 Launch
rtx3090-4.16.64.160
40,960.0
tensor
4 $2.89 4.474 Launch
rtx5090-2.16.64.160
40,960.0
tensor
2 $2.93 2.072 Launch
rtx4090-4.16.64.160
40,960.0
tensor
4 $3.60 4.459 Launch
h100-1.16.64.160
40,960.0
1 $3.83 3.838 Launch
h100nvl-1.16.96.160
40,960.0
1 $4.11 5.118 Launch
teslaa100-2.24.96.160.nvlink
40,960.0
tensor
2 $4.61 10.885 Launch
h200-1.16.128.160
40,960.0
1 $4.74 9.415 Launch
h200-2.24.256.160.nvlink
40,960.0
tensor
2 $9.40 22.027 Launch
Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa10-4.16.128.240
40,960.0
tensor
4 $1.76 1.178 Launch
teslaa100-1.16.128.240
20,000.0
1 $2.51 1.922 Launch
rtx3090-4.16.96.320
40,960.0
tensor
4 $2.97 1.568 Launch
rtx4090-4.16.96.320
40,960.0
tensor
4 $3.68 1.553 Launch
h100-1.16.128.240
20,000.0
1 $3.96 1.909 Launch
h100nvl-1.16.96.240
40,960.0
1 $4.12 2.212 Launch
rtx5090-3.16.96.240
40,960.0
pipeline
3 $4.35 1.609 Launch
h200-1.16.128.240
40,960.0
1 $4.74 6.509 Launch
teslaa100-2.24.256.240
40,960.0
tensor
2 $4.93 7.980 Launch
teslaa100-2.24.256.320.nvlink
40,960.0
tensor
2 $4.94 7.980 Launch
rtx5090-4.16.128.320
40,960.0
tensor
4 $5.76 4.434 Launch
h100-2.24.256.240
40,960.0
tensor
2 $7.85 7.967 Launch
h200-2.24.256.240.nvlink
40,960.0
tensor
2 $9.41 19.121 Launch

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