Qwen3-30B-A3B

reasoning

Qwen3-30B-A3B is the first Mixture-of-Experts (MoE) model in the series, featuring a total of 30 billion parameters, with only 3 billion activated per token. The model is built on an architecture with 48 layers, 32 attention heads, and supports a context window of 40K tokens.

The model employs an innovative Mixture of Experts (MoE) architecture with a total of 128 experts, out of which only 8 are activated per token. This allows achieving inference efficiency comparable to compact 3B-parameter models, while the generation quality approaches that of dense 30B-parameter models. The key advantage of the MoE approach is its optimal balance between high performance and energy efficiency. The system dynamically selects the most suitable experts for each specific task, delivering excellent processing quality at significantly lower computational costs compared to dense models of similar capability.

Qwen3-30B-A3B is well-suited for high-load systems requiring a balance between quality and performance: cloud API services, enterprise chatbots, content automation systems, multi-user AI platforms. The model is ideal for companies that need the quality of large models but operate under limited computational budgets.


Announce Date: 29.04.2025
Parameters: 31B
Experts: 128
Activated at inference: 4B
Context: 41K
Layers: 48
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 Qwen3-30B-A3B 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 Qwen3-30B-A3B

Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa2-1.16.32.240
40,960.0
1 $0.39 -1.280 Launch
teslat4-1.16.64.160
40,960.0
1 $0.42 -1.280 Launch
rtx2080ti-2.16.64.160
40,960.0
tensor
2 $0.71 -0.507 Launch
rtx3080-2.16.64.160
40,960.0
tensor
2 $1.03 -0.987 Launch
rtx4090-1.32.64.160
40,960.0
1 $1.18 0.640 Launch
rtxa5000-2.16.64.160.nvlink
40,960.0
tensor
2 $1.23 5.733 Launch
rtx5090-1.32.64.160
40,960.0
1 $1.69 2.560 Launch
teslaa10-4.16.128.160
40,960.0
tensor
4 $1.75 15.920 Launch
teslaa100-1.16.64.160
40,960.0
1 $2.37 14.080 Launch
rtx3090-4.16.128.160
40,960.0
tensor
4 $3.01 15.920 Launch
h100-1.16.64.160
40,960.0
1 $3.83 14.080 Launch
h100nvl-1.16.96.160
40,960.0
1 $4.11 17.440 Launch
teslaa100-2.24.256.160.nvlink
40,960.0
tensor
2 $4.93 32.613 Launch
h200-2.24.256.160.nvlink
40,960.0
tensor
2 $9.40 61.893 Launch
h200-4.32.768.480
40,960.0
tensor
4 $19.23 128.240 Launch
Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa2-1.16.32.240
40,960.0
1 $0.39 -5.475 Launch
teslat4-1.16.64.160
40,960.0
1 $0.42 -5.475 Launch
rtx2080ti-2.16.64.160
40,960.0
tensor
2 $0.71 -4.701 Launch
rtx3080-2.16.64.160
40,960.0
tensor
2 $1.03 -5.181 Launch
rtx4090-1.32.64.160
40,960.0
1 $1.18 -3.555 Launch
rtxa5000-2.16.64.160.nvlink
40,960.0
tensor
2 $1.23 1.539 Launch
rtx5090-1.32.64.160
40,960.0
1 $1.69 -1.635 Launch
teslaa10-4.16.128.160
40,960.0
tensor
4 $1.75 11.725 Launch
teslaa100-1.16.64.160
40,960.0
1 $2.37 9.885 Launch
rtx3090-4.16.128.160
40,960.0
tensor
4 $3.01 11.725 Launch
h100-1.16.64.160
40,960.0
1 $3.83 9.885 Launch
h100nvl-1.16.96.160
40,960.0
1 $4.11 13.245 Launch
teslaa100-2.24.256.160.nvlink
40,960.0
tensor
2 $4.93 28.419 Launch
h200-2.24.256.160.nvlink
40,960.0
tensor
2 $9.40 57.699 Launch
h200-4.32.768.480
40,960.0
tensor
4 $19.23 124.045 Launch
Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa2-1.16.32.240
40,960.0
1 $0.39 -13.115 Launch
teslat4-1.16.64.160
40,960.0
1 $0.42 -13.115 Launch
rtx2080ti-2.16.64.160
40,960.0
tensor
2 $0.71 -12.341 Launch
rtx3080-2.16.64.160
40,960.0
tensor
2 $1.03 -12.821 Launch
rtx4090-1.32.64.160
40,960.0
1 $1.18 -11.195 Launch
rtxa5000-2.16.64.160.nvlink
40,960.0
tensor
2 $1.23 -6.101 Launch
rtx5090-1.32.64.160
40,960.0
1 $1.69 -9.275 Launch
teslaa10-4.16.128.160
40,960.0
tensor
4 $1.75 4.085 Launch
teslaa100-1.16.64.160
40,960.0
1 $2.37 2.245 Launch
rtx3090-4.16.128.160
40,960.0
tensor
4 $3.01 4.085 Launch
h100-1.16.64.160
40,960.0
1 $3.83 2.245 Launch
h100nvl-1.16.96.160
40,960.0
1 $4.11 5.605 Launch
teslaa100-2.24.256.160.nvlink
40,960.0
tensor
2 $4.93 20.779 Launch
h200-2.24.256.160.nvlink
40,960.0
tensor
2 $9.40 50.059 Launch
h200-4.32.768.480
40,960.0
tensor
4 $19.23 116.405 Launch

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