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
teslat4-2.16.32.160
40,960.0
tensor
2 $0.54 1.660 Launch
teslaa2-2.16.32.160
40,960.0
tensor
2 $0.57 1.680 Launch
rtx2080ti-3.12.24.120
40,960.0
pipeline
3 $0.84 1.622 Launch
teslaa10-2.16.64.160
40,960.0
tensor
2 $0.93 5.556 Launch
rtx2080ti-4.16.32.160
40,960.0
tensor
4 $1.12 3.546 Launch
rtxa5000-2.16.64.160.nvlink
40,960.0
tensor
2 $1.23 5.556 Launch
rtx3090-2.16.64.160
40,960.0
tensor
2 $1.56 6.076 Launch
rtx5090-1.16.64.160
40,960.0
1 $1.59 2.874 Launch
rtx3080-4.16.64.160
40,960.0
tensor
4 $1.82 2.613 Launch
rtx4090-2.16.64.160
40,960.0
tensor
2 $1.92 6.057 Launch
teslaa100-1.16.64.160
40,960.0
1 $2.37 14.625 Launch
h100-1.16.64.160
40,960.0
1 $3.83 14.608 Launch
h100nvl-1.16.96.160
40,960.0
1 $4.11 18.021 Launch
teslaa100-2.24.96.160.nvlink
40,960.0
tensor
2 $4.61 33.401 Launch
h200-1.16.128.160
40,960.0
1 $4.74 29.480 Launch
h200-2.24.256.160.nvlink
40,960.0
tensor
2 $9.40 63.111 Launch
Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa10-2.16.64.160
40,960.0
tensor
2 $0.93 1.648 Launch
teslat4-4.16.64.160
40,960.0
tensor
4 $0.96 3.563 Launch
rtxa5000-2.16.64.160.nvlink
40,960.0
tensor
2 $1.23 1.648 Launch
teslaa2-4.32.128.160
40,960.0
tensor
4 $1.26 3.602 Launch
rtx3090-2.16.64.160
40,960.0
tensor
2 $1.56 2.168 Launch
rtx4090-2.16.64.160
40,960.0
tensor
2 $1.92 2.148 Launch
teslaa100-1.16.64.160
40,960.0
1 $2.37 10.717 Launch
rtx5090-2.16.64.160
40,960.0
tensor
2 $2.93 5.990 Launch
h100-1.16.64.160
40,960.0
1 $3.83 10.700 Launch
h100nvl-1.16.96.160
40,960.0
1 $4.11 14.112 Launch
teslaa100-2.24.96.160.nvlink
40,960.0
tensor
2 $4.61 29.493 Launch
h200-1.16.128.160
40,960.0
1 $4.74 25.572 Launch
h200-2.24.256.160.nvlink
40,960.0
tensor
2 $9.40 59.203 Launch
Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa10-4.16.64.160
40,960.0
tensor
4 $1.62 4.247 Launch
teslaa2-6.32.128.160
40,960.0
pipeline
6 $1.65 1.550 Launch
rtxa5000-4.16.128.160.nvlink
40,960.0
tensor
4 $2.34 4.247 Launch
teslaa100-1.16.64.160
40,960.0
1 $2.37 3.610 Launch
rtx3090-4.16.64.160
40,960.0
tensor
4 $2.89 5.287 Launch
rtx4090-4.16.64.160
40,960.0
tensor
4 $3.60 5.248 Launch
h100-1.16.64.160
40,960.0
1 $3.83 3.593 Launch
h100nvl-1.16.96.160
40,960.0
1 $4.11 7.005 Launch
rtx5090-3.16.96.160
40,960.0
pipeline
3 $4.34 5.908 Launch
teslaa100-2.24.96.160.nvlink
40,960.0
tensor
2 $4.61 22.386 Launch
h200-1.16.128.160
40,960.0
1 $4.74 18.465 Launch
rtx5090-4.16.128.160
40,960.0
tensor
4 $5.74 12.932 Launch
h200-2.24.256.160.nvlink
40,960.0
tensor
2 $9.40 52.096 Launch

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