Qwen3-8B

reasoning

Qwen3-8B represents a new tier in the series. With 8.2 billion parameters, this model retains an architecture of 36 layers and 32 attention heads, but introduces several important improvements — it no longer uses tied embeddings, and its context window has been expanded to 40K tokens, enabling excellent capabilities for handling long documents and complex tasks.

The doubling of parameter count compared to the 4B version significantly enhances response quality across all task types, especially in mathematical reasoning, programming, and advanced analysis. The model excels in tasks requiring multi-step reasoning and deep contextual understanding. Built-in support for both *thinking* and *non-thinking* modes allows performance optimization based on task complexity and available processing time, while the *Thinking Budget* mechanism enables fine-grained control over computational intensity for optimal efficiency.

Qwen3-8B is ideal for advanced professional applications such as financial analysis, medical diagnostics, and legal practice. It is well-suited for building intelligent assistants for professionals, automated technical documentation systems, and educational platforms.


Announce Date: 29.04.2025
Parameters: 9B
Context: 41K
Layers: 36
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 Qwen3-8B 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 Qwen3-8B

Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa10-1.16.32.160
40,960.0
1 $0.53 2.226 Launch
teslat4-2.16.32.160
40,960.0
tensor
2 $0.54 2.864 Launch
teslaa2-2.16.32.160
40,960.0
tensor
2 $0.57 2.877 Launch
rtx2080ti-2.12.64.160
40,960.0
tensor
2 $0.69 1.556 Launch
rtx3090-1.16.24.160
40,960.0
1 $0.83 2.399 Launch
rtx3080-2.16.32.160
40,960.0
tensor
2 $0.97 1.245 Launch
rtx4090-1.16.32.160
40,960.0
1 $1.02 2.393 Launch
rtxa5000-2.16.64.160.nvlink
40,960.0
tensor
2 $1.23 5.461 Launch
rtx5090-1.16.64.160
40,960.0
1 $1.59 3.673 Launch
teslaa100-1.16.64.160
40,960.0
1 $2.37 11.508 Launch
h100-1.16.64.160
40,960.0
1 $3.83 11.496 Launch
h100nvl-1.16.96.160
40,960.0
1 $4.11 13.771 Launch
teslaa100-2.24.96.160.nvlink
40,960.0
tensor
2 $4.61 24.025 Launch
h200-1.16.128.160
40,960.0
1 $4.74 21.411 Launch
h200-2.24.256.160.nvlink
40,960.0
tensor
2 $9.40 43.832 Launch
Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa10-1.16.32.160
40,960.0
1 $0.53 1.673 Launch
teslat4-2.16.32.160
40,960.0
tensor
2 $0.54 2.311 Launch
teslaa2-2.16.32.160
40,960.0
tensor
2 $0.57 2.325 Launch
rtx2080ti-2.12.64.160
40,960.0
tensor
2 $0.69 1.003 Launch
rtx3090-1.16.24.160
40,960.0
1 $0.83 1.847 Launch
rtx4090-1.16.32.160
40,960.0
1 $1.02 1.840 Launch
rtxa5000-2.16.64.160.nvlink
40,960.0
tensor
2 $1.23 4.909 Launch
rtx3080-3.16.64.160
40,960.0
pipeline
3 $1.43 1.820 Launch
rtx5090-1.16.64.160
40,960.0
1 $1.59 3.121 Launch
rtx3080-4.16.64.160
40,960.0
tensor
4 $1.82 2.947 Launch
teslaa100-1.16.64.160
40,960.0
1 $2.37 10.955 Launch
h100-1.16.64.160
40,960.0
1 $3.83 10.943 Launch
h100nvl-1.16.96.160
40,960.0
1 $4.11 13.219 Launch
teslaa100-2.24.96.160.nvlink
40,960.0
tensor
2 $4.61 23.472 Launch
h200-1.16.128.160
40,960.0
1 $4.74 20.858 Launch
h200-2.24.256.160.nvlink
40,960.0
tensor
2 $9.40 43.279 Launch
Prices:
Name GPU Price, hour TPS Max Concurrency
teslat4-2.16.32.160
40,960.0
tensor
2 $0.54 1.162 Launch
teslaa2-2.16.32.160
40,960.0
tensor
2 $0.57 1.175 Launch
rtx2080ti-3.12.24.120
40,960.0
pipeline
3 $0.84 1.136 Launch
teslaa10-2.16.64.160
40,960.0
tensor
2 $0.93 3.759 Launch
rtx2080ti-4.16.32.160
40,960.0
tensor
4 $1.12 2.419 Launch
rtxa5000-2.16.64.160.nvlink
40,960.0
tensor
2 $1.23 3.759 Launch
rtx3090-2.16.64.160
40,960.0
tensor
2 $1.56 4.106 Launch
rtx5090-1.16.64.160
40,960.0
1 $1.59 1.971 Launch
rtx3080-4.16.64.160
40,960.0
tensor
4 $1.82 1.797 Launch
rtx4090-2.16.64.160
40,960.0
tensor
2 $1.92 4.093 Launch
teslaa100-1.16.64.160
40,960.0
1 $2.37 9.805 Launch
h100-1.16.64.160
40,960.0
1 $3.83 9.794 Launch
h100nvl-1.16.96.160
40,960.0
1 $4.11 12.069 Launch
teslaa100-2.24.96.160.nvlink
40,960.0
tensor
2 $4.61 22.322 Launch
h200-1.16.128.160
40,960.0
1 $4.74 19.708 Launch
h200-2.24.256.160.nvlink
40,960.0
tensor
2 $9.40 42.129 Launch

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