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 128K 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: 132K
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
teslat4-2.16.32.160
131,072.0
tensor
2 $0.54 0.984 Launch
teslaa2-2.16.32.160
131,072.0
tensor
2 $0.57 0.984 Launch
teslaa10-2.16.64.160
131,072.0
tensor
2 $0.93 1.784 Launch
rtx2080ti-4.16.32.160
131,072.0
tensor
4 $1.12 1.306 Launch
teslav100-1.12.64.160
131,072.0
1 $1.20 1.123 Launch
rtxa5000-2.16.64.160.nvlink
131,072.0
tensor
2 $1.23 1.784 Launch
rtx3090-2.16.64.160
131,072.0
tensor
2 $1.56 1.784 Launch
rtx5090-1.16.64.160
131,072.0
1 $1.59 1.123 Launch
rtx3080-4.16.64.160
131,072.0
tensor
4 $1.82 1.106 Launch
rtx4090-2.16.64.160
131,072.0
tensor
2 $1.92 1.784 Launch
teslaa100-1.16.64.160
131,072.0
1 $2.37 3.523 Launch
h100-1.16.64.160
131,072.0
1 $3.83 3.523 Launch
h100nvl-1.16.96.160
131,072.0
1 $4.11 4.223 Launch
h200-1.16.128.160
131,072.0
1 $4.74 6.573 Launch
Prices:
Name GPU Price, hour TPS Max Concurrency
teslat4-3.32.64.160
131,072.0
pipeline
3 $0.88 1.459 Launch
teslaa10-2.16.64.160
131,072.0
tensor
2 $0.93 1.598 Launch
teslat4-4.16.64.160
131,072.0
tensor
4 $0.96 2.121 Launch
teslaa2-3.32.128.160
131,072.0
pipeline
3 $1.06 1.459 Launch
rtx2080ti-4.16.32.160
131,072.0
tensor
4 $1.12 1.121 Launch
rtxa5000-2.16.64.160.nvlink
131,072.0
tensor
2 $1.23 1.598 Launch
teslaa2-4.32.128.160
131,072.0
tensor
4 $1.26 2.121 Launch
rtx3090-2.16.64.160
131,072.0
tensor
2 $1.56 1.598 Launch
rtx4090-2.16.64.160
131,072.0
tensor
2 $1.92 1.598 Launch
teslav100-2.16.64.240
131,072.0
tensor
2 $2.22 2.398 Launch
teslaa100-1.16.64.160
131,072.0
1 $2.37 3.337 Launch
rtx5090-2.16.64.160
131,072.0
tensor
2 $2.93 2.398 Launch
h100-1.16.64.160
131,072.0
1 $3.83 3.337 Launch
h100nvl-1.16.96.160
131,072.0
1 $4.11 4.037 Launch
h200-1.16.128.160
131,072.0
1 $4.74 6.387 Launch
Prices:
Name GPU Price, hour TPS Max Concurrency
teslat4-3.32.64.160
131,072.0
pipeline
3 $0.88 1.073 Launch
teslaa10-2.16.64.160
131,072.0
tensor
2 $0.93 1.212 Launch
teslat4-4.16.64.160
131,072.0
tensor
4 $0.96 1.734 Launch
teslaa2-3.32.128.160
131,072.0
pipeline
3 $1.06 1.073 Launch
rtxa5000-2.16.64.160.nvlink
131,072.0
tensor
2 $1.23 1.212 Launch
teslaa2-4.32.128.160
131,072.0
tensor
4 $1.26 1.734 Launch
rtx3090-2.16.64.160
131,072.0
tensor
2 $1.56 1.212 Launch
rtx4090-2.16.64.160
131,072.0
tensor
2 $1.92 1.212 Launch
teslav100-2.16.64.240
131,072.0
tensor
2 $2.22 2.012 Launch
teslaa100-1.16.64.160
131,072.0
1 $2.37 2.951 Launch
rtx5090-2.16.64.160
131,072.0
tensor
2 $2.93 2.012 Launch
h100-1.16.64.160
131,072.0
1 $3.83 2.951 Launch
h100nvl-1.16.96.160
131,072.0
1 $4.11 3.651 Launch
h200-1.16.128.160
131,072.0
1 $4.74 6.001 Launch

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