Qwen3-VL-30B-A3B-Thinking

reasoning
multimodal

Qwen3‑VL‑30B‑A3B‑Thinking is a multimodal reasoning model from the Qwen3‑VL series, built on a Mixture‑of‑Experts (MoE) architecture. The model has a total of 30 billion parameters, of which only 3 billion are active during inference. It is based on a hybrid multimodal block that integrates the DeepStack visual encoder with the Qwen3‑LM language core. The connection between them is handled by the Interleaved‑MRoPE positioning mechanism, which precisely distributes frequency features across time, width, and height. This solution gives the model stable spatial perception (including 3D grounding) and efficient synchronization of video timestamps with textual semantics. As a result, it forms a unified visual‑textual stack in which the text transformer processes visual representations as n‑dimensional tokens seamlessly embedded into the common reasoning context.

Like all models in the series, Qwen3‑VL‑30B‑A3B‑Thinking supports a 256 K‑token context window (expandable to 1 M), allowing it to work with multi‑hour videos, large books, and complex agent pipelines without loss of data coherence. The “Thinking” (reasoning) mode provides an elevated cognitive level: the model generates responses step by step, demonstrating strict causal reasoning and well‑grounded conclusions.

The model is particularly effective in scenarios requiring the integration of textual and visual data with deep reasoning, such as the analysis of illustrated documents, semantic understanding and transcription of videos, OCR systems (supporting 32 languages), visual programming (code generation from visual sketches), and scientific or educational applications.

Thanks to vendor‑side quantization in FP8 mode, the model can be run with a practical context length on two RTX 4090 GPUs, making it accessible to a wide range of researchers and developers.


Announce Date: 26.09.2025
Parameters: 31B
Experts: 128
Activated at inference: 3B
Context: 263K
Layers: 48
Attention Type: Full Attention
Developer: Qwen
Transformers Version: 4.57.0.dev0
License: Apache 2.0

Public endpoint

Use our pre-built public endpoints for free to test inference and explore Qwen3-VL-30B-A3B-Thinking 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-VL-30B-A3B-Thinking

Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa100-1.16.64.160
262,144.0
1 $2.37 152.86 2.198 Launch
h100-1.16.64.160
262,144.0
1 $3.83 142.32 2.195 Launch
h100nvl-1.16.96.160
262,144.0
1 $4.11 2.728 Launch
h200-1.16.128.160
262,144.0
1 $4.74 4.519 Launch
rtx5090-2.16.64.160
262,144.0
tensor
2 $2.93 1.433 Launch
teslaa100-2.24.96.160.nvlink
262,144.0
tensor
2 $4.61 5.105 Launch
h200-2.24.256.160.nvlink
262,144.0
tensor
2 $9.40 9.747 Launch
rtx3090-3.16.96.160
262,144.0
pipeline
3 $2.29 1.608 Launch
teslaa10-3.16.96.160
262,144.0
pipeline
3 $1.34 1.486 Launch
rtx4090-3.16.96.160
262,144.0
pipeline
3 $2.83 1.604 Launch
teslaa10-4.12.48.160
262,144.0
tensor
4 $1.57 2.218 Launch
rtx3090-4.16.64.160
262,144.0
tensor
4 $2.89 2.381 Launch
rtx4090-4.16.64.160
262,144.0
tensor
4 $3.60 2.375 Launch
rtx3090-4.16.128.160.nvlink
262,144.0
tensor
4 $3.01 2.381 Launch
teslaa2-4.32.128.160
262,144.0
tensor
4 $1.26 1.007 Launch
Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa100-1.16.64.160
262,144.0
1 $2.37 146.89 1.635 Launch
h100-1.16.64.160
262,144.0
1 $3.83 1.632 Launch
h100nvl-1.16.96.160
262,144.0
1 $4.11 2.165 Launch
h200-1.16.128.160
262,144.0
1 $4.74 3.956 Launch
teslaa100-2.24.96.160.nvlink
262,144.0
tensor
2 $4.61 4.551 Launch
h200-2.24.256.160.nvlink
262,144.0
tensor
2 $9.40 9.193 Launch
rtx3090-3.16.96.160
262,144.0
pipeline
3 $2.29 1.063 Launch
rtx4090-3.16.96.160
262,144.0
pipeline
3 $2.83 1.058 Launch
rtx5090-3.16.96.160
262,144.0
pipeline
3 $4.34 1.959 Launch
rtx3090-4.16.64.160
262,144.0
tensor
4 $2.89 1.844 Launch
teslaa10-4.16.64.160
262,144.0
tensor
4 $1.62 1.682 Launch
rtx4090-4.16.64.160
262,144.0
tensor
4 $3.60 1.838 Launch
rtx5090-4.16.128.160
262,144.0
tensor
4 $5.74 3.039 Launch
rtx3090-4.16.128.160.nvlink
262,144.0
tensor
4 $3.01 1.844 Launch
teslaa2-6.32.128.160
262,144.0
pipeline
6 $1.65 1.346 Launch
Prices:
Name GPU Price, hour TPS Max Concurrency
h200-1.16.128.240
262,144.0
1 $4.74 2.258 Launch
teslaa100-2.24.256.240
262,144.0
tensor
2 $4.93 99.16 3.302 Launch
teslaa100-2.24.256.320.nvlink
262,144.0
tensor
2 $4.94 3.302 Launch
rtx5090-4.16.128.320
262,144.0
tensor
4 $5.76 1.200 Launch
rtx4090-6.44.256.240
262,144.0
pipeline
6 $5.84 76.21 1.465 Launch
dedicated-rtx3090-8.64.128.960-1
262,144.0
tensor
8 $6.04 1.471 Launch
rtx4090-8.44.256.240
262,144.0
tensor
8 $7.52 1.465 Launch
h100-2.24.256.240
262,144.0
tensor
2 $7.85 120.69 3.150 Launch
h100nvl-2.24.192.240
262,144.0
tensor
2 $8.17 3.867 Launch
h200-2.24.256.240.nvlink
262,144.0
tensor
2 $9.41 7.448 Launch

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