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Batch Inference is now generally available on Runpod Serverless

Qwen

Qwen3.8 2.4T A95B

Qwen's 2400B-parameter mixture-of-experts (95B active) open language model with a 256K-token context, ready to serve across a multi-node Runpod Cluster.

Language
Text generation
2.4T MoE · 95B active · 256K context
See deploy options

Qwen3.8 2.4T A95B

Specifications

Provider
Qwen
Modality
Language
Task
Text generation
Parameters
2.4T
Active parameters
95B
Context
256K tokens
Precision
BF16
Weights
4892
License
qwen3.8-max
Released
2026-08-07
Endpoint ID
Weights on Hugging Face
Qwen/Qwen3.8-2.4T-A95B

Frequently asked questions

How do I deploy Qwen3.8 2.4T A95B on Runpod?

Deploy the vLLM worker from the Runpod Hub as a Serverless endpoint and set MODEL_NAME to Qwen/Qwen3.8-2.4T-A95B. The endpoint is OpenAI-compatible and scales to zero when idle. You can also run vLLM on a Pod.

What GPU do I need to run Qwen3.8 2.4T A95B?

Qwen3.8 2.4T A95B needs about 5,871 GB of GPU memory, more than one 8-GPU node, so it runs on a multi-node Runpod Cluster.

How much does Qwen3.8 2.4T A95B cost on Runpod?

Serverless endpoints and Pods bill per second for the GPU you choose. A Serverless endpoint costs nothing while scaled to zero.

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qwen-qwen3-8-2-4t-a95b
Qwen/Qwen3.8-2.4T-A95B
Language
2026-08-07 17:00
Multi-node cluster