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How to deploy DeepSeek-V3.2-Exp on a GPU cloud
A 685B (MoE) language model for chat and instruction-following. Full specs, license and use cases.
DeepSeek-V3.2-Exp size and hardware requirements
685.4B
Total parameters
Mixture-of-experts: 8 of 256 experts active per token (exact active-parameter count not stated on the model card)
Active parameters
F8_E4M3
Published precision
766.0 GB
Min VRAM (native)
| Precision | Weight size on disk | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| FP8 (native) | 638.3 GB | 766.0 GB | RTX PRO 6000 | 8 | $13.12/hr |
| INT4 (quantized) | 319.2 GB | 383.0 GB | RTX A6000 | 8 | $2.64/hr |
How to run DeepSeek-V3.2-Exp
Run DeepSeek-V3.2-Exp with SGLang
From deepseek-ai/DeepSeek-V3.2-Exp's own deployment docs.
# H200
docker pull lmsysorg/sglang:dsv32
# MI350
docker pull lmsysorg/sglang:dsv32-rocm
# NPUs
docker pull lmsysorg/sglang:dsv32-a2
docker pull lmsysorg/sglang:dsv32-a3Source: https://huggingface.co/deepseek-ai/DeepSeek-V3.2-Exp/raw/main/README.md
Deploy DeepSeek-V3.2-Exp on Aquanode
Aquanode has no one-click deploy template for DeepSeek-V3.2-Exp; you install the inference engine yourself with the commands below. Aquanode sells GPU pods billed per second, not a hosted inference API.
- Launch a bare GPU pod sized to the requirement above (8× RTX PRO 6000 or larger).
- Open a terminal on the pod, or save one of the commands above as a startup script so it runs automatically the first time the pod boots.
- Run the command and connect to the resulting endpoint.
Launch a GPU pod