Reasoning
How to deploy DeepSeek-V4-Pro-0813 on a GPU cloud
A 1.6T-parameter (49B active) reasoning model with a 1M-token context. Full specs, license and use cases.
DeepSeek-V4-Pro-0813 size and hardware requirements
1650.5B
Total parameters
Mixture-of-experts: 49B active parameters (stated on the model card)
Active parameters
FP4 + FP8 Mixed
Published precision
1128.0 GB
Min VRAM (native)
| Precision | Weight size on disk | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| FP4 + FP8 Mixed (native) | 831.4 GB | 1128.0 GB | AMD MI300X | 6 | $14.34/hr |
How to run DeepSeek-V4-Pro-0813
Run DeepSeek-V4-Pro-0813 with vLLM
From DeepSeek-V4-Pro-0813's own model card: an example serving it with vLLM and DSpark speculative decoding on a single 4×GB300 node.
vllm serve deepseek-ai/DeepSeek-V4-Pro-0813 \
--trust-remote-code --kv-cache-dtype fp8 --block-size 256 \
--data-parallel-size 4 --enable-expert-parallel \
--moe-backend deep_gemm_mega_moe \
--attention-config '{"use_fp4_indexer_cache": true}' \
--speculative-config '{"method":"dspark","num_speculative_tokens":7,"draft_sample_method":"greedy"}'Source: https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro-0813/raw/main/README.md
Run DeepSeek-V4-Pro-0813 with SGLang
From DeepSeek-V4-Pro-0813's own model card, same single 4×GB300 node example as the vLLM command above.
sglang serve \
--trust-remote-code \
--model-path deepseek-ai/DeepSeek-V4-Pro-0813 \
--tp 4 \
--moe-runner-backend flashinfer_mxfp4 \
--speculative-algorithm DSPARK \
--mem-fraction-static 0.90 \
--chunked-prefill-size 4096Source: https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro-0813/raw/main/README.md
Deploy DeepSeek-V4-Pro-0813 on Aquanode
Aquanode has no one-click deploy template for DeepSeek-V4-Pro-0813; 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 (6× AMD MI300X 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