| Type | MoE LLM |
| Capabilities | Text Generation, Instruction Following, Reasoning, Mathematical Reasoning+5 more |
| Release Date | 01 December, 2025 |
| Links | |
| License | MIT |
Inference Instructions
Deploy and run this model on NVIDIA B200 GPUs using the command below. Copy the command to get started with inference.
CONSOLE
docker run --gpus all --shm-size 128g -p 8000:8000 -v ~/.cache/huggingface:/root/.cache/huggingface -e HF_TOKEN='YOUR_HF_TOKEN' --ipc=host lmsysorg/sglang:v0.5.8-cu130 python3 -m sglang.launch_server --model-path deepseek-ai/DeepSeek-V3.2 --host 0.0.0.0 --port 8000 --max-running-requests 1024 \ --attention-backend nsa --nsa-prefill-backend flashmla_auto --nsa-decode-backend flashmla_kv --kv-cache-dtype fp8_e4m3 --chat-template ./examples/chat_template/tool_chat_template_deepseekv32.jinja --tp 8 --tool-call-parser deepseekv32 --reasoning-parser deepseek-v3 --mem-fraction-static 0.90 --trust-remote-code
Model Benchmarks
Each model was tested with a fixed input size and total token volume while increasing concurrency to measure serving performance under load.
ITL vs Concurrency
Time to First Token
Throughput Scaling
Total Tokens/sec vs Avg TTFT
Vultr Cloud GPU
NVIDIA HGX B200
Deploy NVIDIA B200 on Vultr Cloud GPU