| Type | Vision-Language Model |
| Capabilities | Text Generation, Instruction Following, Reasoning, Mathematical Reasoning+6 more |
| Paper/Blog | |
| License | Apache 2.0 |
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 -it --rm --runtime=nvidia --gpus all --ipc=host --shm-size=128g -p 8000:8000 -v ~/.cache/huggingface:/root/.cache/huggingface -e VLLM_USE_FLASHINFER_MOE_FP16=1 -e VLLM_FLASHINFER_MOE_BACKEND=latency -e HF_TOKEN='YOUR_HF_TOKEN' vllm/vllm-openai:v0.17.1 Qwen/Qwen3.5-122B-A10B --tensor-parallel-size 8 --mm-encoder-tp-mode data --max-model-len auto --max-num-batched-tokens 65536 --gpu-memory-utilization 0.95 --enable-expert-parallel --tool-call-parser qwen3_coder --reasoning-parser qwen3 --enable-auto-tool-choice --max-num-seqs 1024 --trust-remote-code
Note
To use VLLM_USE_FLASHINFER_MOE_FP16=1 and VLLM_FLASHINFER_MOE_BACKEND=latency for more optimal performance, need to use expert parallelism.
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
