| Type | Dense LLM |
| Capabilities | Text Generation, Instruction Following, Reasoning, Mathematical Reasoning+4 more |
| Release Date | 29 April, 2025 |
| Links | |
| 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 --gpus all --shm-size 64g -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 Qwen/Qwen3-1.7B --host 0.0.0.0 --port 8000 --max-prefill-tokens 32768 --max-running-requests 1024 --enable-piecewise-cuda-graph --tp 8 --mem-fraction-static 0.95 --attention-backend flashinfer --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