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Qwen3 4B

NVIDIA
The Qwen3-4B is a mid-scale Causal Language Model built on a 36-layer architecture with 3.6B non-embedding parameters, positioned as a balanced option for workloads that require stronger reasoning depth without sacrificing serving efficiency. It supports a native 32,768-token context window and can be extended up to 131,072 tokens using YaRN, enabling reliable performance on long-form documents, multi-step analysis, and extended conversational workflows. Incorporating Qwen3’s hybrid thinking design, the model adapts smoothly between deliberate reasoning tasks and fast, general-purpose generation.
TypeDense LLM
CapabilitiesText Generation, Instruction Following, Reasoning, Mathematical Reasoning+4 more
Group Release DateApril 28, 2025
Links
Paper|Blog|HF Model Card
LicenseApache 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/Qwen3-4B 
 --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

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