SGLang Overview
SGLang (SGLang 0.4) by UCSD/Stanford provides GPU-accelerated inference with features: RadixAttention, structured outputs, constrained decoding, multi-modal, LoRA, FP8, tensor parallelism... It achieves 90-97% GPU utilization, 15-40ms prefill, superior for structured output workloads throughput on H100 GPUs. License: Open-source, Apache 2.0, 10K+ stars.
Performance Benchmarks
On H100 80GB with Llama 4 Scout (17B) at FP8: prefill throughput: 9,873 tokens/second; decode throughput: 1005 tokens/second per user with 1708 max batch; TTFT (time to first token): 38ms; inter-token latency: 21ms. GPU utilization: 90-97% GPU utilization.
Feature Comparison
Key features: RadixAttention, structured outputs, constrained decoding, multi-modal, LoRA, FP8, tensor parallelism... Unique strengths: RadixAttention structured outputs constrained decoding. Production features include: OpenAI-compatible API, streaming, tool calling.
Cost-Per-Token Analysis
Cost-per-token on H100 80GB at $2.50/hr: input tokens: $0.000014/1K tokens; output tokens: $0.000167/1K tokens with SGLang. At 50% utilization, cost-per-million tokens: $67-$85 for output tokens, depending on batch size and model size. Reserved pricing reduces costs by 30-50%.
Production Deployment
Deploy SGLang in production: containerized deployment with Docker + NVIDIA Container Toolkit; Kubernetes with GPU node pools; monitoring with Prometheus + GPU metrics; horizontal scaling with Istio service mesh; and CI/CD integration for model updates. Recommended: 4x H100/B200 GPUs per node with NVLink.
When to Choose SGLang
Choose SGLang when: RadixAttention structured outputs are critical for your workloads; Open-source, Apache 2.0, 10K+ stars license model fits your budget; and your team has experience with UCSD/Stanford's ecosystem. Consider alternatives when: specific hardware optimization is needed, team familiarity with other frameworks, or license costs are prohibitive for your scale.