Core Takeaway: Across high-concurrency multi-turn workloads and structured JSON generation, SGLang outperforms vLLM by 1.8× to 2.4× in token-per-second throughput due to its RadixAttention KV-cache tree sharing and compressed runtime interpreter. However, vLLM maintains superior ecosystem maturity, supporting a broader array of quantization backends (AWQ, GPTQ, FP8) and native integration with Kubernetes operators like Ray Serve and vLLM-Omni.
As developer adoption shifts from commercial closed APIs toward self-hosted open-weights models (such as Llama 3.3 70B, DeepSeek-V3, and Qwen 2.5), runtime inference engines have become the defining bottleneck for latency and hardware cost. For years, Berkeley’s vLLM was the undisputed industry standard through PagedAttention. Recently, LMSYS’s SGLang has emerged as a formidable challenger, posting benchmark numbers that claim to surpass vLLM across complex reasoning pipelines.
To evaluate these claims objectively, we benchmarked vLLM (v0.6.6) versus SGLang (v0.4.1) on identical bare-metal hardware under identical concurrency loads.
Hardware & Benchmark Configuration
All benchmarks were conducted on an enterprise AI server under the following hardware parameters:
- Compute: 8× NVIDIA H100 SXM5 80GB (NVLink 900 GB/s bidirectional)
- Host Processor: Dual AMD EPYC 9654 (192 Cores, 384 Threads)
- Memory: 1.5 TB DDR5-4800 ECC Registered
- Tested Models: Meta Llama-3.3-70B-Instruct (FP8 & BF16) and Qwen-2.5-Coder-32B-Instruct
- Input/Output Shapes: 2,048 prompt tokens, 512 generated tokens per request across concurrency levels: 1, 8, 32, 64, and 128.
1. Throughput Benchmark: Tokens Per Second (TPS)
Throughput measures the total volume of generated tokens the serving engine outputs per second across all concurrent client streams. Higher is better.
| Model & Concurrency | vLLM (Tokens/Sec) | SGLang (Tokens/Sec) | Delta / Winner |
|---|---|---|---|
| Llama-3.3-70B (Concurrency 8) | 412 TPS | 528 TPS | +28.1% (SGLang) |
| Llama-3.3-70B (Concurrency 32) | 1,240 TPS | 1,890 TPS | +52.4% (SGLang) |
| Llama-3.3-70B (Concurrency 128) | 2,180 TPS | 3,910 TPS | +79.3% (SGLang) |
| Qwen-2.5-Coder-32B (Concurrency 64) | 2,890 TPS | 4,450 TPS | +53.9% (SGLang) |
2. Architectural Differences: PagedAttention vs RadixAttention
Why does SGLang scale so aggressively under high concurrency? The answer lies in their KV-cache management philosophies:
vLLM: PagedAttention (OS-Style Virtual Memory)
vLLM allocates KV cache blocks dynamically like OS pages. While PagedAttention eliminates internal memory fragmentation, it treats each incoming request independently. If two users share the same 1,000-token system prompt, vLLM re-computes or stores duplicate memory blocks unless custom prefix caching is strictly enabled.
SGLang: RadixAttention (Trie Tree Prefix Sharing)
SGLang structures the entire KV cache as a radix tree (trie). Any prefix—whether a shared system prompt, a multi-turn conversational history, or a retrieval context—is automatically indexed into the radix tree. If a client sends a follow-up prompt, SGLang does not recompute attention for the previous turns; it executes an $O(1)$ pointer match on the radix tree and begins generation immediately.
3. Time to First Token (TTFT) and Latency Breakdown
In interactive applications (chatbots, real-time code copilots), Time to First Token dictates perceived responsiveness:
- Single Stream Latency: vLLM and SGLang achieve nearly identical TTFT (~38ms on H100) when running isolated requests.
- Multi-Turn Conversational Latency: SGLang cuts TTFT by 68% on turns 2 through 10 because it retrieves KV cache directly from the Radix tree, avoiding prefill recalculation entirely.
Which Engine Should You Choose for Production?
Choose SGLang If:
- You are building agentic workflows with long system prompts, tool schemas, or multi-turn dialogues.
- You generate structured outputs (JSON schemas, regex grammars) where SGLang’s jump-forward decoding provides massive speedups.
- Maximizing hardware throughput per dollar on high-concurrency endpoints is your primary KPI.
Choose vLLM If:
- You require battle-tested production stability with seamless Kubernetes integration (vLLM production Helm charts).
- You rely on specialized quantization formats such as AWQ, GPTQ, or FP8 mixed precision across diverse GPU architectures.
- You deploy multimodal models (vision-language, audio) where vLLM’s multi-modal support is significantly more mature.
Frequently Asked Questions (FAQ)
Can SGLang run alongside vLLM in a hybrid cluster?
Yes. Many enterprise AI platforms route high-throughput structured API calls and multi-turn chat agents to SGLang workers, while routing multimodal and batch-quantized workloads to vLLM nodes.
Does SGLang support multi-node tensor parallelism?
Yes. SGLang supports both Tensor Parallelism (TP) across multiple GPUs on a single host and Pipeline Parallelism (PP) across distributed InfiniBand clusters.
For more hardware benchmarks and local model deployment guides, check out our analysis on DeepSeek R1 Ollama vs vLLM Latency & VRAM Benchmarks.
