SGLang vs vLLM: RadixAttention Multi-Turn Serving vs PagedAttention Batch Inference
Automatic KV cache reuse across complex prompt trees versus general-purpose linear batch throughput
vLLM pioneered PagedAttention to eliminate internal KV cache memory fragmentation for linear prompt-response batches. SGLang introduces RadixAttention, maintaining a radix tree over the KV cache to automatically match, retain, and reuse prompt prefixes across multi-turn agent conversations, few-shot templates, and structured JSON generation, achieving 3x to 5x higher throughput on repetitive agent workloads.
Technical Tradeoff & Benchmark Matrix
| Serving Architecture | SGLang (RadixAttention) | vLLM (PagedAttention) |
|---|---|---|
| Cache Management Primitive | Radix tree over LRU memory pages (RadixAttention) | Virtual memory page tables (PagedAttention) |
| Multi-Turn Prefix Cache Reuse | Automatic, zero-overhead across all requests in batch | Requires manual prefix caching configuration or explicit hash keys |
| Structured JSON Decoding | Native compressed finite state machine (FSM) decoding | Outlines or guided-decoding integration with regex compilation |
| Complex Workflow Speedup | 3.0x to 5.2x faster for multi-agent loops & tool calling | 1.0x to 1.5x baseline throughput for linear chat |
| Kernel Optimization | FlashInfer kernels optimized for variable-length decoding | Custom fused PagedAttention CUDA kernels + FlashAttention-3 |
| Runtime Stack | Lightweight Python frontend with C++ runtime bindings | Modular Python engine with comprehensive serving integrations |
Interactive Calculation Engines
Calculate empirical values directly using our zero-latency edge computational models:
LLM Reasoning & Inference Cost Calculator
Evaluates hidden chain-of-thought token costs and monthly budget scaling for reasoning models versus standard large language models.
Transformer KV Cache VRAM Allocation Modeler
Models High-Bandwidth Memory (HBM) consumption for transformer attention layers during long-context serving.
Frequently Asked Questions & Technical Analysis
Q: How does RadixAttention achieve zero-cost prefix caching in SGLang?
RadixAttention treats the KV cache as a radix tree where nodes represent token sequences. When a new prompt arrives with a shared system prompt or few-shot examples, SGLang traverses the tree to locate existing KV cache tensors, completely bypassing the compute-heavy prefill phase for matching tokens.
Q: When should an engineering team choose vLLM over SGLang?
vLLM is ideal for general-purpose chatbot serving, diverse batch generation without shared prefixes, enterprise Triton deployment ecosystems, and architectures requiring specialized hardware backends (such as AWS Neuron, Google TPU, or AMD ROCm).