Quick Verdict
Bottom Line
SGLang is faster for workloads with prefix overlap: multi-turn chat, RAG, few-shot prompting, coding agents. RadixAttention reuses cached KV computations across requests, delivering 29% higher throughput on standard benchmarks and up to 6.4x on prefix-heavy traffic. vLLM is the safer choice for diverse hardware environments, batch processing of unique prompts, and teams that want the largest open-source community. Both serve trillions of tokens daily in production.
Architecture: PagedAttention vs RadixAttention
The core difference comes down to what happens to the KV cache after a request completes. The KV cache stores attention computations for every token in a sequence. It is the bottleneck that determines throughput, latency, and cost at scale.
vLLM: PagedAttention
Inspired by OS virtual memory. Breaks the KV cache into small, fixed-size blocks that can be stored anywhere in GPU memory. Each sequence grows its cache block by block, on demand. When a request finishes, blocks are freed immediately for reuse. Near-zero memory waste. Clean, predictable, and optimal when requests have no shared prefixes.
SGLang: RadixAttention
Extends paged memory management with a critical insight: don't throw away the KV cache after a request completes. Maintains an LRU cache of KV computations in a radix tree, indexed by token sequence. New requests get prefix-matched against the tree. If a match exists, SGLang reuses the cached computation instead of recomputing from scratch. Multi-turn chat, shared system prompts, and RAG over common documents all trigger reuse automatically.
Why Prefix Reuse Matters
Consider a coding agent that sends a system prompt with 2,000 tokens of tool definitions on every request. With PagedAttention, those 2,000 tokens get recomputed on each call. With RadixAttention, they get computed once and cached. On a 200-request session, that is 400,000 tokens of redundant prefill eliminated. At scale, this is the difference between needing 8 GPUs and needing 5.
The tradeoff is memory. RadixAttention's LRU cache consumes GPU memory that could otherwise serve new requests. When GPU memory is tight and prefix overlap is low, the cache overhead works against you. SGLang handles this with LRU eviction, recursively freeing leaf nodes when memory pressure rises. But for workloads with genuinely unique prompts, the cache adds overhead without benefit.
vLLM V1 Architecture
vLLM shipped a major V1 engine rewrite that removes the distinction between prefill and decode phases, treating prompt tokens and generated tokens uniformly. V1 pins host memory and uses direct DMA transfers (zero-copy), eliminating redundant tensor copies between GPU and CPU during scheduling. The result is lower scheduling overhead at high concurrency, an area where V0 struggled. V1 also integrates FlashAttention 3, an encoder cache for multimodal models, and external KV cache layer support via LMCache.
Benchmarks: H100 Throughput Comparison
All numbers are on NVIDIA H100 GPUs unless stated otherwise. These benchmarks come from independent tests by Spheron, PremAI, and Clarifai, not from either project's own marketing.
| Metric | SGLang | vLLM |
|---|---|---|
| Output tokens/sec (general) | ~16,200 | ~12,500 |
| Throughput advantage (general) | 29% faster | Baseline |
| Prefix-heavy workloads (RAG, multi-turn) | Up to 6.4x faster | Baseline |
| DeepSeek V3 inference | 3.1x faster | Baseline |
| DeepSeek V3 with MTP (H200, BS=1) | 1.8x decode speedup | No MTP support |
| Time to first token (TTFT) | Lower (prefix caching) | Higher (recompute each request) |
| Batch unique prompts (no prefix overlap) | Comparable | Comparable |
The gap is largest on prefix-heavy workloads. When every request starts with a unique prompt and shares nothing with other requests, SGLang's cache provides no benefit, and the two engines converge. Real-world traffic almost always has some prefix overlap. System prompts, tool definitions, shared context windows, and multi-turn conversations all create reusable prefixes.
Expert Parallelism at Scale
SGLang's prefill-decode disaggregation with large-scale expert parallelism achieves 52,300 input tokens/sec and 22,300 output tokens/sec per node on DeepSeek models across 96 H100 GPUs. The prefill instance uses DeepEP normal mode while the decode instance uses low-latency mode, allowing different optimization strategies for each phase. vLLM also supports disaggregated prefill, but SGLang's implementation is more mature for MoE models.
Structured Output: JSON Compliance and Speed
Coding agents need structured output. Tool calls, function arguments, and API responses all require valid JSON. Without constrained decoding, typical LLM JSON compliance sits at 90-94%. Both engines support guided decoding to force valid output, but they handle it differently.
SGLang: Compressed FSM
SGLang uses a compressed finite state machine for constrained decoding. The key optimization: mask generation (computing which tokens are valid at each step) overlaps with the GPU's inference computation. This means structured output adds minimal latency. The compressed FSM reduces latency by up to 2x and boosts throughput by up to 2.5x compared to standard guided decoding. JSON compliance reaches 96-98%.
vLLM: Guided Decoding
vLLM supports guided decoding via outlines and lm-format-enforcer. At batch sizes of 8 or greater, vLLM shows a significant performance drop with guided decoding enabled because mask generation runs sequentially and does not overlap with GPU inference. The overhead is measurable and grows with batch size. vLLM is aware of this and actively working on overlapped execution.
If your workload requires structured output at high batch sizes, SGLang's overlapped mask generation gives it a clear edge. For low-batch or latency-insensitive use cases, both engines produce correct output.
Speculative Decoding
Speculative decoding uses a small draft model to predict multiple tokens, then verifies them with the main model in a single forward pass. Both engines support it. The speedup is 1.5-3x for memory-bound scenarios (large models, small batches), and diminishes at high batch sizes where the GPU is already saturated.
| Feature | SGLang | vLLM |
|---|---|---|
| Basic speculative decoding | Yes | Yes |
| EAGLE speculative decoding | Yes (optimized for DeepSeek MTP) | Yes |
| DeepSeek V3 MTP decode speedup | 1.8x (BS=1), 1.5x (BS=32) on H200 | Not optimized for DeepSeek MTP |
| Speculative prefill | Supported | Supported (draft-assisted sparse prefill) |
| Typical speedup range | 1.5-3x (memory-bound) | 1.5-3x (memory-bound) |
The practical difference is DeepSeek-specific. SGLang integrates DeepSeek's Multi-Token Prediction natively, achieving 1.8x decode speedup at batch size 1 on H200 GPUs. For non-DeepSeek models, both engines offer comparable speculative decoding performance.
Hardware and Model Support
This is vLLM's strongest advantage. If you are not running NVIDIA GPUs, vLLM is likely your only option among these two.
| Hardware | SGLang | vLLM |
|---|---|---|
| NVIDIA GPUs (H100, A100, B200) | Full support | Full support |
| AMD GPUs (MI300X, MI355) | Growing support (ROCm) | Full support (ROCm) |
| Google TPUs (v4-v6e) | SGLang-Jax backend (Oct 2025) | Native support |
| AWS Trainium / Inferentia | No | Yes |
| Intel Gaudi | No | Yes (via plugin) |
| Intel XPU | Yes | Yes |
| Arm / PowerPC CPUs | No | Yes |
| Ascend NPUs | Yes | Yes |
Quantization
Both engines support FP8, FP4, INT4, INT8, AWQ, GPTQ, Marlin, and bitsandbytes quantization. vLLM also integrates with LLM Compressor for custom quantization workflows. SGLang supports torchao-based quantization via command-line flags. For FP8 on H100/H200 GPUs (the most common production setup), both engines have mature support with comparable performance.
Model Architecture Coverage
vLLM supports more model architectures, including encoder-decoder models (T5, BART) that SGLang does not. Both support the full range of popular decoder-only models: Llama, Mistral, Qwen, DeepSeek, Gemma, Phi, and hundreds of Hugging Face models. SGLang also natively supports vision-language models with RadixAttention extending KV cache reuse to image tokens.
Who Runs What in Production
SGLang Production Users
xAI (Grok 3), Microsoft Azure endpoints, LinkedIn AI features, Cursor code completion, Oracle Cloud, Google Cloud, AWS. Runs across 400,000+ GPUs. Generates trillions of tokens daily. Officially preferred framework for DeepSeek V3/R1 serving. Joined the PyTorch ecosystem.
vLLM Production Users
Default backend for most cloud API providers and OpenAI-compatible serving deployments. 75K GitHub stars. Used by thousands of organizations for self-hosted inference. Red Hat, IBM, and Intel contribute actively. The vLLM production stack and Semantic Router projects extend it into multi-model routing and fleet management.
SGLang powers the highest-profile single deployments (Grok 3 is a flagship example). vLLM powers more total deployments by volume because it is the default choice for most teams standing up an OpenAI-compatible endpoint. The two engines have increasingly converged on features: both support continuous batching, paged attention, chunked prefill, disaggregated prefill, speculative decoding, CUDA graphs, and tensor/pipeline/expert/data parallelism.
Community and Ecosystem
| Metric | SGLang | vLLM |
|---|---|---|
| GitHub stars | ~25K | ~75K |
| GitHub forks | ~5K | ~12K+ |
| Contributors per release | ~100-150 | ~200+ |
| Origin | UC Berkeley (LMSYS) | UC Berkeley |
| Lead maintainer affiliation | xAI / LMSYS | vLLM project (independent) |
| Corporate backing | xAI, AMD, NVIDIA | Red Hat, IBM, Intel, NVIDIA |
| PyTorch ecosystem member | Yes | Yes |
| Documentation | Good (docs.sglang.io) | Extensive (docs.vllm.ai) |
vLLM has the larger community by every metric: 3x more stars, 2x more forks, more contributors per release, and more third-party integrations. If you hit a problem at 3am, you are more likely to find an existing GitHub issue, Stack Overflow answer, or blog post for vLLM. SGLang's community is smaller but fast-growing, with deep expertise in MoE models and DeepSeek optimization. Both projects originated at UC Berkeley.
When SGLang Wins
Multi-Turn and Chat Workloads
RadixAttention caches the conversation history prefix. Each new turn reuses the prior computation instead of reprocessing the entire context. For 20-turn conversations with a 2K-token system prompt, the savings are 40,000+ tokens of redundant prefill per session. This translates to 6.4x throughput on prefix-heavy benchmarks.
DeepSeek Models
SGLang is the officially preferred serving framework for DeepSeek V3 and R1. Day-one support with MTP speculative decoding (1.8x speedup), expert parallelism with EPLB load balancing, and prefill-decode disaggregation tuned for MoE architectures. vLLM supports DeepSeek but without the same level of optimization.
Structured JSON Output at Scale
The compressed FSM overlaps mask generation with GPU inference, maintaining throughput even with constrained decoding enabled. At batch size 8+, SGLang's structured output is measurably faster than vLLM's sequential guided decoding. For APIs serving thousands of concurrent requests that all require valid JSON, this gap compounds.
Coding Agent Backends
Coding agents send repeated system prompts, tool definitions, and context windows. Every request in a session shares a long prefix. RadixAttention was designed for exactly this pattern. Cursor chose SGLang for code completion for this reason. The prefix caching alone can reduce GPU cost by 30-40% for agent-style traffic.
When vLLM Wins
Non-NVIDIA Hardware
If your fleet includes TPUs, AWS Trainium, Intel Gaudi, or Arm CPUs, vLLM is the only viable option between these two. SGLang's TPU support (via SGLang-Jax) is experimental. vLLM has production-grade support for Google TPU v4-v6e, Trainium, Inferentia, and Gaudi with active corporate contributions from Google, AWS, and Intel.
Batch Processing of Unique Prompts
When every request has a distinct prompt with no shared prefix (bulk document summarization, one-off completions, evaluation harnesses), RadixAttention's cache provides no benefit and adds memory overhead. vLLM's clean page-and-free model is simpler and avoids the cache management cost.
Ecosystem and Integration Breadth
75K GitHub stars. 200+ contributors per release. More third-party integrations (LangChain, LlamaIndex, BentoML, Ray Serve). More tutorials, blog posts, and Stack Overflow answers. The vLLM production stack adds multi-model routing and fleet management. For teams that value community size and integration options, vLLM is lower risk.
Encoder-Decoder Models
vLLM supports encoder-decoder architectures (T5, BART, Whisper) that SGLang does not. If your pipeline includes summarization models, translation models, or speech-to-text alongside LLM inference, vLLM can serve all of them from one engine. SGLang focuses exclusively on decoder-only and vision-language models.
What This Means for Coding Agents
Every coding agent, whether it is Claude Code, Cursor, Aider, Codex, or Cline, ultimately sends requests to an LLM inference engine. The choice between vLLM and SGLang determines the latency, throughput, and cost of every tool call, every code completion, and every edit application.
Coding agents have a specific traffic pattern that favors SGLang. The system prompt (typically 1,000-3,000 tokens of tool definitions and instructions) is identical across every request in a session. The conversation history grows but shares a prefix with every previous turn. RAG context from the codebase often overlaps between requests when the agent is working in the same area of the code. RadixAttention exploits all three patterns.
But the inference engine is one layer in the stack. Above it sits the model (what generates the code), and above that sits the edit application layer (what applies the generated code to your files). A model that generates perfect code through a slow inference engine will outperform a fast engine running a weaker model.
Morph Works With Any Inference Backend
Morph's fast apply model and inference APIs sit above the inference engine layer. Whether your backend runs vLLM, SGLang, TensorRT-LLM, or something else entirely, Morph's APIs handle code edit application, model routing, and context management. The choice of inference engine is yours. What matters is the quality of the model and the speed of the edit application, not which serving framework runs underneath.
Frequently Asked Questions
Is SGLang faster than vLLM?
On H100 GPUs, SGLang produces 16,200 tokens/sec vs vLLM's 12,500 (29% faster). On prefix-heavy workloads, the gap widens to 6.4x due to RadixAttention's KV cache reuse. For batch processing of unique prompts with no prefix overlap, both engines converge to similar throughput.
What is the difference between PagedAttention and RadixAttention?
PagedAttention (vLLM) manages KV cache memory in fixed-size blocks and frees them after each request. RadixAttention (SGLang) keeps the KV cache in an LRU radix tree and reuses it when new requests share a prefix with previous ones. PagedAttention is simpler. RadixAttention is faster when prefix overlap exists.
Which engine is better for DeepSeek V3 and R1?
SGLang. It is the officially preferred framework for DeepSeek models, with day-one support and optimizations including multi-token prediction (1.8x decode speedup), expert parallelism with EPLB load balancing, and prefill-decode disaggregation tuned for MoE architectures. SGLang achieves 3.1x faster DeepSeek V3 inference than vLLM.
Does vLLM support TPUs and non-NVIDIA hardware?
Yes. vLLM supports Google TPUs (v4-v6e), AWS Trainium/Inferentia, Intel Gaudi, AMD GPUs, Intel XPU, Arm CPUs, and PowerPC CPUs. SGLang primarily targets NVIDIA and AMD GPUs, with experimental TPU support via SGLang-Jax. For non-NVIDIA infrastructure, vLLM is the clear choice.
Which engine should I use for coding agents?
SGLang, because coding agents generate traffic patterns (repeated system prompts, shared context, multi-turn sessions) that RadixAttention is designed to exploit. Cursor uses SGLang for this reason. That said, the inference engine is one layer in the stack. Morph's APIs work with any backend, so the model quality and edit application speed matter more than the serving framework.
Can I switch between vLLM and SGLang easily?
Both engines expose an OpenAI-compatible API endpoint. If your application calls the standard /v1/chat/completions or /v1/completions endpoint, switching between them requires changing only the server URL. Model weights are interchangeable. No code changes are needed in the application layer.
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