SGLang's RadixAttention gives it a 29% throughput edge over vLLM on H100 GPUs (16,200 vs 12,500 tokens/sec) and up to 6.4x gains on prefix-heavy workloads like RAG and multi-turn chat. vLLM wins on ecosystem breadth—broader hardware support (TPUs, Trainium, Gaudi), encoder-decoder models, and a 3x larger contributor base. Default to SGLang for DeepSeek deployments, conversational AI, and structured output. Default to vLLM for batch processing with unique prompts, multi-hardware environments, or when you need the largest model compatibility.
Three months ago we migrated a client's multi-turn chatbot from vLLM to SGLang. Same model, same H100 cluster, same traffic pattern. Throughput jumped 34%, and their GPU bill dropped by $12,000 a month. The only change was the inference engine.
That result isn't universal—and that's the problem with every "SGLang vs vLLM" article that declares a winner without context. The 29% throughput gap that benchmarks show on standard workloads can shrink to nearly zero on unique-prompt batch jobs, or balloon to 6x on prefix-heavy RAG pipelines. The right engine depends entirely on your workload shape.
With DeepSeek V4 officially endorsing SGLang and vLLM pushing into disaggregated prefill and Blackwell optimization, both engines are evolving fast. Here's where each one actually wins in 2026—with the numbers to back it up.
Architecture: PagedAttention vs RadixAttention
The core difference between vLLM and SGLang comes down to how they manage the KV cache—the GPU memory structure that stores attention computations for each token in a sequence. This is the bottleneck that determines throughput, latency, and cost at scale.
How vLLM's PagedAttention Works
vLLM borrowed a concept from operating systems: virtual memory paging. Instead of allocating one large contiguous block of GPU memory per request (which wastes 60-80% of capacity), PagedAttention 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, its blocks are immediately freed for reuse. The result: GPU memory waste drops from 60-80% to under 4%, and vLLM can serve significantly more concurrent requests on the same hardware. Combined with continuous batching—processing new requests at the iteration level rather than waiting for batch windows to complete—PagedAttention made vLLM the production default when it launched. For a deeper look at how vLLM compares to other serving frameworks, see our vLLM vs Ollama vs TensorRT-LLM comparison.
How SGLang's RadixAttention Works
SGLang starts with the same paged memory management but adds a critical insight: don't throw away the KV cache after a request completes. RadixAttention maintains an LRU cache of KV computations in a radix tree data structure. When a new request arrives, the runtime performs a prefix match against the tree. If the new request shares a prefix with a previous one—which happens constantly in multi-turn chat, RAG over shared documents, and few-shot prompting—SGLang reuses the cached computation instead of recomputing it from scratch. The cache-aware scheduler amplifies this advantage. Instead of first-in-first-out processing, SGLang prioritizes requests with longer shared prefixes, approximating a depth-first traversal of the radix tree that maximizes cache hits. In practice, this produces cache hit rates of: The practical implication: if 10 users query the same 10,000-word document in a RAG pipeline, SGLang processes those 10,000 words once. vLLM processes them 10 times.
- Few-shot learning: 85-95%
- Multi-turn chat: 75-90%
- Code analysis: 60-80%
- Mixed production traffic: 50-70%
Architecture Summary
| Feature | vLLM (PagedAttention) | SGLang (RadixAttention) |
|---|---|---|
| Memory management | Paged blocks, <4% waste | Paged blocks + radix tree cache |
| Cache reuse | Per-request only | Cross-request via prefix matching |
| Scheduling | Continuous batching (FIFO) | Cache-aware (prefix-prioritized) |
| Memory overhead | Lower baseline | Higher (retains cache tree) |
| Best scenario | Unique prompts, batch jobs | Shared prefixes, multi-turn |
Benchmark Comparison: The Numbers That Matter
Raw throughput numbers mean nothing without workload context. Here's how both engines perform across the scenarios that actually matter for production deployments.
The 29% total throughput gap is the headline, but the output token throughput tells the real story—SGLang generates output tokens more than twice as fast, which is what users actually perceive as "speed."
As Spheron's analysis noted: "RadixAttention's benefit disappears for unique-prompt workloads." If you're running batch content generation where every prompt is different, vLLM performs equally well—and its broader ecosystem may tip the decision.
Runpod's testing confirmed this pattern: on single-turn unique prompts with DeepSeek-R1-Distill-Llama-70B, vLLM actually outperformed SGLang (60 tok/s vs 52.7 tok/s). But the moment cache hits came into play, SGLang pulled ahead (35 tok/s with cache vs 32.8 tok/s).
Standard Throughput on H100
Running Llama 3.1 8B on a single H100 80GB GPU, benchmarks from Prem AI and LocalAIMaster show a consistent pattern:
Concurrency Behavior
Under increasing concurrent load, the engines diverge further. SGLang maintains 30-31 tokens per second per request at high concurrency, while vLLM drops from 22 to 16 tokens per second. SGLang's cache-aware scheduling keeps individual request quality stable even as total load increases.
DeepSeek V3/V4 Performance
This is where SGLang's partnership with DeepSeek pays off. On DeepSeek V3 specifically, SGLang achieves 3.1x faster inference than vLLM, thanks to optimized MLA (Multi-head Latent Attention) backends including FlashAttention3, FlashInfer, FlashMLA, and CutlassMLA. SGLang also supports Multi-Token Prediction via EAGLE speculative decoding for DeepSeek models, delivering a 1.8x decode speedup at batch size 1 and 1.5x at batch size 32 on H200 GPUs. If you're deploying any DeepSeek model—including the V4 that's reshaping open-source AI—SGLang isn't just faster, it's the officially recommended engine.
Prefix-Heavy Workloads: Where SGLang Dominates
On workloads with significant prefix sharing—RAG pipelines, few-shot classification, multi-turn agents—RadixAttention delivers up to 6.4x throughput improvement over engines without cross-request caching. This is SGLang's strongest differentiator and the scenario where vLLM's architecture simply can't compete.
Where vLLM Wins
Benchmarks from Spheron testing Llama 3.3 70B FP8 on H100 show that when prompts are unique (no shared prefixes), the gap shrinks to near-zero:
| Metric | SGLang | vLLM | Delta |
|---|---|---|---|
| Total throughput | ~16,200 tok/s | ~12,500 tok/s | SGLang +29% |
| Output token throughput | 894 tok/s | 413 tok/s | SGLang +117% |
| Time to first token (TTFT) | 79 ms | 103 ms | SGLang 23% faster |
| Inter-token latency (ITL) | 6.0 ms | 7.1 ms | SGLang 15% faster |
| Concurrency | vLLM (tok/s) | SGLang (tok/s) | Delta |
|---|---|---|---|
| 1 | 120 | 125 | ~4% |
| 10 | 650 | 680 | ~5% |
| 50 | 1,850 | 1,920 | ~4% |
| 100 | 2,400 | 2,460 | ~2% |
Ecosystem and Production Readiness
Performance isn't the only decision factor. The surrounding ecosystem—hardware support, model compatibility, community, and production tooling—determines how fast you ship and how easily you maintain.
vLLM has a 3x larger contributor base and significantly faster community support. If you hit an edge case at 2 AM, vLLM's larger community is more likely to have a workaround documented. SGLang's community is growing rapidly—xAI (Grok 3), Microsoft Azure, Cursor, Oracle Cloud, and LinkedIn all run SGLang in production—but it's still catching up on breadth.
Community and Maturity
Hardware Support
vLLM supports the broadest hardware range in the inference engine space: NVIDIA GPUs, AMD CPUs and GPUs, Intel CPUs and GPUs, Google TPUs (v4 through v6e), AWS Trainium and Inferentia, Intel Gaudi, and Arm processors. vLLM also has early Blackwell/GB200 optimization, demonstrating 26,200 prefill tokens per second on DeepSeek-style MoE models. SGLang primarily targets NVIDIA GPUs with growing AMD GPU support through the DeepSeek collaboration. If your infrastructure includes TPUs, Trainium, or non-NVIDIA accelerators, vLLM is currently the only viable option.
Model Compatibility
vLLM supports the broadest range of model architectures: decoder-only, encoder-decoder (T5, BART), mixture-of-experts, embedding models, and multimodal models. SGLang covers decoder-only LLMs, multimodal, embedding, reward, and diffusion models—but does not support encoder-decoder architectures. For most LLM serving use cases this gap doesn't matter, but if your pipeline includes T5-based models, it's a dealbreaker for SGLang.
Production Adoption
Both engines serve trillions of tokens daily in production. SGLang powers xAI's Grok 3, Microsoft Azure endpoints, LinkedIn's AI features, Cursor's code completion, and runs across 400,000+ GPUs. vLLM remains the default backend for most cloud API endpoints and OpenAI-compatible serving deployments. For our own client deployments, we've used both—the choice always comes down to workload shape, not engine quality.
| Metric | SGLang | vLLM |
|---|---|---|
| GitHub stars | ~25K | ~75K |
| Contributors | ~600 | ~2,400 |
| License | Apache 2.0 | Apache 2.0 |
| Issue response time | 3-5 days | 12 hours - 3 days |
Structured Output: SGLang's Hidden Advantage
If your application depends on structured JSON output—and in 2026, most production AI applications do—SGLang has a measurable edge.
SGLang uses a compressed finite state machine for constrained decoding that's roughly 3x faster than standard guided decoding approaches. The key innovation: it overlaps per-step grammar mask generation with the LLM's forward pass, hiding the computational cost of constraint enforcement.
Without constrained decoding, typical LLM JSON compliance sits at 90-94%. With SGLang's compressed FSM, compliance reaches 96-98.2%. For applications where a malformed JSON response means a failed API call or a broken user experience, that 4-8 percentage point improvement eliminates entire categories of error handling. For more on making LLM outputs reliable, see our guide on fixing slow LLM latency in production.
Decision Framework: Which Engine for Your Workload
Skip the benchmarks and start here. Your workload pattern determines the right choice.
Choose SGLang When
- Multi-turn chat or conversational AI. RadixAttention gives you 10-20% bonus throughput from cache reuse across turns. Every conversation that continues reuses prior computation.
- RAG pipelines with shared documents. If multiple users query the same document corpus, SGLang processes shared context once. The savings compound with user count.
- DeepSeek model deployments. SGLang is the officially endorsed engine with optimized MLA backends and day-0 support for every DeepSeek release.
- Structured JSON output at scale. The 3x faster constrained decoding and 96-98% compliance rate matters when you're serving millions of structured API responses daily.
- AI agents with iterative reasoning. Agent loops that repeatedly call the same tools with overlapping context benefit directly from prefix caching.
- Cost optimization is the priority. The 29% throughput advantage on standard workloads translates to roughly $15,000/month savings at 1 million daily requests on H100s.
Choose vLLM When
- Batch content generation with unique prompts. If every prompt is different, RadixAttention provides zero benefit and vLLM's ecosystem advantages win.
- Non-NVIDIA hardware. TPUs, Trainium, Gaudi, Intel GPUs, or Arm—vLLM is the only option with broad accelerator support.
- Encoder-decoder models. T5, BART, or similar architectures require vLLM. SGLang doesn't support them.
- Rapid prototyping and smaller teams. vLLM's 3x larger community, faster issue response, and broader documentation lower the getting-started friction.
- Blackwell/GB200 early adoption. vLLM has demonstrated early optimization on next-gen NVIDIA hardware with disaggregated prefill.
- Maximum model compatibility. If you need to serve a wide variety of model architectures through a single engine, vLLM covers more ground.
The Hybrid Approach
For production systems with mixed workloads, running both engines behind a routing layer is increasingly common. Route multi-turn conversations and RAG queries to SGLang; route batch jobs and unique-prompt workloads to vLLM. Both support OpenAI-compatible APIs, so the application layer stays identical. For more on routing strategies, see our guide on model routing to reduce API costs.
Migration and Setup
Both engines install in minutes and serve an OpenAI-compatible API, which means switching between them is a base-URL change in your application code.
pip install sglang[all] python -m sglang.launch_server --model-path meta-llama/Llama-3.1-8B-Instruct --port 8000
pip install vllm vllm serve meta-llama/Llama-3.1-8B-Instruct --port 8000
SGLang Quick Start
vLLM Quick Start
The API endpoint for both is identical—/v1/chat/completions—so any OpenAI SDK client works without modification. The real migration work is in tuning: SGLang benefits from adjusting cache retention settings for your specific prefix patterns, while vLLM benefits from tuning batch sizes and memory allocation for your concurrency profile.
What We Recommend
After deploying both engines across client projects ranging from internal knowledge assistants to high-volume API platforms, our default recommendation is:
Start with SGLang if your workload involves any multi-turn interaction, shared document context, or structured output. The 29% throughput advantage is real, and RadixAttention's benefits compound as your usage patterns develop shared prefixes—which most production applications naturally do.
Start with vLLM if you're in a multi-hardware environment, need encoder-decoder support, or are running pure batch workloads with no prefix overlap.
Run both if you have distinct workload types and the operational capacity to manage two inference backends. The OpenAI-compatible API makes routing between them trivial.
The inference engine war isn't about which is "better"—it's about which architecture matches your memory access patterns. Get that right, and everything else follows. For a broader look at how model selection fits into production AI architecture, explore our LLMs & Models pillar page.
Frequently Asked Questions
Quick answers to common questions about this topic
On H100 GPUs running Llama 3.1 8B, SGLang achieves roughly 16,200 tokens per second compared to vLLM's 12,500—a 29% throughput advantage. The gap widens dramatically on prefix-heavy workloads (up to 6.4x) because RadixAttention reuses cached KV computations across requests. However, vLLM can match or beat SGLang on single-turn unique-prompt workloads where prefix caching provides no benefit.
