vLLM vs SGLang 2026: We Benchmarked Both on H100s

vLLM vs SGLang 2026: We Benchmarked Both on H100s

Hugging Face put TGI into maintenance mode in December 2025 and now points teams toward vLLM or SGLang for new deployments. If you're standing up an inference stack today, the real question isn't "should I move off TGI?", it's which of these two engines actually fits your workload.

Quick Summary

Choose vLLM if you want the broadest hardware support, the largest community, and a battle-tested path to production across AWS, GCP, and Azure.

Choose SGLang if your workload is heavy on multi-turn conversations, structured outputs, or prefix-heavy pipelines like RAG, and you're comfortable with a smaller ecosystem.

Now let's unpack where each engine actually pulls ahead.

How Did We Get Here? TGI's Exit

Text Generation Inference (TGI) carried the Hugging Face ecosystem for years, but as of December 2025 it only accepts bug fixes, no new features. Hugging Face's own Inference Endpoints now default to vLLM, with SGLang as an alternative.

That leaves two real contenders for self-hosted LLM serving. Both are open-source, both speak the OpenAI API, and both run on NVIDIA GPUs. The differences show up under load.

Verdict: Both vLLM and SGLang are production-ready TGI replacements. If you're migrating, either one is a safe bet, the rest of this guide helps you pick which.

Throughput and Latency Benchmarks

Benchmarks vary by model, GPU, and concurrency, so here are numbers from independent tests on the same hardware. The following data comes from Spheron's H100 benchmarks using Llama 3.3 70B Instruct in FP8 and PremAI's tests with Llama 3.1 8B.

Llama 3.3 70B on H100 (FP8)

Llama 3.1 8B on H100

On smaller models the gap widens. PremAI measured SGLang at roughly 16,200 tok/s versus vLLM at 12,500 tok/s, a 29% throughput advantage for SGLang. LMDeploy matched SGLang here, but that's a separate conversation.

What the Numbers Mean

At 70B scale the delta is modest (3-5%). At 8B scale it's significant. The pattern makes sense: SGLang's RadixAttention pays off more when prefill is a larger fraction of the total cost, which happens with smaller models and shorter outputs.

Tail latency tells a similar story. SGLang's TTFT p95 was consistently 5-8% lower than vLLM at every concurrency level tested. If you're building a real-time chat interface where every 50ms matters, that gap compounds across users.

Verdict: SGLang wins on raw throughput, especially for smaller models. vLLM is close at 70B+ scale. For most production workloads the difference is single-digit percentages, meaningful at scale, but not a dealbreaker either way.

Prefix Caching: RadixAttention vs Automatic Prefix Caching

Both engines cache KV computations for repeated prefixes, but the mechanisms differ in ways that matter for certain workloads. If you're already familiar with prompt caching at the API level, think of this as the server-side version.

vLLM uses block-level hashing. It splits KV cache into fixed-size blocks, hashes them, and looks up matches on new requests. Predictable, efficient, and easy to reason about, but you need consistent block boundaries for cache hits.

SGLang uses a radix tree indexed at the token level. It automatically discovers shared prefixes across requests without manual configuration. If 50 users send messages in the same conversation thread, SGLang finds and reuses the common prefix automatically.

Where It Actually Matters

RunPod benchmarked multi-turn conversations and found SGLang delivered ~30-31 tok/s consistently under high concurrency, while vLLM dropped from 22 to 16 tok/s as cache pressure increased. That's a meaningful gap for chatbot and agent workloads.

For batch inference on templated prompts, where every request uses the same system prompt, vLLM's approach works fine. The cache boundaries align naturally with your template structure.

Verdict: SGLang wins for dynamic, multi-turn workloads. vLLM is perfectly adequate for batch inference and templated prompts where prefixes are predictable.

Structured Outputs

If you need JSON schema enforcement or constrained generation, this section matters a lot. Both engines support structured outputs through grammar backends like XGrammar and LLGuidance, but the performance story is very different.

SqueezeBits ran detailed benchmarks and found that vLLM shows significant throughput degradation with guided decoding enabled, especially at batch size 8 and above. SGLang, by contrast, overlaps mask generation with the GPU inference step, keeping overhead minimal.

Repetitive vs Dynamic Schemas

The backend choice also matters:

Without structured enforcement, outputs fall to ~61% correctness on complex schemas. With it, correctness jumps 20-25 percentage points. So this isn't optional for production agent workflows, and the engine you pick determines how much throughput you sacrifice.

Verdict: SGLang wins for structured outputs. If your pipeline relies on JSON schema enforcement (and most agent workflows do), SGLang's overlapped approach means you don't pay a throughput tax.

Multi-LoRA and Fine-Tuned Model Serving

Both engines support serving multiple LoRA adapters from a single base model, which is essential if you fine-tune models for different tenants or tasks.

SGLang treats multi-LoRA as a first-class feature with native batching, requests targeting different adapters can share the same batch. vLLM supports it too, but SGLang's implementation has been slightly more polished in recent releases.

The practical difference? If you're serving 5-10 LoRA adapters off one Llama 70B base model, both work. If you're running 50+ adapters with heterogeneous traffic patterns, SGLang's native batching handles the scheduling more gracefully.

Verdict: SGLang has a slight edge for multi-LoRA at scale. For a handful of adapters, both engines work equally well.

Speculative Decoding

Both engines support speculative decoding, which uses a small "draft" model to predict tokens that the main model then verifies in parallel. The result is 2-3x faster inference for memory-bound scenarios.

vLLM recently introduced Unified Parallel Drafting, and speculative decoding now works alongside structured outputs. SGLang's implementation is similar in capability, with slightly better performance at moderate concurrency levels.

The real differentiator isn't the engine, it's whether speculative decoding fits your workload. It helps most with long outputs from large models where the bottleneck is memory bandwidth, not compute.

Verdict: Tie. Both engines deliver comparable speculative decoding speedups.

Hardware Support and Deployment

This is where vLLM pulls ahead significantly.

vLLM

• NVIDIA GPUs (A100, H100, H200, B200)
• AMD GPUs (MI250, MI300X)
• Intel GPUs (via vllm-xpu-kernels)
• AWS Trainium and Inferentia
• Google TPUs
• Mature Kubernetes docs with Helm charts, startup/readiness/liveness probes
• NVIDIA Container Toolkit integration out of the box

SGLang

• NVIDIA GPUs (A100, H100, H200, B200)
• AMD GPUs (MI300X, via ROCm)
• Docker-first deployment
• Kubernetes is possible but less documented

If you're deploying on anything other than NVIDIA or AMD, vLLM is your only option. On AWS specifically, Trainium support means you can cut inference costs significantly, and SGLang can't touch that hardware.

For teams running on standard NVIDIA GPUs, the deployment story is similar. Both provide Docker images and OpenAI-compatible endpoints. vLLM just has more battle-tested production guides and community-contributed Helm charts.

If you're exploring tools for running LLMs locally or want a broader view of self-hosted inference, both engines support local deployment on consumer GPUs too, though they're designed for datacenter hardware.

Verdict: vLLM wins on hardware breadth and deployment maturity. SGLang is fine if you're on NVIDIA or AMD. Anywhere else, vLLM is the only choice.

Disaggregated Serving

Both engines support separating prefill (compute-heavy) from decode (memory-heavy) into different worker pools. This lets you scale each phase independently, more prefill workers during prompt-heavy bursts, more decode workers for long generation.

SGLang supports Mooncake and NIXL as transfer backends for disaggregation and has published results showing 2.7x higher decoding throughput on NVIDIA GB200 NVL72 clusters. vLLM's disaggregated serving is also functional, though less prominently documented.

This feature matters most at very large scale (96+ GPUs). If you're running a handful of GPUs, you probably don't need it yet.

Verdict: SGLang has a slight edge on disaggregated serving maturity. Both support it; SGLang has published more real-world results.

When to Use Each: Decision Framework

The honest answer for many teams: try both. They're both open-source, both expose the same OpenAI API, and switching between them is a container swap. Run your actual workload against each for a day and compare the metrics that matter to you.

If you're routing traffic across multiple inference backends, an LLM gateway can sit in front of either engine and handle failover, rate limiting, and observability.

How Techsy Approaches Inference Server Selection

When we help teams deploy LLM-powered features, the inference engine choice comes down to three questions:

1. What hardware are you locked into? If it's Trainium or TPUs, it's vLLM. Everything else, both work.
2. What's your workload shape? Multi-turn chat and agent loops favor SGLang's prefix caching. Batch processing and simple completions are fine on either.
3. How much ops capacity do you have? vLLM's larger community means more StackOverflow answers and Helm charts when something breaks at 3 AM.

We've run production workloads on both. They're genuinely close. The right answer depends on your constraints, not on one being "better" in the abstract.

Need help choosing or deploying an inference server? Reach out to us, we'll assess your workload and recommend the right stack.

Choosing a tool is the easy half. Getting it to run reliably inside a real product is where most teams stall, and that is exactly what our AI integration team builds for clients, from RAG pipelines to custom agents.

Frequently Asked Questions

Is SGLang faster than vLLM?

On smaller models (7B-8B), SGLang shows roughly 29% higher throughput on H100 GPUs. On 70B+ models, the gap narrows to 3-5%. SGLang also has lower tail latency (TTFT p95) at all concurrency levels tested.

Can I use vLLM and SGLang with the OpenAI API format?

Yes. Both expose OpenAI-compatible endpoints out of the box. You can swap one for the other without changing your client code. Your /v1/chat/completions calls work identically on either.

Why did Hugging Face deprecate TGI?

TGI entered maintenance mode in December 2025. Hugging Face decided to contribute to vLLM and SGLang instead of maintaining a separate inference engine. TGI still works for existing deployments, but no new features are coming.

Does SGLang support NVIDIA and AMD GPUs?

SGLang supports NVIDIA GPUs (A100, H100, H200, B200) and AMD GPUs (MI300X via ROCm). It does not support Intel GPUs, AWS Trainium, Inferentia, or Google TPUs. vLLM has broader hardware coverage.

What is RadixAttention and why does it matter?

RadixAttention is SGLang's prefix caching mechanism. It stores KV cache entries in a radix tree indexed at the token level, automatically discovering shared prefixes across requests. This makes multi-turn conversations and RAG pipelines significantly faster because repeated context doesn't need to be recomputed.

Which engine is better for structured JSON outputs?

SGLang. It overlaps grammar mask generation with GPU inference, so structured output enforcement barely impacts throughput. vLLM shows noticeable degradation at batch sizes of 8 and above when guided decoding is enabled.

Can I serve multiple LoRA adapters from one base model?

Both engines support multi-LoRA serving. SGLang treats it as a native feature with batching across different adapters in the same request batch. vLLM supports it too, but SGLang's scheduling is more efficient at high adapter counts.

What is disaggregated prefill/decode serving?

It means running the prefill phase (processing the prompt) on separate GPU workers from the decode phase (generating tokens). Prefill is compute-bound; decode is memory-bound. Separating them lets you scale each independently. Both engines support this, with SGLang having more published production results.

How do I migrate from TGI to vLLM or SGLang?

Since all three expose OpenAI-compatible APIs, migration is mostly a container swap. Point your Docker Compose or Kubernetes deployment to the new image, adjust model loading flags, and update health check endpoints. Client code stays the same.

Should I use vLLM or SGLang for a RAG pipeline?

SGLang is the stronger choice for RAG. Its RadixAttention automatically caches and reuses the long system prompts and document contexts that RAG pipelines repeatedly send. vLLM's block-level caching works too, but you'll see better cache hit rates with SGLang's token-level approach when document chunks vary slightly across requests.

Final Verdict

SGLang wins more categories, but vLLM's advantages, hardware breadth and ecosystem maturity, are the kind of things that matter at 3 AM when a node goes down.

If you're on NVIDIA hardware and your workload involves multi-turn conversations, agents with structured outputs, or RAG pipelines with shared prefixes, start with SGLang. You'll get better throughput and lower latency where it counts.

If you need multi-cloud flexibility, non-NVIDIA hardware support, or the comfort of the largest open-source LLM serving community, start with vLLM. It's the safer default that'll serve most teams well.

Either way, both engines are excellent and improving fast. Pick one, deploy it, measure your actual workload, and switch if the numbers tell you to. The OpenAI-compatible API makes that switch painless.

Sources

• Spheron H100 Benchmarks: vLLM vs TensorRT-LLM vs SGLang
• PremAI: vLLM vs SGLang vs LMDeploy Benchmarks
• SqueezeBits: Guided Decoding Performance on vLLM and SGLang
• RunPod: SGLang vs vLLM KV Cache Reuse
• SGLang Official Documentation
• vLLM Official Documentation