SGLang vs vLLM: LLM Inference Engine Comparison

Contents

• SGLang vs vLLM Overview
• Throughput Benchmarks
• Feature Comparison
• Structured Output Support
• Prefix Caching and Context Reuse
• Deployment and Operations
• Use Case Recommendations
• Architecture Deep Dive
• Migration Path
• FAQ
• Cost Per Million Tokens: Inference Economics
• Real-World Deployment Scenarios
• Engineering Complexity: Code Examples
• The Prefix Caching Deep Dive
• Community and Support
• Related Resources
• Sources

SGLang vs vLLM Overview

SGLang: 16,215 tok/s. vLLM: 12,553 tok/s. SGLang wins 29%. But vLLM owns the ecosystem and maturity advantage.

SGLang shines on structured output and multi-turn caching. vLLM is simpler to deploy, battle-tested everywhere.

Throughput Benchmarks

Raw Throughput (H100, March 2026)

29% gap. Scheduler overhead kills vLLM between prefill and decode. SGLang's custom CUDA kernels exploit tensor cores better. SGLang's prefill-decode split is more granular.

100M tokens/day? SGLang: 1.7 hours. vLLM: 2.2 hours. Compounding cost savings.

Latency (Time-to-First-Token, TTFT)

SGLang cuts TTFT by 15%. Chat apps and RAG need this. vLLM's latency works for batch.

Feature Comparison

SGLang Strengths

Structured output: constrained JSON, function calls, regex. FSM runs in-kernel, no post-processing.

Multi-turn caching with RadixAttention. Share a 1k-token system prompt across 10 requests, cache once. Cache hit: 75-95%.

Multi-LoRA batching: serve multiple adapters on one model.

vLLM Strengths

Ecosystem. vLLM integrates with LangChain, LlamaIndex, and OpenAI-compatible REST APIs out of the box. Most RAG frameworks default to vLLM.

Operator simplicity. Deployment is straightforward. Standard Python. Easy Docker containerization. Battle-tested at teams like Databricks, ServiceTitan, and others at scale (trillions of tokens daily).

Quantization ecosystem. vLLM supports GPTQ, AWQ, FP8, and INT4 natively. More third-party quantization frameworks are tested against vLLM than SGLang.

Multi-LoRA isn't required. If not serving multiple fine-tunes per model, this is a non-issue.

Structured Output Support

SGLang: Native Constrained Decoding

SGLang accepts regex patterns, JSON schemas, or custom FSMs. During decode, the model is constrained to generate only tokens that keep the output valid.

Example:

response = engine.forward(
 "Extract person: [NAME: <name>, AGE: <age>]",
 constraints=regex(r"\[NAME: \w+, AGE: \d+\]")
)

Output is guaranteed valid. No post-processing. Decoding speed is baseline.

vLLM: Post-processing Validation

vLLM generates tokens freely, then validates and auto-corrects the output. If a model generates invalid JSON, vLLM re-tokenizes and regenerates.

Latency overhead: 20-50ms depending on correction complexity. More common than people expect: LLM outputs are sometimes sloppy.

For structured generation workloads (form filling, code generation, API calls), SGLang is superior. The reduction in rework and latency matters at scale.

Prefix Caching and Context Reuse

SGLang: RadixAttention

RadixAttention is a tree-based KV cache that handles overlapping prefixes automatically. If requests A and B both start with the same 500-token prompt, RadixAttention caches it once.

Impact: In a RAG system answering 100 questions about the same document, the document embedding is cached across all 100 requests. Cost per query drops 10-20% compared to recomputing.

vLLM: Basic Prefix Caching (Recent)

vLLM added prefix caching in v0.5 but it's less efficient than RadixAttention. Requires explicit API calls to cache prefixes. Less automatic.

For conversational AI and RAG pipelines, SGLang's RadixAttention is a clear win. For one-off requests with unique prefixes, the benefit is zero.

Deployment and Operations

SGLang Deployment

Setup: Python, pip install sglang. Standard NVIDIA CUDA stack.

Maturity: v0.3.x as of March 2026. Still actively evolving. Fewer long-term production deployments than vLLM (though companies like Cursor and LinkedIn use it).

Scaling: Tensor parallelism, pipeline parallelism. No Kubernetes operator built-in (though the community is building OME, an open operator that works with both).

Downtime risk: SGLang is newer. Breaking API changes are possible. Monitor releases before upgrading.

vLLM Deployment

Setup: Python, pip install vllm. Stable CUDA ABI.

Maturity: v0.8.x as of March 2026. Thousands of production deployments. Battle-tested. Changes are backward-compatible.

Scaling: Distributed serving with tensor/pipeline parallelism. Kubernetes integration exists (though not officially maintained). Most cloud providers have vLLM templates.

Downtime risk: Minimal. vLLM treats stability as a feature. Updates rarely break existing deployments.

For risk-averse teams or large deployments, vLLM is safer. For teams willing to optimize for bleeding-edge performance, SGLang is worth the operational complexity.

Use Case Recommendations

Use SGLang If.

1. Structured generation is core. JSON, function calls, code generation. Constrained decoding matters.
2. Multi-turn is the workload. Chatbots, RAG, agents. RadixAttention cache hit rates are 75-95%.
3. Cost-per-token is the metric. The 29% throughput gap compounds into savings on long-running services.
4. Teams are fine-tuning with LoRA. Multi-LoRA batching on a single model is a real advantage.

Use vLLM If.

1. Teams need production stability. Kubernetes, Helm, CI/CD integration. Thousands of known deployments.
2. Team is small or junior. vLLM's simplicity is underrated. Less knob-turning required.
3. Teams are already invested in the ecosystem. LangChain, LlamaIndex, OpenAI APIs. vLLM is the default.
4. Throughput under 10K tok/s. The gap matters at scale. At 1K tok/s (research or small service), both are fine.
5. Quantization is core. vLLM has more third-party quantization support.

A Third Option: LMDeploy

LMDeploy's TurboMind engine matches SGLang in throughput (16,132 tok/s) while maintaining vLLM-like stability. C++-based. Fewer Python dependencies. Worth considering for high-throughput batch processing.

Architecture Deep Dive

vLLM Architecture

Request Queue
 ↓
Batch Scheduler (assigns prefill + decode phases)
 ↓
Prefill Phase (attend to full input)
 ↓
Decode Phase (generate tokens one-by-one)
 ↓
Output Buffer

The scheduler batches requests across both phases. Latency: request spends time waiting for prefill, then waits again for decode slot.

SGLang Architecture

Request Queue
 ↓
Zero-Overhead Scheduler (fine-grained token scheduling)
 ↓
Prefill (chunked, interleaved with decode)
 ↓
Decode (prioritized, interleaved with prefill)
 ↓
Output Buffer (with RadixAttention KV tree)

SGLang disaggregates prefill and decode: each token from decode can interleave with prefill of the next request. Reduces stalls. KV state is stored in a tree (Radix tree, hence RadixAttention) for efficient sharing.

This is why SGLang is faster. More efficient use of GPU compute.

Migration Path

vLLM → SGLang

Both support OpenAI-compatible REST APIs. Swap the backend URL. If using raw Python, API is similar but not identical. Budget 1-2 weeks for integration testing.

SGLang → vLLM

More disruptive. Lose RadixAttention caching and structured output. Implement post-processing validation for structured outputs.

FAQ

Is SGLang production-ready?

As of March 2026, yes. Cursor, LinkedIn, and other high-traffic services use it. But it's younger than vLLM. Monitor releases closely.

Can I use both simultaneously?

Yes. Run vLLM for simple text generation. Route structured generation requests to SGLang. Use a load balancer or async queue. Adds operational complexity.

Which is easier to deploy?

vLLM. Docker, Kubernetes, cloud provider templates exist. SGLang is catching up.

Does throughput difference matter for my workload?

At <1,000 tok/s (research or single-user chatbot), no. At >10,000 tok/s (production API), the 29% gap saves real money. At scale, SGLang's cost-per-token is 15-20% lower.

Should I use RadixAttention if I'm not doing RAG?

Only if you have multi-turn conversations with shared prefixes. Single-turn requests get no benefit.

Is structured output worth switching to SGLang?

Depends. If generating JSON responses, yes. Constrained decoding is faster and always-valid. If post-processing is acceptable, vLLM works.

Cost Per Million Tokens: Inference Economics

From a purely cost perspective, both engines have identical hardware costs. But throughput differences cascade into operational expenses.

Example: 100M Token/Day Service

vLLM at 12,553 tok/s:

• Time to process: 100M ÷ 12,553 = 7,961 seconds = 2.2 hours
• H100 cost (RunPod): $2.69/hr × 2.2 = $5.92

SGLang at 16,215 tok/s:

• Time to process: 100M ÷ 16,215 = 6,168 seconds = 1.7 hours
• H100 cost: $2.69/hr × 1.7 = $4.57

Cost difference: $1.35 per 100M tokens. Over a year (36.5B tokens): ~$493 saved on compute alone.

Multi-region deployment complicates the math: need multiple H100 clusters, redundancy costs. But the per-token cost advantage accrues.

Real-World Deployment Scenarios

Scenario 1: RAG System (e-commerce Q&A)

Customer asks 50-word question about product. System retrieves 3 similar products (1,500 tokens context) and generates 200-word answer.

Per-request tokens:

• Context (retrieval): 1,500
• Question embedding: 50
• Answer generation: 200
• Total: 1,750 tokens per request

Per-request latency:

• Prefill (context + question): 1,550 tokens at 4,000 tok/s = 387 ms
• Decode (answer, 200 tokens): 200 tokens at varying speed (each token computed serially) = 200 × (1 sec ÷ 1,000 tok/s avg) = 200 ms
• Total: ~600 ms

vLLM vs SGLang:

• vLLM TTFT: 50 ms (time to first token)
• SGLang TTFT: 35 ms (15 ms faster, RadixAttention amortizes context)
• Streaming user perception: user feels SGLang is more responsive

At 1,000 requests/day, vLLM serves 600K tokens/day on one H100. SGLang serves 800K tokens/day (same H100). At scale (100K requests/day), SGLang saves one full H100 ($2.69/hr × 24 = $64.56/day = $23,564/year).

Scenario 2: Batch Code Generation

Generate 10,000 code snippets, each ~500 tokens. Prompt is fixed (4,000 tokens system prompt). Total: 50M input tokens (system prompt + queries) + 5M output tokens.

RadixAttention hit rate: 99% (system prompt cached, never recomputed).

vLLM:

• Recomputes 4,000-token system prompt for each of 10,000 requests
• Wasted compute: 40M tokens on recalculating identical context
• Actual unique compute: 10M (queries) + 5M (outputs) = 15M
• Overhead: 40M ÷ 15M = 2.67x inefficiency

SGLang:

• Caches 4,000-token system prompt once (RadixAttention tree)
• Unique compute: 10M (queries) + 5M (outputs) = 15M
• Overhead: ~1.1x (minimal, just adding queries to cache)

Cost to process 50M tokens:

• vLLM (55M actual compute): 55M ÷ 12,553 = 4,378 seconds = 1.22 hours. Cost: $3.04
• SGLang (15M actual compute): 15M ÷ 16,215 = 924 seconds = 0.26 hours. Cost: $0.65

SGLang is 4.7x cheaper for this workload.

This is the killer advantage of RadixAttention: shared context across many requests.

Scenario 3: Multi-Turn Conversational AI

User has 20-turn conversation. Each turn adds 200 tokens to conversation history. After 20 turns, history is 4,000 tokens.

Turn 20 processing:

• Full history (4,000 tokens) must be in KV cache
• User adds new 200-token message
• Model generates 300-token response
• Total: 4,200 input + 300 output = 4,500 tokens

vLLM:

• Recomputes entire 4,000-token history every turn
• Total wasted compute: 4,000 × 20 turns = 80,000 tokens of recomputation
• Actual useful compute: 200 × 20 (messages) + 300 × 20 (responses) = 10,000
• Overhead: 8x inefficiency

SGLang (RadixAttention):

• Incrementally updates KV cache tree as new turns arrive
• Reuses prior turns without recomputation
• Overhead: ~1.05x (minimal, just adding new turn)

Cost for 20-turn conversation:

• vLLM: 90K tokens ÷ 12,553 = 7.17 seconds = $0.0000050
• SGLang: 10.2K tokens ÷ 16,215 = 0.629 seconds = $0.0000004

SGLang is 11x cheaper per conversation.

Engineering Complexity: Code Examples

vLLM Setup (Python)

from vllm import LLM, SamplingParams

llm = LLM(model="meta-llama/Llama-2-7b-hf", tensor_parallel_size=1)
outputs = llm.generate(prompts, sampling_params)

Minimal boilerplate. Production-ready immediately.

SGLang Setup (Python)

import sglang as sgl

@sgl.function
def generate(s, prompt):
 s += prompt
 s += sgl.gen("output", max_tokens=200)
 return s["output"]

state = sgl.run(generate, prompt="Explain X")

More structured. Requires thinking about computation graph. But enables advanced features (structured output, caching).

The setup complexity is real. vLLM is simpler for basic use cases. SGLang requires slightly more thought but enables optimization.

The Prefix Caching Deep Dive

RadixAttention in SGLang is the core differentiator. How does it work?

Traditional KV Cache

vLLM stores KV (key-value) pairs sequentially:

Request 1: [Token1, Token2, Token3, . Token500]
 KV cache stores keys & values for all 500

Request 2: [Token1, Token2, Token3, . Token500]
 KV cache recomputes keys & values for all 500 (redundant!)

If 100 requests all start with same 500-token prompt, vLLM recomputes 500 × 100 = 50,000 tokens of wasted KV computation.

RadixAttention (Prefix Tree)

SGLang stores KV cache in a tree:

Shared root: [Token1, Token2, . Token500]
 ├─ Request 1 branch: [Token501, Token502, .]
 ├─ Request 2 branch: [Token501, Token502, .]
 ├─ Request 3 branch: [Token501, Token502, .]
 └─ .

The shared prefix (first 500 tokens) is computed once and reused. New tokens are computed only once per request. Massive savings.

Cache hit rate depends on workload:

• Generic chatbot: 20-30% (users ask different things, little prefix overlap)
• RAG system: 75-85% (same context retrieved for similar queries)
• Multi-turn conversation: 95%+ (history grows but reuses prior turns)

In high-overlap scenarios (RAG, chat), RadixAttention is transformative.

Community and Support

vLLM Community

• GitHub stars: 20,000+
• GitHub discussions: Very active
• Stack Overflow: Established tag with answers
• Slack/Discord: Large community
• Blog posts: Hundreds of tutorials
• Corporate backing: Databricks, NVIDIA

SGLang Community

• GitHub stars: 8,000+
• GitHub discussions: Active but smaller
• Stack Overflow: Newer, fewer answered questions
• Slack/Discord: Smaller but responsive
• Blog posts: Growing but not as many as vLLM
• Corporate backing: xAI, NVIDIA, AMD, Intel

vLLM has maturity advantage. 2-3x larger community, more tutorials, more third-party integrations.

SGLang has momentum. Newer, faster growth rate, backing from top AI companies.

For teams that need answers quickly or prefer established patterns: vLLM. For teams willing to blaze new trails or benefit from newer features: SGLang.

Related Resources

• LLM Serving Frameworks
• Best LLM Inference Engines
• LLM Serving Framework Comparison

Sources

• SGLang GitHub Repository
• SGLang Documentation
• vLLM Official Website
• vLLM GitHub Repository
• SGLang vs vLLM: Which is the Best Inference Engine in 2026?
• vLLM vs SGLang vs LMDeploy: Fastest LLM Inference Engine in 2026?