vLLM vs SGLang in 2026: Serve LLMs Faster

1. The Problem: The Benchmark Said 29%, Your p99 Did Not Move

Every few weeks a new post claims one engine beats the other by 29% — then teams migrate, debug kernel conflicts at midnight, and watch p99 latency sit exactly where it was. Both engines leapfrog each other every release, so a leaderboard delta is noise. What actually decides the winner is your prompt shape: unique prompts behave nothing like RAG loops where every request shares a 4k system prompt.

The goal of this guide: understand the two caching philosophies, serve the same model on both engines with verified commands, hit both with identical code, and walk away with a decision rule you can defend in a design review.

2. Minimal Concepts You Actually Need

Four ideas explain nearly every benchmark you will read this year. Get these and the numbers below read themselves.

3. Tutorial: Serve One Model on Both Engines, Step by Step

We will serve Llama 3.1 8B Instruct on vLLM (:8000) and SGLang (:30000) on the same GPU host, smoke-test both, call both with identical Python, force JSON output on both, and run a 5-minute load comparison. Six steps, official commands only.

pip install vllm openai

vllm serve meta-llama/Llama-3.1-8B-Instruct \
  --host 0.0.0.0 --port 8000 \
  --enable-prefix-caching \
  --gpu-memory-utilization 0.9

pip install sglang openai

python3 -m sglang.launch_server \
  --model-path meta-llama/Llama-3.1-8B-Instruct \
  --host 0.0.0.0 --port 30000 \
  --mem-fraction-static 0.9

curl http://localhost:8000/v1/models
curl http://localhost:30000/v1/models

from openai import OpenAI

SYSTEM = 'You are a concise release-notes writer for developer tools.'
USER = 'Summarize these changes in 3 bullets: faster KV cache, new tokenizer, fixed streaming.'

def ask(base_url):
    client = OpenAI(base_url=base_url, api_key='EMPTY')
    r = client.chat.completions.create(
        model='meta-llama/Llama-3.1-8B-Instruct',
        messages=[{'role': 'system', 'content': SYSTEM},
                  {'role': 'user', 'content': USER}],
        temperature=0, max_tokens=128,
    )
    return r.choices[0].message.content

print('vLLM:  ', ask('http://localhost:8000/v1'))
print('SGLang:', ask('http://localhost:30000/v1'))

schema = {'type': 'object', 'properties': {
    'title': {'type': 'string'}, 'bullets': {'type': 'array', 'items': {'type': 'string'}}},
    'required': ['title', 'bullets']}

# vLLM: grammar-constrained JSON
vllm = OpenAI(base_url='http://localhost:8000/v1', api_key='EMPTY')
r1 = vllm.chat.completions.create(model='meta-llama/Llama-3.1-8B-Instruct',
    messages=[{'role': 'user', 'content': USER}],
    extra_body={'guided_json': schema}, max_tokens=256)

# SGLang: OpenAI-style JSON mode
sgl = OpenAI(base_url='http://localhost:30000/v1', api_key='EMPTY')
r2 = sgl.chat.completions.create(model='meta-llama/Llama-3.1-8B-Instruct',
    messages=[{'role': 'user', 'content': USER}],
    response_format={'type': 'json_object'}, max_tokens=256)

import asyncio, time
from openai import AsyncOpenAI

PROMPTS = [USER] * 32  # replace with YOUR production prompts

async def bench(base_url, conc=16):
    client = AsyncOpenAI(base_url=base_url, api_key='EMPTY')
    sem = asyncio.Semaphore(conc)
    async def one(p):
        async with sem:
            r = await client.chat.completions.create(
                model='meta-llama/Llama-3.1-8B-Instruct',
                messages=[{'role': 'system', 'content': SYSTEM},
                          {'role': 'user', 'content': p}],
                max_tokens=128)
            return len(r.choices[0].message.content.split())
    t0 = time.time()
    toks = sum(await asyncio.gather(*[one(p) for p in PROMPTS]))
    dt = time.time() - t0
    return toks / dt

for conc in (1, 8, 32):
    v = await bench('http://localhost:8000/v1', conc)
    s = await bench('http://localhost:30000/v1', conc)
    print(f'conc={conc:3d}  vLLM={v:7.1f} tok/s  SGLang={s:7.1f} tok/s')

4. Common Errors That Invalidate Your Comparison

I have reviewed enough engine shootouts to spot the failure modes by smell. Avoid these five and your numbers will survive contact with production.

Conclusion

On 2026 H100s the honest summary fits in one line: SGLang wins prefix-heavy small-model serving by up to ~29%, ties on big models and unique prompts, and both speak OpenAI-compatible APIs so the migration cost is one config change plus a flag audit.

Run the six steps above with your production prompts this week. If the concurrency curve bends toward SGLang, migrate the prefix-heavy services and leave the rest on vLLM — heterogeneous fleets are normal, and the engines coexist behind the same load balancer.

Sources

• Techsy — vLLM vs SGLang 2026: H100 Benchmarks (Sep 1, 2026)
• ComputingForGeeks — SGLang vs vLLM: Install and Benchmark (RTX 4090, Jul 2026)
• vLLM docs — Automatic Prefix Caching (hash-based design)
• vLLM docs — Disaggregated Prefilling (experimental)
• SGLang docs — OpenAI-compatible Chat Completions API
• VIPS Learn — SGLang vs vLLM comparison (Apr 2026)