SGLang vs vLLM Differences

SGLang vs vLLM Differences | Generated by AI

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Question: What are the differences between SGLang and vLLM?

Answer:

Both are high-performance LLM inference engines, but they have different design philosophies and optimization targets.

Core Philosophy

	vLLM	SGLang
Origin	UC Berkeley, 2023	Stanford, 2024
Primary innovation	PagedAttention (KV cache mgmt)	RadixAttention (prefix reuse)
Design goal	Maximize throughput via memory efficiency	Maximize throughput via computation reuse
Primary abstraction	Serving engine	Structured generation language + engine

Key Technical Differences

1. KV Cache Management

vLLM — PagedAttention:

Splits KV cache into fixed-size pages (like virtual memory)
Eliminates KV cache fragmentation
Allows sharing pages across parallel sequences (beam search, speculative decoding)

SGLang — RadixAttention:

Organizes KV cache as a radix tree (trie over token sequences)
Automatically reuses KV cache across requests that share a common prefix
Critical win: multi-turn chat, RAG with shared system prompts, few-shot prompting — all get prefix reuse for free

Prefix: [system prompt tokens]
Request A: [system prompt] + [user query A]
Request B: [system prompt] + [user query B]
                ↑
        SGLang computes this once, reuses across A and B

2. Structured Output / Constrained Decoding

SGLang was originally built around structured generation — it has a native DSL:

# SGLang native DSL (original API)
@sgl.function
def multi_turn_qa(s, question):
    s += sgl.system("You are helpful")
    s += sgl.user(question)
    s += sgl.assistant(sgl.gen("answer", max_tokens=256))

vLLM added structured output later via guided_decoding (outlines/lm-format-enforcer backend). SGLang’s is tighter because the engine was designed around it from day one.

3. Continuous Batching & Scheduling

Both support continuous batching, but:

vLLM uses chunked prefill + preemption with recompute or swap
SGLang uses chunk prefill with RadixAttention — prefill chunks land in the radix tree, so later requests benefit immediately

As of mid-2025:

vLLM has broader model support (more HuggingFace models work out-of-box)
SGLang tends to be faster on supported models (LLaMA, Mistral, Qwen, DeepSeek) and has very strong DeepSeek MLA (Multi-head Latent Attention) support — relevant to you given your DeepSeek usage

5. MoE / DeepSeek Support

SGLang specifically optimized for DeepSeek V3/R1 MoE models:

Expert parallelism across GPUs
MLA (latent KV compression) — DeepSeek’s variant that compresses KV cache via low-rank projection
FP8 quantization path tuned for AMD and NVIDIA

vLLM also supports DeepSeek but SGLang’s team has been faster here.

Benchmark Reality (approximate, varies by workload)

Metric	vLLM	SGLang
Throughput (shared prefix)	Baseline	+20–50% from RadixAttention
Throughput (no shared prefix)	~similar	~similar
TTFT (time-to-first-token)	Good	Often better with prefix reuse
Model coverage	Wider	Narrower but growing
Ease of deployment	Mature, stable	Slightly more experimental

When to Use Which

Use SGLang when:

You have long shared prefixes (system prompts, RAG context, few-shot examples)
Running DeepSeek V3/R1/MLA models
Need tight structured output / constrained generation
Chasing maximum throughput on supported architectures

Use vLLM when:

You need maximum model compatibility
Running diverse workloads without a dominant shared prefix
You want the more battle-tested production path (more operators use it)
Integrating with existing tooling (Ray Serve, KServe, etc.)

Quick Start Comparison

# vLLM
pip install vllm
python -m vllm.entrypoints.openai.api_server \
  --model meta-llama/Llama-3-8B-Instruct \
  --tensor-parallel-size 1

# SGLang
pip install sglang[all]
python -m sglang.launch_server \
  --model-path meta-llama/Llama-3-8B-Instruct \
  --tp 1

Both expose an OpenAI-compatible /v1/chat/completions endpoint — drop-in swappable for most use cases.

Bottom line for your workload: Given you’re running DeepSeek V3/V4 and care about throughput at scale, SGLang is worth benchmarking seriously — the RadixAttention wins are real when your prompts share structure (which they do in agent pipelines and multi-turn chat). vLLM is the safer production choice if you need broad compatibility or are serving a mix of models.

References

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