SGLang vs vLLM: Complete LLM Inference Engine Comparison

What Are SGLang and vLLM?

SGLang and vLLM are the two leading high-performance inference engines for deploying large language models in production. Both dramatically outperform HuggingFace Transformers (vLLM claims up to 24x higher throughput), but they take different architectural approaches and excel in different scenarios.

SGLang

SGLang is a high-performance serving framework developed by LMSYS (the organization behind Chatbot Arena and Vicuna). It runs on over 400,000 GPUs worldwide, generating trillions of tokens daily in production. Notable users include xAI (Grok 3) and Microsoft Azure (DeepSeek R1 on AMD).

SGLang's core innovation is RadixAttention—a radix tree-based KV cache management system that automatically discovers and reuses shared prefixes across requests without manual configuration.

vLLM

vLLM is a high-throughput and memory-efficient inference engine originally developed in the Sky Computing Lab at UC Berkeley. It has evolved into a community-driven project with broad industry adoption.

vLLM's core innovation is PagedAttention—treating the KV cache like virtual memory with page-based allocation. This reduces memory waste from 60-80% (traditional systems) to under 4%, enabling 2-4x more concurrent requests on the same hardware.

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Performance Benchmarks

Throughput Comparison (H100, Llama 3.1 8B)

Key Performance Findings

1. SGLang maintains 29% throughput advantage over fully optimized vLLM on H100 GPUs
2. SGLang delivers 2x+ higher output throughput in comparative benchmarks
3. SGLang remains stable under high concurrency (30-31 tok/s constant) while vLLM drops from 22 to 16 tok/s
4. Multi-turn conversations: SGLang provides ~10% boost due to RadixAttention caching

Throughput Relative to HuggingFace

Both frameworks dramatically outperform naive inference approaches.

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Core Technology: RadixAttention vs PagedAttention

Understanding the architectural differences helps you choose the right framework.

RadixAttention (SGLang)

RadixAttention uses a radix tree (trie) data structure to manage the KV cache:

How RadixAttention Works:
├── Automatic prefix discovery across requests
├── Radix tree stores common prefixes efficiently
├── LRU eviction policy for cache management
├── Cache-aware scheduling maximizes reuse
└── No manual configuration required

Key characteristics:

• Automatic: Discovers shared prefixes without configuration
• Dynamic: Adapts to varying conversation patterns
• Efficient: Depth-first search order for tree traversal
• Best for: Multi-turn conversations, agents, iterative reasoning

How it works in practice: When multiple requests share a common system prompt or conversation history, RadixAttention automatically identifies this overlap and stores it once. Subsequent requests referencing the same prefix get instant cache hits without re-computation.

PagedAttention (vLLM)

PagedAttention treats the KV cache like OS virtual memory:

How PagedAttention Works:
├── KV cache split into fixed-size "pages"
├── Pages allocated on demand (no upfront guessing)
├── Copy-on-write for shared sequences
├── 2-4x memory efficiency improvement
└── <4% memory waste (vs 60-80% traditional)

Key characteristics:

• Memory-efficient: Near-optimal utilization
• Predictable: Fixed page sizes simplify memory planning
• Scalable: Supports copy-on-write for parallel sampling
• Best for: High-concurrency batch processing, memory-constrained environments

How it works in practice: Instead of allocating a contiguous memory block sized for maximum possible sequence length, PagedAttention allocates small pages incrementally as the sequence grows. This eliminates the waste from overestimating sequence lengths.

When Each Excels

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Installation Guide

SGLang Installation

System Requirements:

• Python 3.10+
• CUDA 12.2+ (CUDA 12 or 13 recommended)
• NVIDIA GPU SM75+ (Turing and above: T4, RTX 20xx, A10, A100, L4, L40S, H100)
• NVIDIA Driver 535+
• 32GB RAM minimum
• 50GB disk space minimum

Method 1: pip with uv (Recommended)

# Install uv package manager
pip install --upgrade pip
pip install uv

# Install SGLang with all dependencies
uv pip install "sglang[all]>=0.4.6.post2"

Method 2: Docker (Production)

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<your-token>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
    --model-path meta-llama/Llama-3.1-8B-Instruct \
    --host 0.0.0.0 --port 30000

Method 3: From source

git clone https://github.com/sgl-project/sglang.git
cd sglang
pip install -e "python[all]"

vLLM Installation

System Requirements:

• Python 3.8+ (3.12 recommended)
• CUDA 11.8+ (CUDA 12+ recommended)
• NVIDIA GPU compute capability 7.0+ (V100, T4, A100, L4, H100)
• Linux OS (Ubuntu 20.04/22.04 recommended)

Method 1: pip (Stable)

pip install vllm

Method 2: pip (Nightly)

pip install -U vllm --pre --extra-index-url https://wheels.vllm.ai/nightly

Method 3: conda + pip

conda create -n vllm python=3.12 -y
conda activate vllm
pip install vllm

Method 4: Docker (Recommended for troubleshooting)

docker run --gpus all --ipc=host \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    vllm/vllm-openai:latest \
    --model meta-llama/Llama-3.1-8B-Instruct

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Feature Comparison

Complete Feature Matrix

Speculative Decoding

Speculative decoding improves latency by using a small draft model to propose multiple tokens, then validating with the large model in a single pass.

SGLang supports:

• EAGLE (state-of-the-art)
• EAGLE3 (improved EAGLE)

vLLM supports:

• EAGLE
• Medusa
• n-gram (simpler, faster)

Both frameworks achieve similar speedups (up to 2-3x for memory-bound scenarios).

Quantization Support

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Supported Models

SGLang Model Support

DeepSeek optimization: SGLang has MLA-optimized kernels for DeepSeek models, making it the preferred choice for DeepSeek R1/V3 deployment.

vLLM Model Support

Model coverage: vLLM supports any model architecture available in HuggingFace Transformers through its backend system, giving it broader coverage overall.

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Hardware Support

SGLang Hardware Requirements

vLLM Hardware Requirements

Hardware flexibility: vLLM has significantly broader hardware support, including cloud-specific accelerators (AWS Trainium, TPU) and Intel hardware.

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API Usage

SGLang Server

Starting the server:

python -m sglang.launch_server \
    --model-path meta-llama/Llama-3.1-8B-Instruct \
    --port 30000 \
    --host 0.0.0.0

OpenAI-compatible API:

curl http://localhost:30000/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
        "model": "default",
        "messages": [{"role": "user", "content": "Hello!"}],
        "temperature": 0.7
    }'

Python client:

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:30000/v1",
    api_key="not-needed"
)

response = client.chat.completions.create(
    model="default",
    messages=[{"role": "user", "content": "Explain quantum computing"}],
    temperature=0.7
)
print(response.choices[0].message.content)

vLLM Server

Starting the server:

python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Llama-3.1-8B-Instruct \
    --port 8000

OpenAI-compatible API:

curl http://localhost:8000/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
        "model": "meta-llama/Llama-3.1-8B-Instruct",
        "messages": [{"role": "user", "content": "Hello!"}],
        "temperature": 0.7
    }'

Python client:

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:8000/v1",
    api_key="not-needed"
)

response = client.chat.completions.create(
    model="meta-llama/Llama-3.1-8B-Instruct",
    messages=[{"role": "user", "content": "Explain quantum computing"}],
    temperature=0.7
)
print(response.choices[0].message.content)

Both frameworks expose OpenAI-compatible APIs, making migration straightforward.

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Production Usage

SGLang in Production

• xAI uses SGLang to serve Grok 3
• Microsoft Azure serves DeepSeek R1 on AMD GPUs using SGLang
• 400,000+ GPUs running SGLang worldwide
• Trillions of tokens generated daily

SGLang is the preferred choice for companies prioritizing maximum throughput and multi-turn conversation performance.

vLLM in Production

• Powers many OpenAI-compatible API endpoints
• Broad cloud provider support (AWS, GCP, Azure)
• Enterprise deployments across industries
• 24x throughput improvement vs HuggingFace Transformers

vLLM is the preferred choice for companies needing broad hardware compatibility and maximum model coverage.

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When to Choose Each Framework

Choose SGLang When:

1. Multi-turn conversations (chatbots, dialogue systems, planning agents)
2. AI agents with iterative reasoning
3. DeepSeek models (MLA-optimized kernels)
4. Maximum throughput is critical (29% faster)
5. Low latency requirements (sub-100ms TTFT)
6. Million-token contexts
7. Structured output generation (JSON/XML)
8. NVIDIA or AMD GPUs (primary platform)

Choose vLLM When:

1. Batch content generation (articles, summaries)
2. Real-time Q&A services with predictable patterns
3. Broad hardware support needed (TPU, AWS Trainium, Intel Gaudi)
4. Maximum model compatibility required
5. Memory-constrained environments (PagedAttention efficiency)
6. Heterogeneous GPU clusters
7. Rapid prototyping (simpler setup)
8. Encoder-decoder models (T5, BART)

Decision Matrix

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Troubleshooting

SGLang Common Issues

Out of memory:

# Reduce context length
python -m sglang.launch_server \
    --model-path meta-llama/Llama-3.1-8B-Instruct \
    --context-length 8192

Slow startup:

# Enable tensor parallelism
python -m sglang.launch_server \
    --model-path meta-llama/Llama-3.1-8B-Instruct \
    --tp 2

vLLM Common Issues

Out of memory:

# Limit GPU memory utilization
python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Llama-3.1-8B-Instruct \
    --gpu-memory-utilization 0.8

Slow generation:

# Enable speculative decoding
python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Llama-3.1-8B-Instruct \
    --speculative-model small-llama

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Key Takeaways

1. SGLang is 29% faster in raw throughput on H100 GPUs
2. RadixAttention excels in multi-turn conversations with automatic prefix caching
3. PagedAttention reduces memory waste from 60-80% to under 4%
4. vLLM has broader hardware and model support
5. Both are production-ready at massive scale (xAI, Microsoft Azure, thousands of deployments)
6. Choose based on workload, not just raw speed
7. Both expose OpenAI-compatible APIs for easy integration

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Next Steps

1. Run DeepSeek R1 with SGLang for best performance
2. Compare local AI tools for simpler personal use
3. Learn about MoE architecture that both frameworks serve
4. Check VRAM requirements for your model
5. Build AI agents using these inference engines

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SGLang and vLLM represent the cutting edge of LLM inference technology. Both dramatically outperform traditional approaches and are production-ready at massive scale. SGLang excels in throughput and multi-turn conversations; vLLM excels in memory efficiency and hardware flexibility. For most production deployments, either framework will deliver excellent performance—the choice comes down to your specific workload patterns and infrastructure requirements.