How to Deploy SGLang with vLLM on NVIDIA GPUs: 2025 Performance Comparison

Choosing between SGLang and vLLM for your NVIDIA GPU-based LLM serving infrastructure is a critical decision that directly impacts inference latency, throughput, and operational costs. Both frameworks have evolved significantly, and 2025 brings substantial performance improvements, particularly with newer hardware like NVIDIA GB300.

This guide walks you through deploying both frameworks, understanding their architectural differences, and determining which fits your production workload.

Understanding SGLang vs vLLM Architecture

Both SGLang and vLLM are serving frameworks designed to maximize GPU utilization and minimize inference latency. However, they take different architectural approaches:

SGLang is built with a focus on:

• Structured generation control through its Structured Generation Language (SGLang DSL)
• Multi-modal support (text, image, video generation)
• Newer optimization techniques including large-scale expert parallelism and speculative decoding
• Recent GPU support including native TPU backend (via SGLang-Jax) and GB300 optimizations

vLLM emphasizes:

• Mature, battle-tested infrastructure used in production at scale
• PagedAttention mechanism for efficient KV cache management
• Broad model compatibility across diverse architectures
• Strong community ecosystem with extensive integrations

Performance Metrics: SGLang on GB300 NVL72

Recent benchmarks (February 2026) show SGLang achieving 25x inference performance improvements on NVIDIA GB300 NVL72 hardware with large-scale expert parallelism. vLLM hasn't published comparable GB300 benchmarks yet, making SGLang the clear choice if you're deploying on cutting-edge NVIDIA hardware.

| Metric | SGLang (GB300) | vLLM (A100) | Notes | |--------|---|---|---| | Prefill Throughput | 3.8x improvement (DeepSeek-V3.2) | Baseline for comparison | SGLang large-scale EP optimized | | Decode Throughput | 4.8x improvement (DeepSeek-V3.2) | Baseline for comparison | Sparse attention support | | Multi-modal Support | Native (text, image, video) | Text-primary | SGLang Diffusion advantage | | TPU Support | Native (SGLang-Jax backend) | CUDA-only | Critical for Google Cloud TPU users | | Day-0 Model Support | Cutting-edge (DeepSeek, Nemotron, MiMo) | Strong but reactive | SGLang faster adoption |

Step 1: Environment Setup for SGLang

First, verify your NVIDIA GPU setup:

nvidia-smi
# Output should show your GPU (e.g., H100, GB100, GB200)

# Install CUDA 12.1+ (required for SGLang 2025 versions)
cuda-version

Install SGLang from PyPI:

pip install sglang
# For GPU support with vLLM-like PagedAttention baseline
pip install sglang[cuda]

# For multi-modal support (if using image/video generation)
pip install sglang[multimodal]

# Verify installation
python -c "import sglang; print(sglang.__version__)"

Step 2: Setup vLLM for Comparison

Install vLLM with the same CUDA version:

pip install vllm
# For CUDA 12.1
pip install vllm[cuda-12.1]

# Verify
python -c "import vllm; print(vllm.__version__)"

Step 3: Deploying Your First Model

SGLang Deployment

import sglang as sgl
from sglang import function, gen, set_default_backend

# Initialize backend for NVIDIA GPU
set_default_backend(sgl.RuntimeEndpoint("http://localhost:30000"))

# Launch SGLang server
# In terminal:
# python -m sglang.launch_server --model-path meta-llama/Llama-2-7b-hf --port 30000

@function
def multi_turn_conversation(s, user_input):
    s += "You are a helpful assistant.\n"
    s += f"User: {user_input}\n"
    s += "Assistant:"
    s += gen(name="response", max_tokens=512, temperature=0.7)

# Run inference
state = multi_turn_conversation.run(user_input="Explain quantum computing in 100 words")
print(state["response"])

vLLM Deployment

from vllm import LLM, SamplingParams

# Initialize LLM
llm = LLM(
    model="meta-llama/Llama-2-7b-hf",
    tensor_parallel_size=1,  # Adjust for multi-GPU
    gpu_memory_utilization=0.9,
)

# Create sampling parameters
sampling_params = SamplingParams(
    temperature=0.7,
    max_tokens=512,
)

# Run inference
prompt = "You are a helpful assistant.\nUser: Explain quantum computing in 100 words\nAssistant:"
outputs = llm.generate([prompt], sampling_params)

for output in outputs:
    print(output.outputs[0].text)

Step 4: Structured Generation with SGLang

This is where SGLang shines. Use its DSL for constrained, structured outputs:

import sglang as sgl
import json

@sgl.function
def extract_json(s, text):
    s += f"Extract key information from the text:\n{text}\n"
    s += "Return as JSON with keys: name, age, location\n"
    s += "JSON:"
    s += sgl.gen(
        name="response",
        max_tokens=200,
        regex=r'\{[^}]+\}',  # Enforce JSON structure
    )

result = extract_json.run(
    text="John Smith, 34 years old, lives in San Francisco"
)
print(json.loads(result["response"]))

vLLM doesn't have built-in structured generation, requiring additional libraries like outlines for regex constraints.

Step 5: Selecting Your Framework

Choose SGLang If:

• Deploying on GB300/GB200 NVL72: Performance gains justify migration effort
• Need structured/constrained generation: JSON schemas, regex patterns for reliability
• Using multi-modal models: Native image/video generation support in 2025
• Running on TPU infrastructure: SGLang-Jax backend required
• Deploying cutting-edge models: DeepSeek-V3.2, Nemotron, MiMo-V2 day-0 support
• Building AI agents: Structured output enables reliable tool calling

Choose vLLM If:

• Maximizing stability: Proven production track record at scale
• Limited NVIDIA hardware: Works well on A100/H100 without new optimizations
• Simple text-only inference: Minimal operational overhead
• Team familiarity: Existing vLLM expertise and integrations
• Open-source contribution: Larger community ecosystem

Cost and Performance Analysis

For a 100K tokens/day workload on NVIDIA H100 ($3.06/hour on AWS):

vLLM baseline: ~15 hours/month = $45.90 SGLang (2025 optimized): With 25x performance gain on GB300 = ~0.6 hours/month = $1.84

The 25x improvement only applies to GB300 hardware. On H100, improvements are typically 15-20%, justifying migration only if you're upgrading GPUs anyway.

Common Migration Pitfalls

1. Assumption of direct API compatibility: SGLang's @function decorator paradigm differs from vLLM's stateless API. Rewrite generation logic.

2. Overlooking regex performance overhead: Structured generation with regex adds 5-15% latency. Profile before production.

3. GPU memory miscalculation: SGLang's multi-modal support requires 15-30% more VRAM. Monitor with nvidia-smi.

4. Ignoring sparse attention requirements: DeepSeek-V3.2 optimizations in SGLang require specific model architecture support.

Conclusion

In 2025, SGLang and vLLM serve different use cases:

• vLLM remains ideal for stable, text-only production workloads
• SGLang excels in structured generation, multi-modal inference, and cutting-edge hardware utilization

Start with vLLM if you're familiar with it. Migrate to SGLang if you need structured outputs, multi-modal support, or are upgrading to GB300 hardware. The 25x performance improvement on next-gen GPUs justifies the migration for cost-sensitive production deployments.