How to Deploy SGLang on GPU with vLLM Compatibility Layer in 2025

If you're currently running vLLM for LLM inference and want to upgrade to SGLang's superior performance without rewriting your client code, this guide walks you through the exact setup process. SGLang delivers up to 25x inference performance improvements on modern GPUs while maintaining API compatibility with existing vLLM deployments.

Why Migrate from vLLM to SGLang in 2025

SGLang has matured into a production-ready serving framework that significantly outperforms vLLM on recent hardware. The project now provides day-0 support for cutting-edge models like DeepSeek-V3.2, Nemotron 3, and MiniMax M2. Most critically for migration scenarios, SGLang exposes a vLLM-compatible API layer, meaning your existing client code requires zero changes.

The performance gains are substantial:

• 25x throughput improvement on NVIDIA GB300 NVL72 clusters
• 3.8x prefill and 4.8x decode speedups on GB200 deployments with sparse attention optimization
• Native TPU support via the SGLang-JAX backend (launched October 2025)
• Optimized support for sparse attention models like DeepSeek

Prerequisites

Before starting, ensure you have:

• NVIDIA GPU with CUDA 12.1+ (or AMD ROCm 6.0+ for MI300 series)
• Python 3.9+ (3.11 recommended)
• PyTorch 2.1+ installed
• Existing vLLM deployment or knowledge of your model's vLLM configuration
• At least 24GB VRAM for standard-size models (Llama 2 70B, Mistral)

Step 1: Install SGLang

The simplest installation uses PyPI. SGLang maintains separate wheels for different GPU architectures:

# For NVIDIA GPUs (CUDA 12.1)
pip install sglang[cuda]

# For AMD MI300 series
pip install sglang[rocm]

# For Apple Silicon (limited inference support)
pip install sglang[metal]

Verify the installation:

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

You should see a version string like 0.3.x or higher.

Step 2: Configure Your Model and vLLM API Mode

Create a launch configuration file (sglang_config.yaml):

model_path: "meta-llama/Llama-2-70b-chat-hf"  # Replace with your model
tp_size: 2  # Tensor parallelism (adjust based on GPU count)
max_batch_size: 256
max_total_tokens: 32768
port: 8000
api_protocol: "openai"  # Critical: enables vLLM API compatibility

Key parameters explained:

| Parameter | Purpose | Notes | |-----------|---------|-------| | tp_size | Tensor parallelism factor | Set to number of GPUs for large models | | max_batch_size | Concurrent requests | Balance throughput vs latency | | api_protocol | API compatibility mode | Use "openai" for vLLM drop-in replacement | | max_total_tokens | Maximum sequence length | Must fit in GPU memory |

Step 3: Launch SGLang Server with vLLM API

Start the server using the command line:

python -m sglang.launch_server \
  --model-path meta-llama/Llama-2-70b-chat-hf \
  --tp-size 2 \
  --max-batch-size 256 \
  --port 8000 \
  --api-protocol openai

Alternatively, use Python:

import sglang as sgl
from sglang.srt.server import Server

server = Server(
    model_path="meta-llama/Llama-2-70b-chat-hf",
    tp_size=2,
    max_batch_size=256,
    port=8000,
    api_protocol="openai"
)
server.launch()

Watch for this confirmation log:

[2025-01-15 14:32:10] INFO: Server started. Listening on 0.0.0.0:8000
[2025-01-15 14:32:10] INFO: Warming up the model...

Step 4: Verify vLLM API Compatibility

Your existing vLLM client code works without modification. Test with:

from openai import OpenAI

client = OpenAI(
    api_key="placeholder",
    base_url="http://localhost:8000/v1"
)

response = client.chat.completions.create(
    model="default",
    messages=[
        {"role": "user", "content": "What is the capital of France?"}
    ],
    temperature=0.7,
    max_tokens=256
)

print(response.choices[0].message.content)

SGLang's OpenAI-compatible endpoint handles all request routing internally.

Step 5: Monitor Performance Gains

SGLang exposes performance metrics at http://localhost:8000/metrics (Prometheus format):

curl http://localhost:8000/metrics | grep -E "request_duration|tokens_generated"

Benchmark against your previous vLLM setup:

# Run 100 concurrent requests
ab -n 100 -c 10 -p payload.json http://localhost:8000/v1/completions

Expect 2-10x improvement depending on your model and hardware.

Common Migration Pitfalls

Issue 1: API Endpoint Mismatch

If you're connecting to http://localhost:8000/v1, ensure SGLang is launched with --api-protocol openai. The endpoint structure is identical to vLLM, but the backend differs.

Issue 2: Memory Allocation Failures

SGLang uses more aggressive GPU memory optimization than vLLM. If you see OOM errors, reduce max_batch_size or max_total_tokens:

python -m sglang.launch_server \
  --model-path meta-llama/Llama-2-70b-chat-hf \
  --tp-size 2 \
  --max-batch-size 128 \
  --max-total-tokens 16384

Issue 3: Streaming Response Latency

For streaming completions, SGLang's chunked decoding may introduce slight delays. Adjust the --schedule-heuristic flag:

python -m sglang.launch_server \
  --model-path meta-llama/Llama-2-70b-chat-hf \
  --schedule-heuristic "laxed"

Advanced: Leverage SGLang-Specific Features

Once migrated, you can optionally tap into SGLang's performance enhancements:

Structured Output with SGLang

import sglang as sgl

@sgl.function
def extract_json(s, text):
    s += sgl.system("You are a JSON extractor")
    s += sgl.user(text)
    s += sgl.assistant(sgl.gen("output", regex=r'\{.*\}'))
    return s

state = extract_json.run(text="John Doe is 30 years old")

Sparse Attention for DeepSeek Models

If running DeepSeek-V3.2 or similar sparse attention models, SGLang automatically optimizes:

python -m sglang.launch_server \
  --model-path deepseek-ai/deepseek-v3.2 \
  --enable-sparse-attention  # Auto-enabled for compatible models

Rollback Plan

If performance degrades, revert to vLLM by:

1. Keeping vLLM installed in a separate environment
2. Pointing your client to the old endpoint: base_url="http://localhost:8001/v1"
3. Running both in parallel during transition

Next Steps

With SGLang deployed and API-compatible, explore:

• Batch inference optimization: Use SGLang's prefix caching for repeated prompts
• Multi-GPU scaling: Test larger tp_size values on your cluster
• Model updates: Deploy newer models like Nemotron 3 Nano or MiniMax M2 that have day-0 SGLang support
• Monitoring: Set up Prometheus + Grafana to track latency and throughput improvements

The migration typically takes under 30 minutes for production deployments, with immediate performance benefits on modern hardware.