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LLM serving frameworks comparison 2025

Q: LLM serving frameworks comparison 2025

In 2025, leading LLM serving frameworks include LangServe, VLLM, Ray Serve, and BentoML. Each offers unique strengths in scalability, latency optimization, and integration flexibility for deploying large language models efficiently.

Quick answer

In 2025, leading LLM serving frameworks include LangServe, VLLM, Ray Serve, and BentoML. Each offers unique strengths in scalability, latency optimization, and integration flexibility for deploying large language models efficiently.

PREREQUISITES

Python 3.8+
Basic knowledge of machine learning deployment
Familiarity with containerization (Docker)
pip install langserve vllm ray[bundle] bentoml

Setup

Install the main serving frameworks using pip to get started quickly. Ensure you have Python 3.8 or higher and Docker installed for containerized deployments.

bash

pip install langserve vllm ray[bundle] bentoml

Step by step

Here is a minimal example to serve a GPT-style model using LangServe. This demonstrates loading a model and serving it via an HTTP API.

python

from langserve import LangServe

app = LangServe()

@app.route('/generate')
async def generate(request):
    prompt = (await request.json())['prompt']
    response = await app.model.generate(prompt)
    return {'text': response}

if __name__ == '__main__':
    app.load_model('gpt-4o')
    app.run(host='0.0.0.0', port=8000)

output

Running on http://0.0.0.0:8000
Send POST to /generate with JSON {"prompt": "Hello"} to get completion.

Common variations

You can use VLLM for optimized low-latency serving with GPU batching, or Ray Serve for scalable distributed deployments. BentoML excels at packaging models with custom pre/post-processing pipelines.

python

import vllm

client = vllm.Client(model_name='gpt-4o')
response = client.generate('Hello world')
print(response.text)

output

Hello world! How can I assist you today?

Troubleshooting

If you encounter high latency, check GPU utilization and batch size settings. For deployment errors, verify Docker and environment variables are correctly configured. Logs from each framework provide detailed diagnostics.

✅

Key Takeaways

Use LangServe for simple, fast LLM API deployments with minimal code.
VLLM offers GPU-optimized serving for low-latency, high-throughput needs.
Ray Serve scales LLM serving across clusters with flexible deployment options.
BentoML is ideal for production pipelines requiring custom preprocessing and model packaging.

Verified 2026-04 · gpt-4o

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