Mosec

Overview

Mosec is a high-performance ML model serving framework that supports dynamic batching and CPU/GPU pipelines. It is written in Python and designed to maximize hardware utilization for inference workloads.

Best for

Best for
ML engineers deploying custom Python models who need efficient batching and mixed CPU/GPU pipelines

Use cases

• Deploying custom machine learning models in production with automatic batching
• Running inference pipelines that require both CPU and GPU stages
• Serving models with variable request sizes to optimize throughput

Notes

Mosec is a high-performance ML model serving framework that supports dynamic batching and CPU/GPU pipelines. It is written in Python and designed to maximize hardware utilization for inference workloads.

899 stars on GitHub. Last updated 2026-06-01. Licensed Apache-2.0.

Use cases

• Deploying custom machine learning models in production with automatic batching
• Running inference pipelines that require both CPU and GPU stages
• Serving models with variable request sizes to optimize throughput

Pros

• Dynamic batching adapts to request load for better resource use
• Supports hybrid CPU/GPU pipelines without extra orchestration
• Native Python integration simplifies workflow for Python‑based ML teams

Cons

• Smaller community and ecosystem compared to established serving frameworks
• Limited support for non-Python model formats and clients
• Production hardening and monitoring features are less mature than alternatives like Triton or Ray Serve

Indexed from awesome-llmops and enriched against its public facts.