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The Small Model Infrastructure Nobody Built (So We Did) — Filip Makraduli, Superlinked
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TL;DR · AI Summary
This article introduces the motivation, challenges, and solutions behind Superlinked's development of inference infrastructure for small models.
Key Takeaways
- Current infrastructure lacks sufficient support for small models, leading to per
- Superlinked built its own inference engine to optimize deployment and execution
- The infrastructure supports multiple model formats with low latency and high thr
Outline
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Overview of current limitations in small model inference infrastructure
Analysis of limitations in existing systems for small models
Design goals and core features of the self-developed inference engine
Achieving low latency and high throughput
Support for ONNX, TorchScript, and other formats
Plans to open-source and continuously improve the infrastructure
Mindmap
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查看大纲文本（无障碍 / 无 JS 友好）
- 小型模型基础设施
- 问题分析
- 现有系统不足
- 性能瓶颈
- 解决方案
- 自研推理引擎
- 多模型格式支持
- 性能优化
- 低延迟
- 高吞吐量
Highlights
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We found that existing inference systems fall short in supporting small models, prompting us to build our own engine.
Our goal is to achieve millisecond-level latency and thousands of inferences per second throughput.
We support multiple model formats to enhance flexibility and compatibility.
#AI Engineering#Model Deployment#Infrastructure#Small Models