ai-inference-optimization
Skill1 starsChanged 6 months agoMITView on GitHub
Practical knowledge for optimizing inference for foundation models. Covers inference fundamentals (computational bottlenecks, online vs batch APIs, latency/throughput metrics), AI accelerators (GPUs, TPUs, computational/memory characteristics), model optimization (compression, speculative decoding, attention optimization, kernels/compilers), and service optimization (batching, prefill/decode separation, prompt caching, parallelism).
1 other project uses a version of this.
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Most recently taken up by
- pramodmax/openshift-ai-skillstheir own version, first seen Jun 16, 2026
Other versions1 project uses a version of this.
Word for word · 0
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Wrote their own version · 1
- pramodmax/openshift-ai-skills★ 1same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository21 other files it gives its agents.
- ai-context-engineeringSkill
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- ai-context-engineSkill
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- ai-finetuningSkill
- ai-foundation-modelsSkill
- ai-multi-agent-mcpSkill
- ai-production-architectureSkill
- ai-prompt-engineeringSkill
- ai-rag-and-agentsSkill
- ai-rag-defenseSkill
- ddia-architectureSkill
- ddia-batch-stream-processingSkill
- ddia-data-ethicsSkill
- ddia-data-modelingSkill
- ddia-replication-shardingSkill
- ddia-transactions-consistencySkill
- job-description-analyzerSkill
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The file
We split this skill into the rules it gives an agent
1 rule. About 0 are common, 0 are distinctive, and 1 is very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈0
Distinctive · estimate
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 21
Check guidelines.md to find which files to load for your task
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
AI Inference Optimization
Knowledge from "AI Engineering" by Chip Huyen (Chapter 9). Practical techniques for making inference faster and cheaper.
Quick Start
- Check
guidelines.mdto find which files to load for your task - Load only relevant files (each topic has knowledge.md, rules.md, examples.md)
- Apply guidance to your work
Contents
References
| Category | Purpose |
inference-fundamentals | Computational bottlenecks, online/batch APIs, latency/throughput/utilization metrics |
ai-accelerators | GPUs/TPUs, computational capabilities, memory size/bandwidth, power |
model-optimization | Compression, speculative decoding, attention optimization, kernels/compilers |
service-optimization | Batching, prefill/decode decoupling, prompt caching, parallelism |
Workflows
| Task | Workflow |
| Diagnose and fix inference bottlenecks | workflows/diagnose-bottleneck.md |
Guidelines
See guidelines.md for task-based file selection.
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