Modeling LLM Performance from First Principles
Home Modeling LLM Performance from First Principles 2026-05-31 Tags: Introduction Over the past two years, I've taken a variety of courses on systems and high performance compute, and I've found myself particularly interested in LLM inference. However, over that same time frame, LLM Inference workloads have been evolving at an extremely rapid pace. This makes it challenging to internalize a robust model for workload performance.
- ▪Home Modeling LLM Performance from First Principles 2026-05-31 Tags: Introduction Over the past two years, I've taken a variety of courses on systems and high performance compute, and I've found myself particularly interested in LLM inferen
- ▪However, over that same time frame, LLM Inference workloads have been evolving at an extremely rapid pace.
- ▪This makes it challenging to internalize a robust model for workload performance.
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| Original publisher | Sidbabu |
| Canonical URL | https://sidbabu.com/posts/llm-perf-modeling |
| Publication time | Thu, 06 Aug 2026 20:25:59 +0000 |
| Retrieval time | 2026-08-06T20:30:45.732Z |
| Last seen | 2026-08-06T20:30:45.732Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | QSV7Nv6tBATp · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Home Modeling LLM Performance from First Principles 2026-05-31 Tags: Introduction Over the past two years, I've taken a variety of courses on systems and high performance compute, and I've found myself particularly interested in LLM inference. However, over that same time frame, LLM Inference workloads have been evolving at an extremely rapid pace. This makes it challenging to internalize a robust model for workload performance. A lot of the knowledge on how to make LLM inference fast and effective is locked in the brains of a few talented engineers, and is shared as "performance tips" - general rules or heuristics that may not be applicable everywhere.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Sidbabu.
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