Introduction to LLM Inference
The Artifact: What’s Actually in That 10GB Download?The “Manual” (The Header)The “Hardware” (The Tensors)Quantization: Shrinking the Brain3. The Three Phases: A Map Before the Territory4. Tokenization: Chopping Text Into NumbersIs Tokenization CPU-Bound?5.
- ▪The Artifact: What’s Actually in That 10GB Download?The “Manual” (The Header)The “Hardware” (The Tensors)Quantization: Shrinking the Brain3.
- ▪The Three Phases: A Map Before the Territory4.
- ▪Tokenization: Chopping Text Into NumbersIs Tokenization CPU-Bound?5.
Hacker News (AI / LLM) files mainly under ai. We currently carry 2,536 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
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Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Karthika Raghavan |
| Canonical URL | https://kraghavan.ca/llm-infrastructure/inference/2026/04/14/re-introduction-to-inference.html |
| Publication time | Sun, 26 Jul 2026 05:18:42 +0000 |
| Retrieval time | 2026-07-26T05:32:48.004Z |
| Last seen | 2026-07-26T05:32:48.004Z |
| 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 | ba37mfOCJiom |
| 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 |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| 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
On this page 1. What Is Inference?2. The Artifact: What’s Actually in That 10GB Download?The “Manual” (The Header)The “Hardware” (The Tensors)Quantization: Shrinking the Brain3. The Three Phases: A Map Before the Territory4. Tokenization: Chopping Text Into NumbersIs Tokenization CPU-Bound?5. Prefill: The Model Reads Your PromptThe Embedding MatrixHow Do the Model Weights Help Here?6. Positional Embeddings: Teaching the Model About OrderHow Is It Calculated?CPU Bottleneck in Prefill?7. The Transformer Layers: Where the Real Work Happens8. Decoding: One Token at a Time, ForeverDecode Step 1: Predicting “on”Decode Step 2: Predicting “the”The Sampling Step (Where Creativity Lives)9. Why Memory Is the Decode Bottleneck10.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Karthika Raghavan.
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