Markdown source

Inference Engineering

Overview

Inference engineering is the practice of making AI model serving reliable, fast, cost-aware, and fit for product constraints. It covers model selection, batching, caching, routing, quantization, GPU utilization, latency budgets, observability, and fallback behavior.

Conference Context

It extends production ML serving, distributed systems, GPU infrastructure, and web-performance engineering. LLMs added new constraints: token streaming, long prompts, context caching, tool latency, and rapidly changing model/provider economics.

Significance

The same prompt can be unusable or profitable depending on latency, throughput, context size, and cost. Inference engineering turns model capability into a dependable product surface.

Applied Use

Measure end-to-end latency and token costs, separate prefill from generation costs, cache stable context, route tasks to the smallest adequate model, batch where possible, and monitor quality regressions when optimizing speed or cost.

It matters in chat products, coding agents, voice agents, search and RAG systems, enterprise assistants, on-device AI, and high-volume API products.

Invest in inference engineering once prototypes need predictable user experience, margins, scale, or reliability. It becomes critical when workloads are high-volume, latency-sensitive, or model-provider dependent.

Measure end-to-end latency and token costs, separate prefill from generation costs, cache stable context, route tasks to the smallest adequate model, batch where possible, and monitor quality regressions when optimizing speed or cost.

It matters in chat products, coding agents, voice agents, search and RAG systems, enterprise assistants, on-device AI, and high-volume API products.

Invest in inference engineering once prototypes need predictable user experience, margins, scale, or reliability. It becomes critical when workloads are high-volume, latency-sensitive, or model-provider dependent.

Measure end-to-end latency and token costs, separate prefill from generation costs, cache stable context, route tasks to the smallest adequate model, batch where possible, and monitor quality regressions when optimizing speed or cost.

It matters in chat products, coding agents, voice agents, search and RAG systems, enterprise assistants, on-device AI, and high-volume API products.

Invest in inference engineering once prototypes need predictable user experience, margins, scale, or reliability. It becomes critical when workloads are high-volume, latency-sensitive, or model-provider dependent.

Measure end-to-end latency and token costs, separate prefill from generation costs, cache stable context, route tasks to the smallest adequate model, batch where possible, and monitor quality regressions when optimizing speed or cost.

It matters in chat products, coding agents, voice agents, search and RAG systems, enterprise assistants, on-device AI, and high-volume API products.

Invest in inference engineering once prototypes need predictable user experience, margins, scale, or reliability. It becomes critical when workloads are high-volume, latency-sensitive, or model-provider dependent.

Measure end-to-end latency and token costs, separate prefill from generation costs, cache stable context, route tasks to the smallest adequate model, batch where possible, and monitor quality regressions when optimizing speed or cost.

It matters in chat products, coding agents, voice agents, search and RAG systems, enterprise assistants, on-device AI, and high-volume API products.

Invest in inference engineering once prototypes need predictable user experience, margins, scale, or reliability. It becomes critical when workloads are high-volume, latency-sensitive, or model-provider dependent.

Measure end-to-end latency and token costs, separate prefill from generation costs, cache stable context, route tasks to the smallest adequate model, batch where possible, and monitor quality regressions when optimizing speed or cost.

It matters in chat products, coding agents, voice agents, search and RAG systems, enterprise assistants, on-device AI, and high-volume API products.

Invest in inference engineering once prototypes need predictable user experience, margins, scale, or reliability. It becomes critical when workloads are high-volume, latency-sensitive, or model-provider dependent.

Connections

Evidence Graph

Transcript-backed resources

Transcript-backed resources

Transcript-backed resources

Transcript-backed resources

Transcript-backed resources

Quote signals

Transcript-backed resources

Quote signals

This evidence graph consolidates scheduled talks, linked videos, transcripts, and slide-derived material connected to this topic.

Linked Sessions

Media Signals

Source Coverage

This table summarizes the local evidence already linked from this topic. It is a navigation aid, not a claim that every linked page has been fully reviewed.

| Evidence type | Count | Review note |
| --- | ---: | --- |
| other | 58 | Related pages outside the main evidence categories. |
| resources | 21 | Video/resource pages; check source status before treating as primary event evidence. |
| slides | 11 | OCR or reconstructed slide evidence; mark claims as OCR-derived unless image-reviewed. |
| talks | 24 | Official schedule pages; use for titles, speakers, tracks, and stated talk framing. |
| transcripts | 1 | Transcript markdown; check session matching and caption quality. |

Talks

Resources

Slides

Transcripts

This table summarizes the local evidence already linked from this topic. It is a navigation aid, not a claim that every linked page has been fully reviewed.

| other | 60 | Related pages outside the main evidence categories. |
| resources | 22 | Video/resource pages; check source status before treating as primary event evidence. |
| talks | 25 | Official schedule pages; use for titles, speakers, tracks, and stated talk framing. |

Talks

Resources

Slides

Transcripts

This table summarizes the local evidence already linked from this topic. It is a navigation aid, not a claim that every linked page has been fully reviewed.

| other | 62 | Related pages outside the main evidence categories. |
| resources | 23 | Video/resource pages; check source status before treating as primary event evidence. |

Talks

Resources

Slides

Transcripts

This table summarizes the local evidence already linked from this topic. It is a navigation aid, not a claim that every linked page has been fully reviewed.

Talks

Resources

Slides

Transcripts

This table summarizes the local evidence already linked from this topic. It is a navigation aid, not a claim that every linked page has been fully reviewed.

Talks

Resources

Slides

Transcripts

This table summarizes the local evidence already linked from this topic. It is a navigation aid, not a claim that every linked page has been fully reviewed.

Talks

Resources

Slides

Transcripts

This table summarizes the local evidence already linked from this topic. It is a navigation aid, not a claim that every linked page has been fully reviewed.

Talks

Resources

Slides

Transcripts

This table summarizes the local evidence already linked from this topic. It is a navigation aid, not a claim that every linked page has been fully reviewed.

| other | 63 | Related pages outside the main evidence categories. |
| resources | 24 | Video/resource pages; check source status before treating as primary event evidence. |

Talks

Resources

Slides

Transcripts

This table summarizes the local evidence already linked from this topic. It is a navigation aid, not a claim that every linked page has been fully reviewed.

| resources | 25 | Video/resource pages; check source status before treating as primary event evidence. |
| slides | 12 | OCR or reconstructed slide evidence; mark claims as OCR-derived unless image-reviewed. |

Talks

Resources

Slides

Transcripts

Active Use Cases

Slide-Derived Supporting Decks

These decks are slide/OCR support only; keep the article synopsis, origin, use cases, and schedule sections as the primary topic narrative.