The Inference Engineering Masterclass — Philip Kiely & Ali Taha, Baseten
- ID
- 10531
- Status
- summarized
- Published
- 04 Aug 2026, 5:44 AM
- Fetched
- 04 Aug 2026, 5:56 AM
- Provider
- Latent Space
- Category
- developer-ai
- Original URL
- https://www.latent.space/p/inference-eng
- Source URL
- https://www.latent.space/feed
Summary
- Score
- 7.0
- Created
- 07 Aug 2026, 1:32 PM
- Tags
- Audience
- developersai_ml_learnersai_agent_users
What happened
Baseten's Philip Kiely and Ali Taha present a masterclass on inference engineering, covering autoregressive and diffusion model deployment. Ali recently published a viral deep-dive into Kimi K3's model code (tracing its full lineage across 8 papers), while Philip authored what's described as the definitive book on inference engineering — the discipline of turning trained weights into fast, reliable, affordable production systems.
Why it matters
If you ship LLM or diffusion models to production, inference engineering is where your latency, cost, and reliability are actually won or lost. The Kimi K3 code breakdown and the inference engineering book are concrete references for understanding how modern open-weight models are structured and how to optimize serving — worth reviewing before your next model deployment or vendor evaluation.
Discussion angle
What inference engineering techniques from the Kimi K3 breakdown could apply to models you're currently serving, and where does the open-weights debate leave teams choosing between self-hosting and managed inference platforms like Baseten?