GoKawiilBaseten explains how inference engineers trade off latency, throughput and cost for LLMs
A technical breakdown describes the 'efficient frontier' concept in LLM inference, distinguishing techniques that shift performance along a fixed tradeoff curve—like sacrificing latency for throughput or intelligence for speed—from techniques that expand the frontier itself, creating more overall efficiency. Examples cited include quantization, distillation, pruning, and reasoning-level adjustments, applied in the context of running large agentic coding models such as GLM-5.3 or Kimi K3 with KV cache reuse.
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As LLM deployment costs scale, understanding which optimizations merely reallocate existing resources versus which create genuinely new efficiency is critical for companies deciding how to serve latency-sensitive users versus high-throughput batch workloads. This framework helps engineering teams prioritize investments that expand capacity rather than just shuffle existing tradeoffs.
- The 'efficient frontier' concept, borrowed from economics, frames LLM inference as a tradeoff between latency, throughput, and quality.
- Some techniques only move a deployment along the existing tradeoff curve, while others expand the curve itself, unlocking new efficiency.
- The analysis is framed around real-world agentic coding use cases with models like GLM-5.3 and Kimi K3 using KV cache reuse.
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See Designing Machine Learning Systems by Chip Huyen on Amazon → Affiliate link — we may earn a commission on purchases, at no extra cost to you. Product picked by AI based on this article; it is not a tested recommendation.Source: baseten.co, 2026-09-01
Published there as: “The efficient frontier of LLM inference”
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