Legare Kerrison and Cedric Clyburn on LLM Performance and Evaluations

2026-04-28 · Source: InfoQ · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, medium

Summary

Legare Kerrison and Cedric Clyburn from Red Hat recently presented at the Arc of AI 2026 Conference, detailing practical methods for evaluating and optimizing Large Language Model (LLM) inference. They emphasized that while generic leaderboards exist, organizations must evaluate LLMs against their unique business problems and data. The speakers introduced the "tradeoff triangle" of model quality, responsiveness, and cost, explaining how optimizing any two impacts the third. Key metrics for evaluation include Requests Per Second (RPS), Time to First Token (TTFT), and Inter-Token Latency (ITL), with specific Service Level Objectives (SLOs) varying for different use cases like e-commerce chatbots versus RAG applications. They also discussed hardware requirements, inference stages (Prefill and Decode), and optimization techniques such as quantization and KV Caching, highlighting tools like GuideLLM for SLO-aware benchmarking and various open-source evaluation frameworks for model, RAG, and application accuracy.

Key takeaway

For AI Architects designing LLM-powered applications, you must move beyond generic benchmarks and define clear Service Level Objectives (SLOs) tailored to your specific business needs. Focus on the "tradeoff triangle" of quality, latency, and cost, using metrics like TTFT and ITL to guide your model and hardware choices. Your evaluation strategy should incorporate tools like GuideLLM for benchmarking and consider quantization for efficiency to ensure production readiness.

Key insights

Effective LLM deployment requires balancing model quality, responsiveness, and cost through tailored evaluation and optimization.

Principles

• Generic benchmarks are insufficient for unique business problems.
• Optimize for two factors in the "tradeoff triangle" impacts the third.
• SLOs guide structured comparisons and cost optimizations.

Method

Evaluate LLMs by defining application SLOs with metrics like RPS, TTFT, and ITL, then benchmark using tools like GuideLLM, and apply optimization techniques such as quantization and KV Caching.

In practice

• Use GuideLLM for SLO-aware LLM benchmarking.
• Quantize models to reduce size and improve efficiency.
• Implement KV Cache to accelerate decoding.

Topics

• LLM Performance Evaluation
• LLM Inference Optimization
• Service Level Objectives
• Retrieval-Augmented Generation
• Model Quantization

Code references

• vllm-project/guidellm
• EleutherAI/lm-evaluation-harness
• openai/evals
• truera/trulens

Best for: AI Architect, AI Engineer, Machine Learning Engineer, MLOps Engineer

Related on AIssential

• Evaluation: Model Benchmarks and LLM Application Assessment — LLM evaluation requires a multi-faceted approach, combining intrinsic, deterministic, and subjective methods with standardized benchmarks.
• What We’ve Learned From A Year of Building with LLMs — Successful LLM product development requires a holistic approach beyond demos, focusing on robust systems, data, and strategic iteration.
• Cost-Aware Model Orchestration for LLM-based Systems — Integrating quantitative performance and energy metrics into LLM orchestration significantly improves accuracy and efficiency.
• Artificial Analysis: Independent LLM Evals as a Service — with George Cameron and Micah-Hill Smith — Independent benchmarking is crucial for navigating the complex, rapidly evolving AI model landscape and ensuring objective performance assessment.
• Optimize, deploy, and benchmark an open-source LLM with vLLM — Efficient LLM inference is achieved through specialized memory management and serving systems like vLLM.
• The Model You Love Is Probably Just the One You Use — Developer preference for LLMs is often driven by familiarity and integration ease, not objective superiority.

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Editorial summary, takeaway, and curation by AIssential. Original article published by InfoQ.