Inference Performance as a Competitive Advantage: How to Optimize LLM Serving for Production AI Systems

Watch the Webinar

Inference optimization separates production AI systems from proof-of-concepts, but most teams overlook it until costs spiral.

What This Webinar Covers: LLM Inference Optimization, GPU Cost Reduction, and Production Deployment Strategies

As generative AI moves into production, the bottleneck shifts from training to serving. With 80-90% of GPU resources consumed during inference, the performance of your serving infrastructure directly determines your competitive position, affecting everything from user experience to unit economics.

This session demystifies LLM inference optimization through FriendliAI’s proven approach. You’ll explore the architectural decisions and deployment strategies that enable sub-second response times at scale, and understand why inference performance isn’t just an engineering concern, it’s a business imperative.

This isn’t about squeezing marginal gains from existing infrastructure. It’s about architecting inference pipelines that scale efficiently from day one.

Who Should Watch: ML Engineers, MLOps Practitioners, and Technical Teams Deploying Generative AI in Production

ML/AI Engineers, MLOps Practitioners, and Technical Teams deploying generative AI applications in production who need to balance response speed, infrastructure costs, and system reliability.

Why You Should Watch: Continuous Batching, Speculative Decoding, Caching, and Real Customer Deployment Results

• Grasp why inference optimization becomes critical as AI systems move from prototype to production
• Explore techniques like continuous batching, speculative decoding, and intelligent caching that reduce serving costs by up to 90%
• Understand the FriendliAI infrastructure approach: from custom GPU kernels to flexible deployment models
• Examine real customer deployments and the measurable impact on latency, throughput, and cost
• Walk away with actionable deployment strategies for high-performance LLM serving at scale
• Gain clarity on turning inference efficiency into measurable competitive differentiation

Key Insight: Why Purpose-Built Inference Engines Outperform Generic Serving Infrastructure in Production AI

Most teams optimize model accuracy but deploy on generic serving infrastructure. Production-grade AI systems require purpose-built inference engines that treat serving performance as a first-class design constraint, not an afterthought.

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