8bit.tr Journal
AI Inference Optimization Stack: Latency, Cost, and Quality
A production-focused guide to optimizing AI inference with batching, caching, quantization, and routing strategies.
Latency Is a Product Feature
Users judge AI tools by speed. A correct answer that arrives late is still a bad experience.
Inference optimization is not just cost cutting. It is how you protect usability at scale.
Batching and Scheduling
Batching improves GPU utilization, but it can increase tail latency.
Use adaptive batching to balance throughput with responsiveness for interactive tasks.
Caching for Repeated Requests
Many AI queries repeat patterns. Caching top queries or embeddings can reduce cost dramatically.
Cache invalidation must respect freshness and user-specific data to avoid incorrect responses.
Quantization and Distillation
Quantization reduces model size and speeds up inference with minimal quality loss.
Distillation creates a smaller model that mimics a larger one, often with strong performance for narrow tasks.
Routing and Model Tiers
Not every request needs the largest model. Route simple tasks to smaller models.
Tiering lowers cost and improves latency while preserving quality for complex queries.
Observability and SLOs
Optimization without visibility is guesswork. Track end-to-end latency, token throughput, cache hit rate, and cost per request. Break these metrics down by model tier so you can see which workloads drive cost and which optimizations actually move the needle.
Define a simple SLO such as P95 latency under two seconds for interactive tasks. Use that SLO to guide batching, routing, and fallback behavior. When the system approaches the limit, shed load or route to smaller models to keep the user experience stable.
Alert on budget burns as well as errors. Spikes in token usage can be just as damaging as downtime.
Review dashboards weekly with product and engineering. Shared visibility aligns performance work with user impact.
Separate batch jobs from interactive traffic to protect tail latency.
FAQ: Inference Optimization
What is the quickest win? Caching frequent results and using smaller models for routine tasks.
Does quantization hurt quality? It can, but careful testing often shows minimal impact.
Should I prioritize latency or cost? Start with latency for user-facing products, then optimize cost.
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