LiteRT-LM Deep Dive: Engineering LLM Inference for the Edge
How Google's LiteRT-LM framework handles session cloning and KV-cache management to run models like Gemini Nano natively on-device without exploding your memory.
AI Infrastructure
Scaling Vector Databases for High-Throughput Text Clustering
Analyzing the bottleneck of bulk clustering and using exact-match caching to reduce index compute load.
Breaking the Bandwidth Wall: Why AI Clusters are Shifting to Ultra Ethernet
To scale past 100k GPUs, the industry is replacing proprietary InfiniBand with AI-optimized Ultra Ethernet.
Multi-Cloud GPU Arbitrage: Routing Workloads Between Hyperscalers and Neoclouds
Don't lock into one vendor. Learn how to use an abstraction layer to route training and inference workloads to the cheapest available capacity across hyperscalers and neoclouds.
Semantic Caching at Scale: Vector Embeddings for 5x Latency Reduction
Moving beyond exact-match caching for repetitive zero-shot inference workloads. Learn how to architect semantic caching to slash latency and compute costs.
Rack-Scale AI Design: The End of Component Scaling
We have hit the physical limits of what a single chip can do. The new unit of compute for AI infrastructure isn't the GPU; it's the fully integrated rack.