AirLLM runs 70B models on a 4GB GPU. The trick is not the sharding; it is that enterprise inference workloads break the edge cost model. Here is why GPU accessibility is not the same thing as cost efficiency.
NPUs promise efficient edge LLM inference, but how do they actually compare to discrete GPUs under real production workloads?
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.
The economic case for deploying local LLMs to eliminate API costs and latency. Why relying entirely on cloud inference is a massive tax on your margins.