Search for a command to run...
Articles tagged with #ai-infrastructure
Every production LLM deployment using speculative decoding is likely running a fixed speculation length of γ=4. That number comes from early benchmarks, it has been copy-pasted across blog posts and framework defaults, and almost nobody questions it....
At Google Cloud Next 2026 (April 22), Google announced something it had never done before: two different eighth-generation TPU chips with distinct silicon designs for distinct jobs. TPU 8t handles training. TPU 8i handles inference. The split is a ha...
Every production AI application hits the same wall eventually. You start calling OpenAI directly, usage grows, costs spike unpredictably, you have no idea which requests are slow, and when OpenAI has an outage your whole product goes down. The standa...
## Why Llama Stack Changes the Open-Source Deployment Story Running open-source LLMs in production has always had a catch: you pick a backend (Ollama, vLLM, llama.cpp), write your integration code against that backend's specific API, and then find y...
Serving a large language model in production is a solved problem — until your traffic doubles, your structured output pipeline slows to a crawl, or your cloud bill arrives. The choice of inference engine determines how many GPUs you actually need, ho...
Why Every Production AI Team Needs an LLM Gateway Enterprise spending on LLM APIs surpassed $8.4 billion in 2026. The teams burning the most aren't the ones with the largest models — they're the ones without a routing layer between their code and the...
