11 articles in this category
You've got a model that's too steady. token drip out one by one, and your users are staring at a spinner. You've heard speculative decod can fix it—bu...
Benchmarks lie. Not maliciously, but they do. A model that crushes your offline eval can fall apart the moment real users hit it—latency spikes, memor...
So you've got a multi-tenant inference service. Maybe it's a chatbot API used by startups, or a vision model serving e-commerce catalogs. Users pile i...
So you're staring at two promising inference optimizations—speculative decoding and tree attention—but your hardware profile is a blank page. No FLOPs...
You've optimized model weights, fused kernels, and quantized to 4-bit. But inference still stalls — not in compute, but in memory. The KV cache, stori...
You've got an LLM in production. Latency's creeping up, throughput is plateauing, and your team is debating where to spend the next sprint. Do you att...
You've got a model with branches — maybe a conditional skip connection, a dynamic routing layer, or a multi-head attention that only fires on certain ...
Imagine you've got three model in a row: a classifier, a summarizer, and a sentiment scorer. Requests come in, churn through each, and something's ste...
You trained a model. It worked. Then you quantized it to INT8 and suddenly the outputs look like they came from a different neural network. I have bee...
You have a pipeline that sort of works. Some request fly through in 30 milliseconds; others hang for three second. The cluster dashboard shows 60 perc...
You have a model. It works. Then someone presses "send" a thousand times a second, and your server folds like wet cardboard. The latency spi...