You're Not Paying for Compute. You're Paying for Memory Bandwidth
TL;DR— Inference cost conversations obsess over FLOPs and token prices, but the real constraint on LLM serving is memory bandwidth— specifically the c…
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TL;DR— Inference cost conversations obsess over FLOPs and token prices, but the real constraint on LLM serving is memory bandwidth— specifically the c…
It's early, but the plan is to reduce dependency on Nvidia and Huawei.
Diffusion text models — which draft an entire block of text at once and then iteratively refine it, rather than generating one token at a time left to…
Every LLM inference engineer hits this wall eventually. You deployed a model, it works in testing, then production traffic arrives. Suddenly your 80GB…
One of the hottest topics in LLM inference acceleration right now is Speculative Decoding . DSpark claims 60%–85% single-user speedup at the same thro…
Messy text is everywhere: support tickets, lead forms, emails, contracts, incident reports, call notes, Slack messages. The annoying part is that the …
GPU programming usually asks Rust developers to surrender the borrow checker at the launch boundary: references collapse into raw pointers, and aliasi…
The silicon race is heating up amid the struggle to keep up with demand.
Over the past few months, I had the opportunity to contribute to llama.cpp’s WebGPU backend, helping push it from isolated operator support toward a m…
If you call an open-weight model behind an API, whether that is your own box, a hosted endpoint, or a router, you are trusting that the thing answerin…
We’ve treated local AI deployments as experimental toys for too long. The moment a homelab becomes a dependency for work, the security posture must sh…
Speculative decoding: when and why it actually speeds up inference Your chat endpoint serves 200 requests per second. The model is a 70B Llama 3 fine-…
Crescent Island is an air-cooled chip that uses LPDDR5 memory.