How much more inference could you get from the GPUs you already have?
As inference workloads grow, it’s not always the GPU compute that becomes the bottleneck. Recomputing tokens of a known context vs reloading from KV cache can take up a lot of GPU bandwidth and limit how many workloads you can run in a given amount of time. That’s one of the things we’ve been working on with PureKVA, reusing KV cache so you can get more out of the GPU capacity you already have. With v1.2, we’ve also added a 3-tier memory architecture, TurboQuant KV cache support, along with things like multi-tenancy and storage isolation. We’re seeing some of the benefits internally as well, particularly around reducing throttling when usage spikes. Curious what others are seeing. Is GPU compute the bigger constraint for you, or GPU memory?Curious about home lab or on-prem models
A friend told me to stop trying to squeeze local models onto my gaming PC and just buy the right box. So I did, and hoping not to regret it. Ordered a BOSGAME M5. Ryzen AI Max+ 395, 128GB unified memory, up to 96GB of it assignable to the GPU. Size of a hardcover book. I've been running LM Studio with models off Hugging Face. Fine for small stuff. So, people who've actually done this at home... What are you running besides LM Studio? What hardware did you buy and regret? Where does unified memory fall apart in practice? One ask. Human answers only. I can already ask a model what a model thinks. I want to know what broke in your house at 11pm on a Tuesday.What’s your first thought when you hear about the next big AI breakthrough?
AI is moving fast, and it feels like every week there’s something new. What’s your first thought when you hear about the next big AI breakthrough? Excitement? Curiosity? Or maybe a little bit of ‘uh-oh, what now? Discover how DeepSeek is reshaping AI with specialized models and intelligent data management.
