A new technical paper titled “Pushing the Envelope of LLM Inference on AI-PC and Intel GPUs” was published by researcher at ...
Running both phases on the same silicon creates inefficiencies, which is why decoupling the two opens the door to new ...
This brute-force scaling approach is slowly fading and giving way to innovations in inference engines rooted in core computer ...
A research article by Horace He and the Thinking Machines Lab (X-OpenAI CTO Mira Murati founded) addresses a long-standing issue in large language models (LLMs). Even with greedy decoding bu setting ...
Google researchers have revealed that memory and interconnect are the primary bottlenecks for LLM inference, not compute power, as memory bandwidth lags 4.7x behind.
In a blog post today, Apple engineers have shared new details on a collaboration with NVIDIA to implement faster text generation performance with large language models. Apple published and open ...
Forged in collaboration with founding contributors CoreWeave, Google Cloud, IBM Research and NVIDIA and joined by industry leaders AMD, Cisco, Hugging Face, Intel, Lambda and Mistral AI and university ...
“Large Language Model (LLM) inference is hard. The autoregressive Decode phase of the underlying Transformer model makes LLM inference fundamentally different from training. Exacerbated by recent AI ...