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 ...
Researchers propose low-latency topologies and processing-in-network as memory and interconnect bottlenecks threaten inference economic viability ...
Serving Large Language Models (LLMs) at scale is complex. Modern LLMs now exceed the memory and compute capacity of a single GPU or even a single multi-GPU node. As a result, inference workloads for ...
Join the event trusted by enterprise leaders for nearly two decades. VB Transform brings together the people building real enterprise AI strategy. Learn more Researchers from Stanford University‘s ...
“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 ...
A new technical paper titled “Efficient LLM Inference: Bandwidth, Compute, Synchronization, and Capacity are all you need” was published by NVIDIA. “This paper presents a limit study of ...
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 ...
Kubernetes has become the leading platform for deploying cloud-native applications and microservices, backed by an extensive community and comprehensive feature set for managing distributed systems.
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Dell has just unleashed its new PowerEdge XE9712 with NVIDIA GB200 NVL72 AI servers, with 30x faster real-time LLM performance over the H100 AI GPU. Dell Technologies' new AI Factory with NVIDIA sees ...