New Atlas

Next-level AI engine comes top in LLM speed showdown

Responses to AI chat prompts not snappy enough? California-based generative AI company Groq has a super quick solution in its LPU Inference Engine, which has recently outperformed all contenders in ...
NextBigFuture

Test Time Training Will Take LLM AI to the Next Level

MIT researchers achieved 61.9% on ARC tasks by updating model parameters during inference. Is this key to AGI? We might reach the 85% AGI doorstep by scaling and integrating it with COT (Chain of ...
NextBigFuture

OpenAI Strawberry LLM Reasoning Needs More Compute and Energy for Inference

Jim Fan is one of Nvidia’s senior AI researchers. The shift could be about many orders of magnitude more compute and energy needed for inference that can handle the improved reasoning in the OpenAI ...
TechRepublic

NVIDIA Boosts LLM Inference Performance With New TensorRT-LLM Software Library

NVIDIA Boosts LLM Inference Performance With New TensorRT-LLM Software Library Your email has been sent As companies like d-Matrix squeeze into the lucrative artificial intelligence market with ...
SDxCentral

AI inference crisis: Google engineers on why network latency and memory trump compute

Researchers propose low-latency topologies and processing-in-network as memory and interconnect bottlenecks threaten inference economic viability ...
Semiconductor Engineering

LLM Inference: Core Bottlenecks Imposed By Memory, Compute Capacity, Synchronization Overheads (NVIDIA)

A new technical paper titled “Efficient LLM Inference: Bandwidth, Compute, Synchronization, and Capacity are all you need” was published by NVIDIA. Abstract “This paper presents a limit study of ...
TweakTown

Dell PowerEdge XE9712: NVIDIA GB200 NVL72-based AI GPU cluster for LLM training, inference

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 ...
Semiconductor Engineering

Pooling CPU Memory for LLM Inference With Lower Latency and Higher Throughput (UC Berkeley)

“The rapid growth of LLMs has revolutionized natural language processing and AI analysis, but their increasing size and memory demands present significant challenges. A common solution is to spill ...
VentureBeat

Inference framework Archon promises to make LLMs quicker, without additional costs

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 ...