EDN

The hidden bottleneck in LLM inference and the impact on MLPerf benchmarking

Here is how the prefill versus generation split exposes GPU structural inefficiencies in AI processor designs.
InfoQ

Cactus v1: Cross-Platform LLM Inference on Mobile with Zero Latency and Full Privacy

A monthly overview of things you need to know as an architect or aspiring architect. Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with ...
Semiconductor Engineering

Characterization of GPU-based Inference for Reasoning-Centric LLMs (Micron, Argonne)

Researchers from Micron Technology and Argonne National Laboratory have released “Understanding Inference Scaling for LLMs: ...
NextBigFuture

Defeating Nondeterminism in LLM Inference by Thinking Machines

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 ...
VentureBeat

Large language model expands natural language understanding, moves beyond English

Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders. Subscribe Now One of the primary use cases for artificial ...
Semiconductor Engineering

LLM Inference On CPUs (Intel)

“Large language models (LLMs) have demonstrated remarkable performance and tremendous potential across a wide range of tasks. However, deploying these models has been challenging due to the ...
InfoQ

JLama: The First Pure Java Model Inference Engine Implemented With Vector API and Project Panama

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Dany Lepage discusses the architectural ...
SDxCentral

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

Google researchers have warned that large language model (LLM) inference is hitting a wall amid fundamental problems with memory and networking problems, not compute. In a paper authored by ...
NextBigFuture

Tokens and Tokenization are an Important for Fundamental LLM Understanding

Tokens are the fundamental units that LLMs process. Instead of working with raw text (characters or whole words), LLMs convert input text into a sequence of numeric IDs called tokens using a ...
VentureBeat

Fine-tuning vs. in-context learning: New research guides better LLM customization for real-world tasks

Two popular approaches for customizing large language models (LLMs) for downstream tasks are fine-tuning and in-context learning (ICL). In a recent study, researchers at Google DeepMind and Stanford ...