News

Semiconductor Engineering2d
HW-based Heterogeneous Memory Management for LLM Inferencing (KAIST, Stanford Unversity)
A new technical paper titled “Hardware-based Heterogeneous Memory Management for Large Language Model Inference” was ...
Semiconductor Engineering26d
GPU Analysis Identifying Performance Bottlenecks That Cause Throughput Plateaus In Large-Batch Inference
A new technical paper titled “Mind the Memory Gap: Unveiling GPU Bottlenecks in Large-Batch LLM Inference” was published by researchers ... reducing GPU memory requirements with minimal impact on ...
Microsoft’s “1‑bit” AI model runs on a CPU only, while matching larger systems
Memory requirements are the most obvious advantage of reducing the complexity of a model's internal weights. The BitNet b1.58 ...
Microsoft Releases Largest 1-Bit LLM, Letting Powerful AI Run on Some Older Hardware
Microsoft’s model BitNet b1.58 2B4T is available on Hugging Face but doesn’t run on GPU and requires a proprietary framework.
AppleInsider1mon
A Powerbook G4 is barely fast enough to run a large language model
After checking out the llama2.c project to implement the Llama2 LLM inference with a single vanilla ... its use of 32-bit and a maximum addressable memory of 4GB. While quantization could help ...