Gemma 4 로컬 추론의 병목: Memory Bandwidth와 KV Cache 오버플로우
The Brutal Reality of Running Gemma 4 Locally
Nemotron-Labs Diffusion 도입으로 LLM Throughput 6.4배 달성
Diffusion Language Models: How NVIDIA Nemotron-Labs Diffusion Shatters the Autoregressive Speed Ceiling
128GB 통합 메모리로 200B 파라미터 로컬 추론 구현
AMD says its $4K Ryzen AI Halo workstation practically pays for itself
128GB Unified Memory 기반 Local LLM 추론 최적화 워크스테이션 설계
AMD says its $4K Ryzen AI Halo workstation practically pays for itself
Wafer-Scale Engine 통한 21 PB/s 대역폭 및 초고속 LLM 추론 달성
Cerebras risked it all on dinner plate-sized AI accelerators a decade ago. Today it’s worth $66 billion
Agentic Workload 최적화를 위한 TPU 8T/8I 하드웨어 이원화 전략
TPUs for the Agentic Era: Hardware Finally Catching Up to the Workload
M5 Max 기반 TurboQuant 적용으로 35B 모델 1M 토큰 컨텍스트 구현
TurboQuant on a MacBook Pro: two findings the upstream discussion missed
Qwen3.6-35B MoE 모델로 M5 Max에서 Aider Polyglot 62.8% 달성
62.8% on Aider Polyglot from a MacBook Pro. Then the other model we tried scored 4%. Here's what actually happened, with a working cost loop attached.
AMD Strix Halo 기반 MoE 추론 460 t/s 달성 및 Speculative Decoding 함정 분석
Breaking the MoE Speculative Trap: 460 t/s on AMD Strix Halo
Apple Silicon 메모리 대역폭 병목 해결을 통한 MLX 추론 속도 최대 87% 향상
Apple Silicon LLM Inference Optimization: The Complete Guide to Maximum Performance
소비자용 GPU에서 Speculative Decoding이 무용지물인 이유
I tested speculative decoding on my home GPU cluster. Here's why it didn't help.