AI infrastructure is entering a new phase focused on rack-scale system composition for agentic AI workflows, where CPUs play critical orchestration roles alongside accelerators. The shift from single-…
AI engineering faces a contradiction between paying premium for frontier models' reasoning capabilities and aggressively compressing context to reduce costs, leading to a 'fallacy of context compactio…
Researchers developed XBAR, an auditable latent crossbar memory fabric that enables model-to-model communication by writing directly into a frozen transformer's KV cache. The system achieves O(1) VRAM…
A developer calculated the actual VRAM requirements for running Llama 3 8B and Gemma 2 9B locally, revealing that the KV cache can consume far more memory than the model weights, especially at longer …
A developer implemented a full Transformer with KV cache on an FPGA, achieving over 56,000 tokens per second at only 80 MHz, without using a GPU or CPU. The design was created gate by gate as a custom…
PagedAttention, a memory optimization technique in the vLLM inference server, applies virtual memory concepts to manage the KV cache in large language models, improving throughput by reducing fragment…
Quant Picker is a new tool that calculates which GGUF quantization level fits a given model and machine, balancing file size, quality, and context budget. It recommends the highest quantization that l…
A hidden bottleneck in LLM proxy architectures is causing 5-13 millisecond blocking delays per request during tokenization, a CPU-bound operation that most systems treat as instantaneous. In event-loo…
Based on the article, the authors replaced their RAG pipeline with a persistent KV cache system, which stores the full document's attention state after a single prefill and reuses it for every query. …
Running large language model inference servers like vLLM and TGI in production requires specialized observability because they behave differently from standard web services, with key metrics like late…
The KV cache is a critical optimization for LLM inference that stores the Key and Value matrices from previously generated tokens, eliminating the need to recompute attention over the entire sequence …
Synchronous continuous batching in LLM inference causes inefficiency by forcing the CPU and GPU to work sequentially, leaving one idle while the other operates. This idle time can account for nearly a…