Latest Blog
How Does Maximal Marginal Relevance Reduce Redundant RAG Evidence?
MMR reranks a candidate pool by rewarding relevance while penalizing similarity to passages already selected, leaving more room for independent evidence.
How Does Pipeline Parallelism Move Model Layers Across Home AI Accelerators?
Pipeline parallelism places different layer groups on different accelerators and moves activations through them in order, reducing per-device model memory.
How Does Tensor Parallelism Split Local AI Inference Across Multiple GPUs?
Tensor parallelism splits layer tensors across GPUs, so each device computes a shard and collective communication reconstructs the logical result.
How Does CPU Offloading Keep Oversized Models Running on a Home Server?
CPU offloading keeps oversized models runnable by storing part of their weights in system RAM and moving needed tensors to the accelerator during execution.
How Does Mixture-of-Experts Routing Change Local Model Compute?
MoE routing activates selected experts per token, reducing expert-layer compute relative to total parameters while the full expert set remains stored.
How Does Flash Attention Reduce Memory Traffic During Local Inference?
Flash Attention keeps tiled attention work on-chip and avoids materializing the full score matrix, reducing HBM traffic without approximating attention.
How Does Paged Attention Manage KV Cache Under Concurrent Local AI Requests?
PagedAttention maps each request’s logical KV history onto fixed-size memory blocks, allocating and recycling capacity as concurrent sequences change.
How Does Prefix Caching Reduce Repeated Prompt Work on a Home AI Server?
Prefix caching reuses attention state for a shared prompt prefix, so later requests compute only the uncached suffix before normal decoding continues.
