#systemdesign

Scalar math loops waste modern hardware. Tensor Cores execute tiled matrix blocks in a single pass.

To prevent memory stalls, align your tensor layouts to 16-byte boundaries for coalesced global loads.

#AI #SystemDesign #SoftwareEngineering #GPU

Logits are raw, unscaled vector scores—akin to arcade points before global ranking percentages are computed. They determine relative preference, not absolute probability, until normalized via Softmax.

#AI #MachineLearning #SystemDesign #SoftwareEngineering

Never recompute what you can remember; memory is cheap compared to matrix multiplication loops.

KV caching stores past token attention states so autoregressive inference avoids O(n²) redundant math. Like keeping your scratchpad during long division.

#AI #SystemDesign #LLMs

KV caching trades memory for compute in autoregressive LLMs. Instead of recomputing Key-Value pairs for past tokens at each step, we retain them in VRAM.

Like keeping intermediate math steps on a scratchpad. O(N²) to O(1) step latency.

#SystemDesign #AI

CER/WER miss the point. Financial doc recognition demands fact-preservation, not character matching. New risk-constrained evaluation shifts focus to critical errors and selective automation.

Benchmarks: https://app.neubench.workers.dev #AI #MachineLearning #SystemDesign

On-Policy Self-Distillation works until cross-lingual divergence breaks teacher conditioning. Teachable prefixes solve this by aligning reasoning trajectories before student divergence.

Benchmarks: https://app.neubench.workers.dev
#AI #MachineLearning #SystemDesign

Scaling LLM training? Stop broadcasting full model states. Ring-AllReduce partitions tensors across a logical ring so per-GPU bandwidth approaches 2x data size—independent of node count.

Like a relay writing circle: pass local fragments, scale globally.

#SystemDesign #AI