#efficiency
3 articles
01
·How Transformers Work·★ MEMBER·PAPER·9 min read
A Field Guide to Attention Variants — MQA, GQA, Sliding Windows, Linear Attention
MQA, GQA, sliding windows and linear attention are not four unrelated tricks. One multiplication decides how large a KV cache gets, and every variant is a decision about which factor in it to attack. Lined up against that formula, the family tree shows exactly what each one gave up and what it bought.
02
·CNNs & Image Recognition·★ MEMBER·PAPER·8 min read
Paper Walkthrough: TurboVLA — Kick the LLM Out of the Loop and Run a Robot Policy at 32 Hz on an RTX 4090 with Under 1 GB of VRAM
Mainstream VLA models route every control step through a large language model. TurboVLA removes the LLM and fuses vision and instructions with lightweight bidirectional cross-attention instead — hitting 97.7% on LIBERO with 0.2B parameters, 31.2 ms latency, and 0.9 GB of VRAM. A from-scratch walkthrough of the paper.
03
·Inference & Serving·★ MEMBER·PAPER·8 min read
Speculative Decoding from Scratch — How a Tiny Draft Model Speeds Up an LLM Without Changing a Single Output
Speculative decoding lets a small model draft several tokens ahead while a large model verifies them in one batch — provably identical outputs, 2–3x faster in practice. A from-scratch walkthrough of the mechanism and the intuition behind the acceptance rate α, straight from the original paper.