ALPHA: 96.4 arXiv:2610.08942
• 2026-10-19
24 min read
Dual-Chunk Linear Attention
Summary
-75% KV VRAM · 2.5x Speedup | ARCH_MODULE
Read Teardown & Code → RESEARCH LIBRARY
Flagship Technical Dispatches
Exhaustive first-principles architectural teardowns, LaTeX mathematical derivations, and verified runnable PyTorch/Triton implementations.
ALPHA: 95.8 arXiv:2502.12874
• 2026-10-19
22 min read
DeepSeek-V3 Multi-Head Latent Attention & FP8 Triton Kernel Deconstruction
Multi-Head Latent Attention (MLA) decouples key-value caching from head counts by compressing Key-Value projections into a low-rank latent vector (d_c = 512). Independent lab benchmarking confirms 73.2% VRAM reduction while matching standard Multi-Head Attention expressivity on Hopper H100 Tensor Memory Accelerators.
-73.2% KV VRAM · 2.4x Speedup | Latent Attention / MoE
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ALPHA: 92.1 arXiv:2407.08608
• 2026-10-19
18 min read
Speculative Token Decoupling: Asynchronous Verification via Flash-Decoding
Autoregressive LLM generation is fundamentally memory-bandwidth bound. Speculative Flash-Decoding breaks serial execution by decoupling draft generation from large model verification passes using an asynchronous double-buffered ring buffer, delivering 2.8x wall-clock latency reduction.
-35% KV VRAM · 2.8x Speedup | Speculative Decoding
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ALPHA: 91.8 arXiv:2410.02525
• 2026-10-19
20 min read
BitNet b1.58: 1-Bit LLM Architectures with Dynamic Precision Scaling
Every standard neural network computes matrix products via power-hungry floating-point MAC units. BitNet b1.58 constrains all linear layer weights to ternary values {-1, 0, +1}, turning matrix multiplications into integer additions and subtractions. Slashes dynamic inference energy by 82% and fits a 70B parameter model within 36 GB of unified memory.
-78.5% KV VRAM · 3.8x Speedup | Quantization / Integer Computing
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