FlashAttention

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Scoured 118 posts in 6.1 ms

Gemma 4 QAT models: Optimizing model compression for mobile and laptop efficiency

 💰Inference Cost  Content type: News  Content type: Blog
blog.google··Hacker News

IntentKV: Cross-Turn Intent-Aware KV Cache Pruning for Agent Inference

 🧠Inference Engineering  Content type: Academic
arxiv.org·

google/gemma-4-12B-it-qat-q4_0-gguf

 🧠Inference Engineering
huggingface.co·

harshuljain13/llm-inference-at-scale: A Practitioner handbook for production llm serving.

 💾KV Cache  Content type: Code
github.com··Hacker News

OpenCV 5.0 Computer Vision Library Released with Rewritten DNN Engine

 🧠Inference Engineering
linuxiac.com·

Efficient and Training-Free Single-Image Diffusion Models

 ⚗️Kernel Fusion

How the UK Is Turning Sovereign AI Ambition Into Action With NVIDIA Technologies

 🧠Inference Engineering  Content type: Blog
blogs.nvidia.com·

Express Language Modeling

 🧠Inference Engineering  Content type: Academic
arxiv.org·

Issue #390 - The ML Engineer 🤖

 💰Inference Cost  Content type: News  Content type: Blog

OpenCV 5 release - New DNN engine with enhanced ONNX and LLM/VLM support, Intel, Arm, and RISC-V hardware optimizations - CNX Software

 🧠Inference Engineering  Content type: News
cnx-software.com·

princezuda/-RequiemGPT-: Fully open source and open weights built and trained by fable five with one prompt. An experience in how AI actually works

 🧠Inference Engineering  Content type: Code
github.com··Hacker News

End-to-End Context Compression at Scale

 🧠Inference Engineering  Content type: Academic
arxiv.org·

See, Act, Correct: three levers for working with a code agent

 🧠Inference Engineering  Content type: Blog

Where to Host Your Open-Source Model (Under 10B Parameters)

 🧠Inference Engineering
digitalocean.com·

FlashMemory-DeepSeek-V4: Lightning Index Ultra-Long Context via Lookahead Sparse Attention

 🧠Inference Engineering  Content type: Academic
arxiv.org··Hacker News

DeepSeek V4, LeCun's Bet Against LLMs, and Lovable's Self-Improving Agent - The Tokenizer Edition #30

 🔢FP8 Training

bigattichouse/packed-twin-inference: PTI achieves ~2× throughput using a single quantized model (Q5_K_M or better) by running 4 generation streams in one batched decode call. The GPU loads model weights once per step and produces 4 predictions simultaneously. KV cache overhead is ~0.8 GiB total for all 4 streams. No draft model. No quality loss

 🧠Inference Engineering  Content type: Code
github.com··r/LocalLLaMA

Full Context on a Vulkan-Only Strix Halo: The Decode-Drop Reproduces, but the Sweet Spot Moves

 ⏱️Prefill Decoding

Attention at the Theoretical Minimum: A Mathematics of Arrays Framework for Memory-Optimal Transformer Kernels

 ⚗️Kernel Fusion  Content type: Academic
arxiv.org·

KJLdefeated/RL.cu: RLVR training for LLM in CUDA/C++

 💾KV Cache  Content type: Code
github.com··Hacker News
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