CUDA

GPU programming, parallel computing, NVIDIA, kernel launch, GPU acceleration

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A system programmer’s guide to LLM inference

 🔤Tokenization  Content type: Blog

Anatomy of a high-performance EP kernel

 🧠LLM Inference  Content type: Blog

Density Field State Space Models: 1-Bit Distillation, Efficient Inference, and Knowledge Organization in Mamba-2

 🔬Deep Learning  Content type: Academic
arxiv.org·

Why Compiler Engineers Rarely Use Strassen's Algorithm for Fast Matrix Multiplications

 🔬Deep Learning  Content type: News  Content type: Blog

smile/deep at master · haifengl/smile

 🔥PyTorch  Content type: Code
github.com··Hacker News

A Scalable PyTorch Abstraction for Multi-GPU Gaussian Splatting

 🔬Deep Learning  Content type: Academic
arxiv.org·

Symbolica 2.0: programmable symbols, JIT evaluators, and type-erased callbacks in Rust

 🔬Deep Learning

NetX-lab/Frontier: Frontier: A Discrete-Event Simulator for Modern LLM Serving

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

Toward Compiler World Models: Learning Latent Dynamics for Efficient Tensor Program Search

 🔥PyTorch  Content type: Academic
arxiv.org·

Show HN: One-Shot Program Generation Through Direct Memory Diffusion

 🔥PyTorch  Content type: Code
github.com··Hacker News

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

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

Does anyone know what PCIe mode was used for these benchmarks?

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

DeployBench: Benchmarking LLM Agents for Research Artifact Deployment

 🔬Deep Learning  Content type: Academic
arxiv.org·

linzhiqiu/t2v_metrics: Evaluating text-to-image/video/3D models with VQAScore

 🧠Neural Networks  Content type: Code
github.com··Hacker News

heterodoxin/graphkv: Graph-guided KV cache compression for memory-efficient LLM inference.

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

I wired a fully offline voice loop to Ollama + LM Studio — 100% CPU, no GPU, nothing leaves your machine (Silero VAD + Parakeet STT + Supertonic TTS 3)

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

KaiFelixBennett/gemma4-turboquant-rdna4: Run Gemma-4-31B at full 256K context on a $1,400 AMD RDNA4 GPU (gfx1201): TurboQuant KV cache + HIP-graph-safe Flash-Attention for llama.cpp, fully measured on real hardware.

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

GoodQ02/goodq4all: Local-first multimodal epistemic memory for scene-level video, audio, and text intelligence.

 🔢Embeddings  Content type: Code
github.com··Hacker News

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

 🧠LLM Inference  Content type: Code
github.com··Hacker News, r/LLM

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