GEMM Optimization

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Scoured 51 posts in 6.7 ms

DiffusionGemma: 4x Faster Text Generation

 🧠Inference Engineering  Content type: News  Content type: Blog

Two Leaps to 1000 Tokens/s on a 1T-Parameter Model: On Inference Systems, Execution Boundaries, and Co-Design

 🧠Inference Engineering  Content type: Blog
tilert.ai··Hacker News

Beyond FLOPs: Benchmarking Real Inference Acceleration of LLM Pruning under a GEMM-Centric Taxonomy

 💰Inference Cost  Content type: Academic
arxiv.org·

MLPerf and the rise of latency-aware LLM benchmarking

 ⏱️Prefill Decoding
edn.com·

DiffusionGemma: The Developer Guide

 🧠Inference Engineering  Content type: Blog
developers.googleblog.com·

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.

 ⏱️Prefill Decoding  Content type: Code
github.com··Hacker News

The Inference Alpha: Maximizing Frontier Models on AMD

 🧠Inference Engineering  Content type: Blog
digitalocean.com·

AutoMegaKernel: A Statically-Checked Agent Harness for Self-Retargeting Megakernel Synthesis

 🎮GPU Computing  Content type: Academic
arxiv.org··Hacker News

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

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

Discrete Diffusion Modelling by Estimating the Ratios of the Data Distribution

 🚀Speculative Decoding  Content type: News  Content type: Blog

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

 🎮GPU Computing  Content type: Academic
arxiv.org·

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

What Arm-based innovations happened in May 2026?

 🧠Inference Engineering  Content type: Blog
newsroom.arm.com·

K-Forcing: Joint Next-K-Token Decoding via Push-Forward Language Modeling

 🚀Speculative Decoding  Content type: Academic
arxiv.org·

Apples to Apples: MLX vs. Llama.cpp for Gemma 4 12B on an M1 16GB

 💰Inference Cost  Content type: Blog
ziraph.com··Hacker News

PALUTE: Processing-In-Memory Acceleration via Lookup Table for Edge LLM Inference

 💰Inference Cost  Content type: Academic
arxiv.org·

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

 🔢FP8 Training

Benchmarking dots.tts on Strix Halo

 🎮GPU Computing
sleepingrobots.com·

From Database and Virtualized Workloads to Backup: Dell PowerEdge R4715 and R5715 for SMB Realities

 🕸️Network Fabrics
storagereview.com·

DeepSeekV4 1.6T Day 0 to Day 43 Performance Over Time - Huawei, GB300 NVL72, MI355X, B200

 🧠Inference Engineering  Content type: News

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