[D] MXFP8 GEMM: Up to 99% of cuBLAS performance using CUDA + PTX

<!-- SC_OFF --><div class="md"><p>The modern ML (LLM) compiler stack is brutal. TVM is 500K+ lines of C++. PyTorch piles Dynamo, Inductor, and Triton on top of each other. I built

<!-- SC_OFF --><div class="md"><p>After burning way too much money on GPT-4 API calls, I finally went all-in on local models with Ollama. The progress in 2026 is insane

<!-- SC_OFF --><div class="md"><p>That foodtruck bench post showing deepseek v4 matching gpt-5.2 at 17x cheaper got me thinking. if frontier cloud models are that overpriced for

<!-- SC_OFF --><div class="md"><p>New preprint. A Mixture-of-Experts inference kernel (TritonMoE) written entirely in OpenAI Triton, targeting portability across NVIDIA and AMD

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1x5skta/cuda_hip_gdn_chunked_kernel_by_am17an_pull/"> <img alt="cuda, hip: GDN chunked kernel by am17an ·

<!-- SC_OFF --><div class="md"><pre><code>**Evo-2 7B on consumer GPUs: Is it actually viable?** I put together a detailed analysis of hardware requirements for running Arc

<!-- SC_OFF --><div class="md"><p><a

<!-- SC_OFF --><div class="md"><p>Hey everyone, I've been working on getting Multi-Token Prediction (MTP) working with quantized GGUFs for Qwen3-27B and the results are pretty