🔲 ML Hardware - nickyfoto · Scour

Choosing the Right AI Abstraction Layer for modern AI systems 🏗️System Design

digitalocean.com·1d

Qwen3.6 27B FP8 runs with 200k tokens of BF16 KV cache at 80 TPS on a single RTX 5000 PRO 48GB 🧠LLMs

huggingface.co·1d·r/LocalLLaMA

Best AI Coding Models for Consumer Hardware (5 You Can Run Locally) 🧠LLMs

firethering.com·4d·Hacker News

Ask HN: Best hardware for local inference 🤖LLM

news.ycombinator.com·3d·Hacker News

Ling-2.6-1T Brings Fast Thinking to Trillion-Parameter AI 🤖AI Research

hackernoon.com·1d

Foundation42/valkyr: Cross-vendor LLM inference based on TRiP using Vulkan compute. Zig + TurboQuant - no CUDA lock-in. 🧠LLMs

github.com·3d·Hacker News

GPU Utilization Is a Counter, Not a Cause ⚡Performance Engineering

dev.to·2d·DEV

Benchmarking Single-Pose Docking, Consensus Rescoring, and Supervised ML on the LIT-PCBA Library: A Critical Evaluation of DiffDock, AutoDock-GPU, GNINA, and DiffDock-NMDN 🧠LLMs

What computer should you buy to run AI models locally? 🥧Raspberry Pi

fedecarg.substack.com·5d·Substack

AI Value Capture 🤖AI Research

newsletter.semianalysis.com

·5d·Hacker News

From OOM to 262K Context: Running Qwen3-Coder 30B Locally on 8GB VRAM ⚡Performance Engineering

dev.to·1d·DEV

Thoki-Buthelezi/elite-ai-systems-engineer-2026: 52-week journey to become an elite ai systems engineer 🤖AI Research

github.com·4d·DEV

VUDA: Breaking CUDA-Vulkan Isolation for Spatial Sharing of Compute and Graphics on the Same GPU ⚡Performance Engineering

VkSplat: High-Performance 3DGS Training in Vulkan Compute 🧠LLMs

TPUs vs. GPUs: What They Are, How They Differ, and Which Workloads Belong on Each 🏗️System Design

dev.to·5d·DEV

Submission: SP8192 + Partial RoPE (16/64) + GPTQ SDClip + SGD TTT — val_bpb 1.0820 (3-seed mean) by swapp1990 · Pull Request #1747 🐹Go

github.com·5d·DEV

SURGE: SuperBatch Unified Resource-efficient GPU Encoding for Heterogeneous Partitioned Data 🏗️System Design

jegly/Box: Private on-device AI suite for Android. Fork of Google AI Edge Gallery with llama.cpp, whisper.cpp, stable-diffusion.cpp, GGUF import, voice chat, vision AI, on-device image generation, biometric lock, encrypted history, and NPU/TPU/GPU acceleration. 🍎Apple

github.com·6d·Hacker News, r/SideProject

I trained my own LLM and published it on HuggingFace 🧠LLMs

dev.to·1d·DEV

Intelligent Elastic Feature Fading: Enabling Model Retrain-Free Feature Efficiency Rollouts at Scale 🤖AI Research

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