Let’s explore new Apple’s deep learning framework, run modern LLM on a laptop, and find an intriguing hint for performance of PyTorch MPS
Exploring LLMs with MLX and the Neural Accelerators in the M5 GPU
Mac with Apple silicon is increasingly popular among AI developers and researchers interested in using their Mac to experiment with the…
LLMs Can Now Write GPU Kernels That Beat torch.compile - Break AI Scaling Limits in 7 Days
We're now seeing multi-agent systems that take your PyTorch code and produce CUDA or Triton kernels with 2x to 14x speedups over torch.compile(mode='max-autotune-no-cudagraphs'). Not on toy benchmarks. On real models like Llama-3.1-8B, Whisper, and Stable Diffusion. Learn proven techniques to shift the scaling law intercept and achieve 10-50% performance gains.
Locally AI - Run AI models locally on your iPhone, iPad, and Mac.
Run Llama, Gemma, Qwen, DeepSeek, and more on your iPhone, iPad, and Mac. Optimized for Apple Silicon. Offline. Private.
raullenchai/Rapid-MLX
The fastest local AI engine for Apple Silicon. 4.2x faster than Ollama, 0.08s cached TTFT, 100% tool calling. 17 tool parsers, prompt cache, reasoning separation, cloud routing. Drop-in OpenAI replacement. Works with Claude Code, Cursor, Aider.
raullenchai/Rapid-MLX
The fastest local AI engine for Apple Silicon. 4.2x faster than Ollama, 0.08s cached TTFT, 100% tool calling. 17 tool parsers, prompt cache, reasoning separation, cloud routing. Drop-in OpenAI replacement. Works with Claude Code, Cursor, Aider.
LLM Leaderboard - Comparison of over 100 AI models from OpenAI, Google, DeepSeek & others
Comparison and ranking the performance of over 100 AI models (LLMs) across key metrics including intelligence, price, performance and speed (output speed - tokens per second & latency - TTFT), context window & others.
google-ai-edge/LiteRT
LiteRT, successor to TensorFlow Lite. is Google's On-device framework for high-performance ML & GenAI deployment on edge platforms, via efficient conversion, runtime, and optimization
Alex Cheema on Twitter / X
A new approach to efficient large scale distributed training on Apple Silicon.Most AI research today is focused on traditional GPUs. These GPUs have a LOT of FLOPS but not much memory. They have a low memory:flops ratio. Apple Silicon has a lot more memory available for the GPU… https://t.co/FsNFnsvphJ pic.twitter.com/LFE1gtAm33— Alex Cheema (@alexocheema) July 11, 2025
Alex Cheema on Twitter / X
This is why we need open benchmarks for local AI.Otherwise it turns into tribalism and name calling.We will be publishing the largest database of open benchmarks for local AI, tested on 1,000+ real hardware setups. Every device, every interconnect, different… https://t.co/ZsU3PCdSsZ— Alex Cheema (@alexocheema) March 9, 2026
Alex Cheema on Twitter / X
It’s kind of crazy but the shitstorm of supply chain issues has created a new best-in-class local AI deployment: M5 Max MacBook clusters.- The memory unit economics are great - each MacBook has 128GB @ 614GB/s for $5k- M5 Max added tensor cores (Apple Neural Accelerators) with… https://t.co/f8STQ0tLZs pic.twitter.com/FLm3oOnyEl— Alex Cheema (@alexocheema) May 14, 2026
Introducing LFM2: The Fastest On-Device Foundation Models on the Market | Liquid AI
Today, we release LFM2, a new class of Liquid Foundation Models (LFMs) that sets a new standard in quality, speed, and memory efficiency for on-device deployment. Built on a hybrid architecture, LFM2 delivers 200% faster decode and prefill performance than Qwen3 and Gemma 3 on CPU. It also significantly outperforms models in each size class on instruction-following and function calling—the core capabilities that make LLMs reliable for building AI agents.
XiongjieDai/GPU-Benchmarks-on-LLM-Inference
Multiple NVIDIA GPUs or Apple Silicon for Large Language Model Inference?
Announcing Gemma 3n Preview: Powerful, Efficient, Mobile-First AI
MacPaw Partners with Liquid AI to Bring On-Device AI to Millions of Mac Users — Blog
MacPaw partners with Liquid AI to bring private, fast, on-device AI to millions of Mac users, starting with the Eney assistant for macOS.
Jan-Nano : 1st Deep Research LLM
Import AI 470: No rights for machines; automating environment generation with SPADE; and building better GPU kernels with Hawkeye
Differential acceleration of cyber, math, and AI
