The hardware bet is wrong. It is running from a war (twitter.com)

Recent discussions within the AI quantization community reveal a significant shift in focus from hardware investments to innovative software solutions. The Unsloth AI team shared insights from their benchmarks, highlighting an overwhelming influx of low-effort claims and suggesting that true advancements lie in mathematical approaches rather than merely acquiring better hardware. They likened the current phase to the "final stage of the Mandelbrot set," where each mathematical enhancement uncovers further possibilities, demonstrating that the AI field is ripe for exploration and experimentation with quantization methods. In experimenting with their own kernel, the Unsloth team achieved remarkable results, processing up to 1,403 tokens per second on an RTX 5090 laptop GPU. This contrasts sharply with traditional models like llama.cpp, which operates through hundreds of smaller GPU programs, leading to inefficiencies. The team proposed a mindset shift: encouraging AI systems to embrace mistakes as pathways to breakthroughs, thus fostering bold exploration rather than cautious execution. As the landscape evolves, the emphasis is now on developing unique mathematical kernels that can outperform existing hardware-dependent solutions, positioning software innovation as the key differentiator in AI development, and challenging traditional notions of advantage based solely on hardware capabilities.