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The author shares their experience running Qwen FN on a Strix box, comparing it to the 27B model. They found the performance impressive but noted that both models offer similar capabilities, leading to a sense of saturation in their personal use cases.
M5 ultra AI test results show prompt processing speeds up to 4-4.5 times faster and token generation 1.5x faster than M3 ultra, but with doubled power consumption, increased fan noise, and higher temperatures.
Benchmark results for Apple's M5 Ultra and M6 chips show significant GPU performance gains, with the M5 Ultra achieving up to 59% higher Metal scores on Geekbench compared to the M3 Ultra, and the M6 chip scoring 34% higher than the M5.
smolbenchmark is a new resource that benchmarks small AI models on consumer hardware devices, providing metrics like speed, efficiency, and thermals to help users choose the best model for their specific hardware.
The user discusses preferences between Qwen 3.8 27b and Qwen Flash Next models on Apple hardware, comparing speeds, and inquires about improving performance with MLX and harnesses without reasoning.
The article provides real-world performance benchmarks for running the Qwen3.8 27B AI model locally on a Mac Studio, comparing it to its predecessor and discussing hardware requirements and quantization effects.
The Lucebox engine now supports running the Qwen3.8-27B model on a single AMD Radeon AI PRO R9700 GPU, achieving up to 227 tok/s on code tasks using the DFlash2 drafter with lossless verification.
A heavily quantized Qwen3.8-27B model running on a 16 GB Quadro GPU successfully implemented a correct multilayer transfer-matrix method from scratch, despite extensive reasoning and debugging over 100 minutes.
A user highlights Buun's work on optimizing AI models, achieving high-speed inference of Qwen 3.6 on a single 3090 GPU and developing DFlash2 for Qwen 3.8.
Unitree previews its new humanoid robot 'Superman' that can perform a 2m standing jump and run at 12.66 m/s, surpassing human records.