MacBook Neo Local AI Test – LLM Benchmarks & MLX Performance! скачать в хорошем качестве

MacBook Neo Local AI Test – LLM Benchmarks & MLX Performance!

Timestamps: 00:00 - Intro 01:47 - Qwen3.5 4B Test 05:24 - LiquidAI LFM1.2 Test 07:00 - Qwen3.5 9B Test 08:03 - Qwen3.5 0.8B Test 09:52 - Llama3 8B Test 11:35 - Claude Code System Optimization 12:57 - Vibe Optimizing With Claude 14:17 - Letting Claude Run Benchmarks 15:21 - MLX Llama3 8B Speed Testing 18:15 - Speculative Decoding Test 19:05 - MLX Llama3 8B Benchmark Results 20:55 - Claude Benchmark Script Demo 22:37 - Closing Thoughts AI Integration & Consulting: https://bijanbowen.com/ Join the Discord:   / discord   In this video, we test how well the MacBook Neo performs for local AI workloads. Several models are benchmarked, including Qwen3.5 (0.8B, 4B, and 9B), LiquidAI LFM1.2, and Llama 3 8B, with a focus on real inference performance. We also experiment with Claude Code to optimize the system and automatically generate benchmark scripts. The tests include MLX-based inference speed measurements and speculative decoding experiments to see how much performance can be improved through optimization.