My M5 Max, Gemma 4, MLX LOCAL Stack. (This KILLS MODEL PROVIDERS)

Model providers DON'T want you to see this video. The M5 Max just exposed the dirty secret of the cloud LLM economy: you're renting what you could already OWN. 🔥 While Anthropic and OpenAI APIs go down AGAIN mid-recording, my local stack keeps shipping. Private. Cheap. Fast. On-device. This is the beginning of the end for the API rental racket. 🎥 FEATURED LINKS: • MLX, Gemma4, Qwen3.6, Pi agent live-bench codebase: https://github.com/disler/live-bench • Tactical Agentic Coding: https://agenticengineer.com/tactical-agentic-coding?y=00Y-p62sk0s 📚 RESOURCES • Nvidia NVFP4: https://developer.nvidia.com/blog/introducing-nvfp4-for-efficient-and-accurate-low-precision-inference/ • Apple M5 GPU Neural Accelerators: https://machinelearning.apple.com/research/exploring-llms-mlx-m5 • mlx-vlm: https://github.com/ml-explore/mlx-lm • Ollama Gemma4 Model: https://ollama.com/library/gemma4 • Ollama MLX Blog: https://ollama.com/blog/mlx • Pi coding agent: http://pi.dev • Gemma4 26 nvfp4: https://huggingface.co/mlx-community/gemma-4-26b-a4b-it-nvfp4 • Vitalik Eth Secure LLMs: https://vitalik.eth.limo/general/2026/04/02/secure_llms.html ⚡ Here's the uncomfortable truth most engineers are ignoring: you're paying a premium for cloud inference when your M5 Max, M4 Max, or even Apple Silicon you already own can run state-of-the-art local LLMs RIGHT NOW. Gemma 4, Qwen 3.5, MLX variants optimized for Apple AI hardware are quietly eating the model providers' lunch. 🧠 In this head-to-head benchmark, I pit the M5 Max vs the M4 Max across three brutal local inference tests: raw prompt throughput, context scaling with Graph Walks, and full agentic coding workflows via the Pi coding agent. The results are going to reshape how you think about local agents. 💣 THE CONTROVERSIAL FINDING: If you're running GGUF models on Apple Silicon in 2026, you're leaving 2x performance on the table. MLX smokes GGUF. Not by a little. By a LOT. 118 tokens per second vs 60. Almost double the pre-fill speed. This is the Mac LLM benchmark result the Ollama crowd doesn't talk about enough. 🛠️ What you'll see inside: • M5 Max vs M4 Max: 15-50% wall clock gains on real workloads • GGUF vs MLX: why MLX + NVFP4 is the ONLY way on Apple Silicon • Gemma 4 vs Qwen 3.5: Google actually cooked here, and I'll show you where each model wins • The context window cliff: why local inference falls off HARD past 16K tokens • Pi coding agent benchmarks: can local agents actually do agentic coding? (Yes. With caveats.) • The micro-agent thesis: where local language models WIN against the cloud right now 🚨 The cloud vs local war isn't coming. It's HERE. Every time Claude goes down, every time your API bill hits four figures, every time a model provider changes pricing or deprecates the model you built your product on, you're getting reminded who actually owns your stack. Spoiler: it's not you. 🍎 Apple AI is in a stealth lead nobody is talking about. MLX is the secret weapon. When the M5 Ultra or M6 Mac mini lands with 500GB of unified RAM, the API-as-a-service model is going to get obliterated for a massive slice of workloads. You need to be ready. 🏗️ This is why I'm pouring time into local agents, micro-agents, and sub-agent processes running on device. Engineering work, personal work, product work - there's a task tier that local models already handle better than cloud when you factor in privacy, speed, and zero API dependency. If you don't know what local AI can do TODAY, you won't recognize the tipping point when it hits. 💡 The big idea: model providers want you hooked on their Kool-Aid. The counter-move is knowing exactly what your M4 Max or M5 Max can run locally, right now, with zero outside API. Prepare, benchmark, vibe check. When the local-cloud cost line crosses, the engineers who were ready are going to compound savings and control at an absurd rate. Stay focused and keep building. 📖 Chapters 00:00 M5 Max Mac Book Pro Unboxing 02:22 Gemma and Qwen Cold Prompt 03:17 Gemma and Qwen Warm Prompt 04:57 Benchmark 1 - M5 destroys M4 14:20 Benchmark 2 - Local Model Bottleneck 27:07 Benchmark 3 - Pi Coding Agent 34:55 Local Benchmark, MLX, Gemma, and M5 Takeaways #localllm #mlx #aicoding