3 months ago, I realized I was hopelessly dependent on corporations that only care about power, money, and control. At this point Cursor, Claude, OpenAI, all had rugged their unlimited plans. I wanted a Mac M3 Ultra with 512GB RAM. Ahmad and Pewdiepie convinced me otherwise. Here's what I learned building my own AI Rig ----------------------------- The Build ($3K-$10K) This is the top performance you can get below 10k USD • 4x RTX 3090s with 2x NVLink • Epyc CPU with 128 PCIe lanes • 256-512GB DDR4 RAM • Romed8-2T motherboard • Custom rack + fan cooling • AX1600i PSU + quality risers Cost: $5K in US, $8K in EU (thanks VAT) Performance Reality Check More 3090s = larger models, but diminishing returns kick in fast. Next step: 8-12 GPUs for AWQ 4-bit or BF16 Mix GLM 4.5-4.6 But at this point, you've hit consumer hardware limits. ---------------------------------------- Models that work: S-Tier Models (The Golden Standard) • GLM-4.5-Air: Matches Sonnet 4.0, codes flawlessly got this up to a steady 50 tps and 4k/s prefill with vLLM • Hermes-70B: Tells you anything without jailbreaking A-Tier Workhorses • Qwen line • Mistral line • GPT-OSS B-Tier Options • Gemma line • Llama line ------------------------------------ The Software Stack That Actually Works For coding/agents: • Claude Code + Router (GLM-4.5-Air runs perfectly) • Roocode Orchestrator: Define modes (coding, security, reviewer, researcher) The orchestrator manages scope, spins up local LLMs with fragmented context, then synthesizes results. You can use GPT-5 or Opus/GLM-4.6 as orchestrator, and local models as everything else! Scaffolding Options (Ranked) 1. vLLM: Peak performance + usability, blazing fast if model fits 2. exllamav3: Much faster, all quant sizes, but poor scaffolding 3. llama.cpp: Easy start, good initial speeds, degrades over context UI Recommendations • lmstudio: Locked to llama.cpp but great UX • 3 Sparks: Apple app for local LLMs • JanAI: Fine but feature-limited ------------------------------- Bottom Line Mac Ultra M3 gets you 60-80% performance with MLX access. But if you want the absolute best you need Nvidia. This journey taught me: real independence comes from understanding and building your own tools. If you're interested in benchmarks I've posted a lot on my profile