A Mac Studio Ultra with 512GB of unified RAM is one of the most capable consumer machines for local LLM inference on the planet. One r/LocalLLaMA user has one — and admits they're barely scratching the surface. The post is drawing attention because it cuts to an honest question the community rarely asks out loud: at what point does more memory stop mattering?
What's the situation
The user's workload is legitimately demanding — inference pipeline testing, embedding workflows, large-context research tasks, and AI tooling development — but tops out well below what 512GB makes possible. They're running standard stacks: Ollama, MLX, and Python-based inference tooling. Nothing approaching production training. The machine's ceiling is nowhere in sight from where they're standing.
Why it matters
This thread is a useful reality check for the local LLM hardware arms race. Unified memory on Apple Silicon is genuinely fast, and 512GB does unlock models in the 200B–400B parameter range at reasonable quantizations — territory no consumer GPU setup can touch. But the user's question exposes a real gap: memory capacity and memory bandwidth are not the same bottleneck, and for most inference workloads below 70B, compute throughput is the constraint that actually hurts. The M2 Ultra's GPU cores, not its RAM ceiling, become the wall first.
What to watch
The workflows where this hardware earns its keep are specific: multi-model parallel inference, very long context windows (think 128K+ tokens across large models), and speculative decoding setups that keep multiple models resident simultaneously. If those aren't your use cases, the community consensus is clear — 128GB or 192GB configs handle the vast majority of local LLM workloads without leaving meaningful performance on the table. The 512GB tier is real hardware for real edge cases; it's just a narrow slice of what most practitioners actually need.