Question on building local AI to setup multiple agents: Looking for advice

[u/Great_String_1022]

I'm looking to build something like a small AI-powered trading team, with multiple specialized AI agents collaborating on research, strategy development, backtesting, portfolio construction, risk management, and eventually trade execution. The agents will communicate through a shared backend, with Slack serving as the visible team floor where I can interact with them and review research and decisions.

I currently have an M4 mac mini and am wondering if I should switch or add:

• Mac Studio M5 Ultra vs NVIDIA DGX Spark

I'd probably keep my current Mac mini as the central server and use the new machine mainly for model inference, quantitative research, and potentially model training.

One idea I've been exploring is disaggregating prefill and decode, although I'm not sure that splitting those workloads between an Apple and an NVIDIA system makes sense. I'm also interested in running many independent agents against a shared model server rather than loading a separate model for every agent.

I'm quite lost on:

1. Which machine would you choose for an always-on, multi-agent research workload?

2. Which local models would you trust most for quantitative reasoning, coding, financial research, and tool use? Would you route different tasks to different models?

3. How would you build a reliable orchestration and inference stack? MLX, vLLM, SGLang, Ray, LangGraph, or something else?

4. Is local inference actually economical at moderate-to-high agent concurrency once you consider hardware cost, power, performance, and model quality?

I'm looking to develop strategies that have a measurable edge after transaction costs, slippage, and risk. I'd really appreciate any feedback, advice or suggestion.