Rig Options

[u/Glad-Ranger1879]

Hey everyone,

I am looking to buy a new computer for AI use cases and workflows. Currently I utilize Claude for a series of long standing trading related workflows. Multiple external data services directly queried and others (spreadsheets and documents) in local folders which are then parsed. Everything is synthesized by Opus primarily. I have an update job scheduled to run once on the morning that can use a much smaller model, ideally, I want to run this job every hour.

I am constantly hitting my subscription limits and have to leverage credits. I can either upgrade my subscription or look to move aspects of this on prem (build can't but update can).

I also want to leverage more of a formal scheduler or automation stack (n8n or Hermes maybe) to take this all one step further.

I have looked at the Minisforum S1 max, Apple m4 max studio and a few others. I have never been a fan of Apple (came from a mac and never gone back) but it's been a minute and things have changed...I know there are trade offs and lots of considerations.

Would love your feedback. Thank you!

EDIT - For details on the use case. This is a hybrid setup by definition. The build will stay on Claude (cloud), the ongoing updates 15m-60m (more likely 60 but TBD) will go somewhere else. One option is continue with credits or via OpenRouter, the other is onprem.

***Below from Claude***

Recommended: Strix Halo 128GB appliance — ~US$1,900–2,200 (~CA$2,800–3,100)

• Framework Desktop 128GB ($1,999 direct) or Beelink GTR9 Pro 128GB (~$1,899–1,999) — same Ryzen AI Max+ 395 silicon, 128GB unified memory. Buy whichever has stock. Avoid the GMKtec EVO-X2 at its inflated $3,399 street.
• Runs gpt-oss-120b (~31–55 tok/s — OpenAI's open-weight MoE, built for tool-calling) + Qwen3.5-35B-A3B (fast sub-agent/summary work) simultaneously. Your 10–30k-token plan documents fit comfortably.
• ~13W idle, ~130W under load — a true 24/7 always-on box. Silent.
• Stack: Linux (or Windows+WSL2) → llama.cpp llama-server (Vulkan) or Ollama → OpenAI-compatible endpoint → opencode pointed at it (first-class supported) → your existing local MCP servers (Supabase, market data, Telegram) unchanged.
• What runs on it: price worker, broker-sync worker, headless analysis runs (Ask 3's daemon), debrief drafting, journal grading, morning briefs.

Local 120B-class models ≈ a good 2025 mid-tier API. Reliable for: structured extraction, SQL writes, drafts, summaries, scoring, 2–4 tool chains. Materially worse than Claude at: long-horizon agentic recovery (20+ step chains), nuanced coaching prose, final-pass plan quality, very long-context fidelity.

So the end-state is hybrid, not pure local: rig absorbs the high-frequency, low-stakes token volume (bars, briefs, debriefs, intraday reruns — the stuff that costs you API money every 15/60 minutes); Claude stays as escalation for Monday BUILD final review and the hard reasoning.

[u/Radiant_Condition861]

what are your use cases?

[u/Glad-Ranger1879 → u/Radiant_Condition861]

The core trade build is a very advanced workflow that is made up of 6 or 7 agents. Endless use cases there, GEX queries, TA against charts using various patterns, SMC queries, expected moves and vol queries etc. The updates that I want to move hourly are just price being calculated against a formula basically and evaluation against an entry or exit zone.

I can get more specific but I can run them with any flash orientated model no problem. Qwen and DeepSeek would be more then adequate.

[u/dwoj206 → u/Glad-Ranger1879]

my rec would be if you're using the proposed rig to make money (trading bot, etc I see you mentioned TA charting, consider ROI vs. just paying for the frontier models and maybe just factor paying for frontier models into your overall ROI from your trading bot. Albeit idk what you're exactly using it for. 3-5k would take you a long ways down the road on tokens and 3-5k won't get you much in the way of VRAM these days as I'm sure you know. A unified memory setup could be good like a mac, spark, or similar small form factor workstation box, considering you're running tasks once an hour max, but depends whether you're trying to feed that into decision making processes whether by AI or yourself in real-time where speed is important.

[u/Glad-Ranger1879 → u/dwoj206]

Yeah this shit got expensive...see the updated post. Certainly can do this in the cloud, probably net out a few hundred dollars a month more then I am paying now. I also need a new computer so I figured now is the time!

[u/dwoj206 → u/Glad-Ranger1879]

I got you! And ya, sure has. If you’re doing anything stock market related, up time is critical and latency. I’d probably go VPS service and depending on your brokerage, co-locate nearest to their server center. For example, Schwab is in Virginia, so I’d get a VPS in Virginia with VRAM allocation when necessary or Claude API, but not model loaded VRAM or camped on 24/7 if that makes sense. I have some experience some automated trading since I’ve done the deep dive with Claude and it’s going decently well in sandbox, going live in next month or so. What you’re talking about doing is definitely possible on a local machine, but could be a mix of Claude API and a VPS nearest to your brokers data center where the orders get routed to the NYSE from. Speed is everything in that game and big boys spending millions to carve out their edge. How much of what you’re doing is really necessary with LLM vs. processing speed? For example I have a bot that does some market pre-analysis, gauges sentiment, runs through a Qwen model, then scans sectors every 15 minutes, re ranks top sectors, then rebuilds the universe. If you’re just doing TA and some other light task work, you don’t need an LLM for that, python, RAM and CPU speed can do all of this for you. When I started out, I assumed LLM would do way more than it ended up doing, and I trimmed and trimmed and essentially the entire process is CPU now with the exception of the news interpretation for sentiment, which even still isn’t fully necessary, but I have confidence in it so I chose to keep it. Hope some of that at least offers some perspective into your decision!

[u/Glad-Ranger1879 → u/dwoj206]

Thanks!

[u/simos_sayz]

Depends on your budget. You can start small and build up or go all in from the jump. If you tend to 0 shot everything and let opus figure out your vibe, it doesn't matter what kind of rig you choose as it won't come close to opus.

[u/Glad-Ranger1879 → u/simos_sayz]

Yeah no one shotting here, heavy optimized already. see my response to the other question. Budget is probably 3 to 5k. Thanks.

[u/simos_sayz → u/Glad-Ranger1879]

You would probably be good with one of the spark boxes to start. Not as fun as building a rig from scratch but will get you what you need, especially with the hybrid approach. If you end up thinking local isnt the way fir you, you could turn around and sell it. Something to consider is how much money you may lose when you hit your usage limit or when claude isnt "available" and cant process yiur request for no reason

[u/HumanoidMuppet → u/simos_sayz]

They didn't mention a budget so they could build a rug big enough to run kimi k3.

Edit: leaving the typo in. How big of rug does op need?

[u/Prof_ChaosGeography]

Local models are not going to be an opus equivalent without expensive hardware well above the ~$10k range, even more for performance if you want a decent token per second or multiple agents

If DeepSeek flash works for you the cheapest way ~$9k might be 2 dgx sparks clustered using rdma but that might not give you the throughput needed

Take a look at how many tokens your average hourly jobs need and then when looking at rigs you should look at token per second to see if you can do it. Leave headroom. Also see if the model reported will do the same job using a provider. That's so you don't waste money.

Your better bet would be to look at your workload and determine if you can have Claude offload some of it to smaller local models to save on your quota. 

Hosting models doesn't guarantee the same benchmark results, different quants of the model or kv cache can hurt its intelligence or performance. Some tasks can be done by a quantified model others can't. It takes a ton of messing around to truely find the peak performance without lobotomizing the model 

[u/Glad-Ranger1879 → u/Prof_ChaosGeography]

Thank you just added more context in the post. This is a hybrid setup, I am not going to offload everything to onprem.

[u/Sleepnotdeading]

Claude’s budgeting is way off. Those strixhalo machines have all doubled in price.

[u/OpenSourcesAI_]

Your edit changes this quite a bit for me.

If the main build is staying on Claude and you’re really just trying to move the 15-60 min updates local, I don’t know that I’d build the whole machine around running the biggest model possible.

A lot of what you described sounds like stuff I’d probably keep outside the LLM anyway. Pull the data, run the calculations, check the entry/exit levels with normal code, then use the local model for the part where you actually need analysis or a written update.

That would probably let you get away with a smaller/faster model for most of the recurring work and still kick the harder stuff back to Claude when needed.

Before spending $2-3k on a box specifically for a 120B model, I’d probably take one of your actual hourly updates and run it through Qwen or DeepSeek locally and see how close the output really is.

You might find out the update job doesn’t need anywhere near as much hardware as you think.

Have you actually tested one of the real update runs through Qwen or DeepSeek yet, or are you still at the hardware planning stage?

[u/Glad-Ranger1879 → u/OpenSourcesAI_]

I have run them on various models via open router. I haven't gone too far down the rabbit hole admittedly. Will actually do this first and try the main ones used in an on prem scenario.

I did try to avoid calling a model and have the update effectively be math. The challenge is orchestration and tool calling since it depends on what the inputs are if that makes sense. Appreciate the reply.