Is new Mac Studio M5 ultra good for trading

[PTScalper]

Hi traders,

lets discuss about your own hardware for trading.
I found out that most of my trades i do just from mobile phone. But on another hand i start to like more and more local AI models for deep diving into some new trading ideas and programming new indicators.

I found out that finaly new Mac Studio is out and it is mega powerhouse, how do you like idea of buying it for local LLm models?

[FTtrader]

Hi,
thanks for sharing.
I think it will be very interesting, especially once will be available 512GB version
Only question, which remains is how much it will cost.

[PTScalper]

I checked latest avaiable versions and i don’t see there 512GB shared RAM. Max right now 256GB, i hope that 512GB will come

[PTScalper]

But on another hand i found out in official News release on Apple website, that there will come 512GB version

And with 1.2TB/s its great monster )
I will try to find best local AI model for that.
What do you think about local AI for your own analysis?

Right now im using Gemma4 31B and 12B parameters, its not bad, but paid cloud APi of Gemini Pro is for example for programming much more better

[FTtrader]

Hi all,

yeah, IT will be monster power house.

With 512GB of unified memory and 1.2 TB/s of memory bandwidth, the M5 Ultra Mac Studio allows you to move models that typically require dedicated server racks directly to your desk.

Because the CPU and GPU share the same memory pool, your constraint is purely the total footprint of the model's weights and its context window (KV cache). Assuming a standard 4-bit quantization (Q4), here is what fits:

Llama 4 maverick (400B model), Deep seek R1 671B or Kimi K2.61T.

Honestly, if i will find out use case, when so of these big models would be usefull for trading, i will buy it.

But first of all i will test it that models vis paid API and will look for usefull Solutions.

[FTtrader]

And Yeah, once again, memory bandwith

The M5 Ultra's 1.2 TB/s bandwidth is the critical factor here—token generation is entirely bandwidth-bound. While earlier Mac Studio configs had high capacity, the M5 Ultra generates tokens about 50% faster than the M3 Ultra, making massive 400B models genuinely interactive rather than just technically loadable.

[FTtrader]

And put it into comparion with Nvidia 5090:

The Mac Studio Advantage: Capacity and Context
The Mac Studio's superpower is its unified memory pool. Because you have 512GB available, you can load a massive 400B parameter model in 4-bit quantization (~220GB) and still have nearly 300GB left over.
This leftover memory is critical for the KV cache (the memory used to remember the context of your conversation). If you are passing entire codebases, massive documents, or using agents that run autonomously for hours, the Mac Studio can handle hundreds of thousands of tokens of context without breaking a sweat. To get 512GB of VRAM in the PC world, you would need 16 RTX 5090s—which requires complex server-grade motherboards and massive power supplies.

[FTtrader]

The RTX 5090 Advantage:

Raw Speed For LLM inference, memory bandwidth dictates how fast tokens are generated.

The RTX 5090 features GDDR7 memory that pushes an insane 1,792 GB/s of bandwidth. 

If you are running models that fit comfortably within its 32GB limit—like Gemma 4 31B or Qwen 3.6 35B—the RTX 5090 will generate text nearly 50% faster than the Mac Studio. Furthermore, the 5090's Blackwell architecture supports advanced tensor precision natively, such as FP4. This makes the Nvidia card vastly superior if you plan to actually train or fine-tune models locally, rather than just run inference

[FTtrader]

Get the Mac Studio if: You want to run state-of-the-art frontier models (70B, 400B+), need massive context windows for RAG or coding agents, and want a quiet, low-power system on your desk.

Get the RTX 5090 if: You are happy running highly capable 30B-class models, need maximum tokens-per-second, or plan to do local fine-tuning and heavy image/video generation (like Flux or Stable Diffusion) alongside your LLMs.

[FTtrader]

And curently i test these models for trading:

Best for Live Autonomous Trading & Portfolio Management

If you are running models in live environments where they make allocation decisions based on market data, the top tier changes constantly based on live forward-testing.

MiniMax M3 & Nemotron 3 Ultra: As of August 2026, these two models are leading live autonomous AI trading benchmarks (such as TradeRank), consistently generating positive returns in automated US equities and crypto trading.

Gemini 3.7 Flash: A top performer for high-speed, live-data processing tasks, especially when integrated with real-time WebSockets from market data providers.