Of course! This is a fantastic and well-researched plan for a local LLM beast. You're asking the right questions to avoid the common pitfalls. Here's a breakdown of my advice, framed as a Reddit-style reply. Re: Planning Multi-RTX 5060 Ti Local LLM Workstation (TRX40 / 32–64GB VRAM)

Hey, great plan! You've clearly done your homework. Targeting the TRX40 platform for its PCIe lanes is 100% the correct move for a multi-GPU inference box. You're avoiding the biggest bottleneck that plagues people trying to do this on consumer platforms. Let's dive into your questions. TRX40 Quirks & Platform Issues

BIOS / PCIe Lanes: This is TRX40's superpower. The Threadripper 3960X has 88 PCIe 4.0 lanes. Your motherboard's manual will show the slot configuration, but on a board like the MSI TRX40 PRO 10G, you can typically run:

• 2 GPUs: Both at a full x16.

• 3 GPUs: x16 / x16 / x8 (or similar).

• 4 GPUs: x16 / x8 / x16 / x8 is a common split.

• The good news: For LLM inference, even PCIe 4.0 x8 has more than enough bandwidth. You will see zero performance penalty compared to x16. The model weights are loaded into VRAM and stay there; the data transfer between GPUs (for tensor parallelism) is relatively minimal once the initial load is done. The main bottleneck is VRAM capacity and GPU compute, not inter-GPU bus speed. Always update to the latest stable BIOS. This is non-negotiable for stability.

RAM Stability: CL22 is a bit on the slow/latent side, but for your use case, it's completely fine. LLM inference is not nearly as memory-sensitive as gaming or some professional applications. Capacity is king. The quad-channel architecture of Threadripper is a bonus and will be very stable with a 4-DIMM kit. Just make sure the kit is on your motherboard's QVL (Qualified Vendor List) if you can, but it's less critical for DDR4 at this point.

Multi-GPU Surprises (The Big One):

• Software & Framework Support: Your biggest "headache" won't be the hardware, it will be the software. You need to understand the difference between tensor parallelism (splitting a single model across GPUs) and model parallelism (running separate models on separate GPUs).

• • For tensor parallelism (running one 30B model across 2+ GPUs), frameworks like vLLM and Text Generation Inference are your best friends. They are designed for high-throughput inference and handle multi-GPU well. Ollama also makes it very simple with its -gpu flag.
• • You'll specify something like CUDA_VISIBLE_DEVICES=0,1,2,3 when launching your server to use all GPUs.

• The "Pooled VRAM" Illusion: Remember, you can't just "pool" VRAM into one big 64GB block like system RAM. A 30B model (in 4-bit quantized, ~20GB) will be split across the GPUs. If you use 2 GPUs, ~10GB will be on GPU0 and ~10GB on GPU1. This is why tensor parallelism works so well.

Case / Cooling

This is arguably the most critical physical aspect of your build.

Open vs. Closed Cases:

• Open Frame (like a Core P3/P5/P8, Xproto): Best for thermals, hands down. GPUs dump heat directly into the room. Fantastic for access and looks. Downsides: Dust, noise, pets/kids, and no directed airflow. The top GPU will still be hotter than the bottom one, but the delta will be smaller.

• Closed Case (with High Airflow): This is the more practical and common choice. You need a full-tower case.

• • Top Recommendations: Fractal Design Meshify 2 XL, Lian-Li V3000 Plus, Phanteks Enthoo Pro 2, Corsair 7000D Airflow.

• The Real Secret: It's all about slot spacing. You MUST check the motherboard manual and the case's PCIe slot spacing. You want at least one slot of space between your dual-slot GPUs. A triple-slot gap is even better. This allows the fans of each GPU to breathe. Blower-style coolers are better for tight multi-GPU, but the RTX 5060 Ti will almost certainly use axial fans (the standard open-air design).

Cooling Strategy:

• Get a case with a mesh front panel.

• Install multiple high-quality, high-static-pressure fans as intake at the front.

• Use the AIO for the CPU as top exhaust.

• Have a rear exhaust fan.

• If you have a 4th GPU sitting right against the PSU shroud, consider a fan mount on the shroud to push air into that GPU.

Power Supply / Spikes

Wattage Calculation: Let's do some napkin math.

• Threadripper 3960X: 280W (max)

• RTX 5060 Ti (estimated TBP): ~190W each (this is a guess based on current trends).

• 4x GPUs: 4 * 190W = 760W

• Motherboard, RAM, SSDs, Fans: ~100W

• Total (Max Theoretical): ~1140W

A 1200W Platinum PSU is the absolute bare minimum. You are cutting it too close. The PSU will be running near its limit, which is inefficient, hot, and loud. More importantly, you have no headroom for transient power spikes (microsecond-long surges that can be 2x the TBP).

Recommendation: Go for a 1600W PSU. It might feel like overkill, but it gives you:

• Ample headroom for transient spikes.

• Quiet operation, as the fan won't need to spin up aggressively.

• Future-proofing for potentially more powerful GPUs or adding more components.

An ATX 3.1 / PCIe 5.1 PSU is a great choice as they are specifically designed to handle these modern GPU power transients.

Final Verdict & Pitfalls to Avoid

Your core concept is solid. Here are the key adjustments and final tips:

Upgrade the PSU: This is your most critical change. Get a 1600W unit. Don't cheap out here; it's the heart of your system.

Case is King: Don't underestimate the importance of a full-tower, high-airflow case with good PCIe slot spacing. Your GPUs will thank you with higher boost clocks and longer lifespans.

Thermal Monitoring: Use a tool like nvtop (Linux) or HWInfo64 (Windows) to monitor your GPU memory junction temperatures (mem_junction_temp) under sustained LLM load. This is often hotter than the core temp.

Start with Linux: For a pure LLM machine, you will have a much better experience on Ubuntu 22.04/24.04 LTS. Driver installation is simpler, there's less background overhead, and the vast majority of inference servers are developed and tested on Linux. The NVIDIA driver is easy to install.

Don't Fear the PCIe x8: As stated, you will not notice a difference for inference. Don't waste mental energy trying to get all 4 slots at x16; it's impossible and unnecessary.

You are on the right track to building an absolute monster of a local AI workstation. Good luck with the build, and post some pics when it's done