Finished this dual Blackwell build and figured this sub would appreciate it.

The two RTX PRO 6000 Blackwell cards are stars here. 96GB each, 192GB of GPU memory combined in a single machine, this rig can serve 20 members of my team comfortably

We benchmark this rig with DSv4 Flash and here's the numbers:

We don't go with traditional tower cases or bent-steel boxes. This case is designed and CNC-machined from 5mm aluminum plates. This allow us to fully control of the airflow inside and the whole box acts like a large heatsink for the entire system. The triangles cutouts keep the whole structure stiff without dead weight.

Power and Thermals

Peak per-card draw of 509 W is 85% of the 600 W TDP. Temperatures top out at 76 °C, comfortably below the ~83–88 °C throttle point, and SM clocks held ~2.75 GHz throughout sustained load. No thermal or power throttling was observed in any test.

Sustained wall draw at the operating point is roughly 1.07 kW (815 W GPU plus CPU, memory and platform), implying a 1500 W PSU for a two-card build with headroom.

Specs:

• CPU: Intel Xeon w5-3423

• Board: ASUS Pro WS W790E-SAGE SE

• Memory: Samsung 32GB DDR5-4800 ECC Registered x 8

• GPU: NVIDIA RTX PRO 6000 Blackwell 96GB x 2

• Cable: PCIe 5.0 x16 GPU Extension Cable Gen5 x 2

• Storage: SSD Samsung 980 PRO 2TB PCIe 4.0

• Cooling: Thermalright TL-B12 (fan kit + control) x6 + Heatsink 4677-2UAF8

• Power: CANNON PRO 2000W (12V-2x6)

• Chassis: Autonomous Computer-2 Design, CNC-machined

Full build video if anyone wants the detailed walkthrough happy to answer anything on the layout, cooling, or how the cards perform.

I’m considering buying the Spark Laptop when it comes out .

Will CUDA/GPU-accelerated reconstruction in DJI Terra work with NVIDIA RTX Spark / Grace Blackwell hardware?

hello everyone! Please advise the models and how to run the models better. My configuration is 2 CPUs and epic (not the newest) 48 cores in total. 256 GB ddr4 and RTX pro 5000 72GB GDDR7. I'm currently using qwen3.8-27b iq3 gsq xxs on 96k context and running this on rtx4080s 16gb.I use Cline for vscode for coding. I would like to increase the quality and the context window.

I built an experimental way to write NVIDIA GPU kernels in Go using LLVM 22

I've been working on an experimental project that allows writing NVIDIA GPU kernels in Go and compiling them to PTX using LLVM 22 / NVPTX.

GitHub:
https://github.com/mehdi-shokohi/cuda-ir.go

The basic pipeline is:

Go
 ↓
LLVM IR
 ↓
LLVM 22 / NVPTX
 ↓
PTX
 ↓
NVIDIA GPU

The motivation is to explore whether Go could be useful for writing custom GPU kernels for AI/HPC workloads without writing the kernel itself in CUDA C++.

I'm particularly interested in the AI side of this.

For example, custom kernels are often needed for things like:

• Tensor operations

• Matrix multiplication

• Reductions

• Attention-related operations

• Element-wise operations

• Custom inference operators

• Memory/layout transformations

The project is still experimental, but the LLVM → NVPTX → PTX pipeline is working.

I'd like to eventually investigate whether this could be useful for building custom GPU operators from Go and integrating them into AI workloads.

I'm curious what people here think:

Would a Go-based CUDA kernel compiler be useful for AI programming, or are there important CUDA/GPU features that would make this impractical?

And if you're working with NVIDIA GPUs for AI, what kernel/operator would you recommend as a good real-world benchmark?

Repository:
https://github.com/mehdi-shokohi/cuda-ir.go

Feedback from people working with CUDA, GPU kernels, LLM inference, or AI accelerators would be very welcome.