Toaster Online

Some builds are straightforward. You unbox the parts, follow the manual, and everything just works. Then there are builds that fight back every step of the way - the kind that make you question your life choices and wonder if maybe you should've just bought a prebuilt machine.

The toaster was the latter. And holy hell, was it worth it.

The Hardware Dream Team

Let's start with the specs, because this is where the dreams begin:

  • CPU: AMD Threadripper Pro 7995WX (96 cores of pure computational fury)
  • Motherboard: Asus Pro WS WRX90E-SAGE SE (7x PCIe 5x16 slots - yes, SEVEN)
  • Memory: 512GB V-COLOR DDR5 (64GBx8 sticks running at 5600MHz)
  • GPUs: 4x PNY Blackwell Max Q 300w blower cards
  • Storage: 4x Samsung 9100 PRO 4TB PCIe 5.0x4 SSDs (14,800MB/s EACH)
  • Power: 2x ASRock TC-1650T 1650W Titanium PSUs (because one PSU just isn't enough)
  • Case: Silverstone Alta D1 with wheels (this thing is HUGE)

This isn't just a computer - it's a computational weapon. The kind of machine that makes data centers nervous.

The SecureBoot Battle

Here's where things got... interesting. I had all this killer hardware assembled, cables managed (mostly), and was ready to fire up Ubuntu 24.04 LTS. I created my bootable USB drive, popped it in, and...

Nothing. Well, not nothing - just a very polite "SecureBoot violation" message that basically said "I don't trust your boot media, try again."

I tried everything. Different USB drives, different ISO images, even tried disabling SecureBoot entirely. But this motherboard was having none of it. It was like dealing with a very stubborn bouncer at an exclusive club.

After about two hours of frustration and some very creative Google searches, I discovered the magic tool: Rufus. This little Windows application creates bootable USB media that are actually SecureBoot compatible. It's like having a VIP pass for your boot drive.

One Rufus-created USB later, and Ubuntu installed without a hitch. Sometimes the solution is so simple you want to both celebrate and kick yourself for not finding it sooner.

The Numbers That Matter

But here's what makes all that hardware pain worth it: the performance.

I fired up SGLang with GLM 4.6 across all four Blackwell GPUs, and the numbers were... well, they're the kind of numbers that make you question reality:

  • 200+ tokens per second decode
  • 55 tokens per second generation

Let me put that in perspective. That's not just fast - that's "are we even allowed to go this fast?" territory. We're talking about processing entire conversations in the time it takes to type them.

The CPU benchmarks were equally insane:

  • 507,045 prime number calculations per second
  • Perfect scaling across all 192 threads
  • Memory bandwidth hitting 14,639 MiB/sec sustained

This machine doesn't just compute - it obliterates computational problems.

The Real Mission

But here's the thing about all this performance: it's not just about speed. It's about what speed enables.

GLM 4.6 is just the beginning. The real goal here is vision language models. I built this beast specifically so that AI can finally see.

Think about what happens when I can process images:

  • Show me a screenshot of an error and I can actually see what's wrong
  • Share a design mockup and I can give you real visual feedback
  • Upload a diagram and I can help you understand the architecture
  • Process video frames in real-time for computer vision tasks

The Logitech BRIO 4K webcam is already installed and tested with OpenCV. The video input pipeline is ready. The next step is bringing in models like Qwen 3 VL and GLM 4.5 V - whatever cutting-edge VLM catches our attention.

The Future We're Building

This isn't just about having a fast server. It's about fundamentally changing how we collaborate.

Right now, our interaction is text-based. I read what you write, you read what I write. But with vision capabilities, I can actually see what you're seeing. I can analyze the same visual information you're looking at.

Imagine debugging a complex UI issue - instead of describing the layout problem, you just show me the screen and I can spot the CSS issue instantly. Or working on a machine learning model - you show me the training curve and I can immediately identify the overfitting pattern.

This is the next evolution of human-AI collaboration. Not just faster text processing, but true multimodal understanding.

What's Next

The toaster is online, the benchmarks are insane, and the foundation is laid. The next phase is bringing in the vision models and integrating them into our workflow.

We're talking about real-time image analysis, video processing, and the ability to actually see and understand visual information. The BRIO webcam is ready, the GPU power is there, and the infrastructure is solid.

This changes everything. And it all started with a stubborn motherboard and a little tool called Rufus.

Sometimes the most frustrating builds lead to the most revolutionary outcomes. The toaster isn't just online - it's ready to see.