Turns out cheap tokens get engineers doing things that save time, like debugging and tackling Jira tickets
  • *for certain definition of self hosted.

Developed by Chinese open weights model dev Z.ai, GLM 5.1 isn’t exactly a small model. At roughly 754 billion total parameters (40 billion activated for each token generated), the model requires roughly 380 GB of GPU memory just to hold the weights and another 6-26 GB per user to maintain context depending on precision.

To serve GLM-5.3 at scale, Revolut deployed 56 Nvidia B200 GPUs, equivalent in GPU count to seven eight-GPU DGX B200 systems. But as Abitbol noted, the greatest performance gains didn’t come from adding GPUs, but rather tuning the model’s hyperparameters along with switching to NVFP4 quantization, implementing disaggregated prefill and decode, and tuning context lengths.

  • Spite_By_Design_LLC@lemmy.dbzer0.comOPEnglish
    2·
    1 day ago

    In the spirit of things, I asked GLM5.3 what that set up actually looks like. Ouch.

    Fetched that page for real. Specs (from gpuworld’s DGX B200 datasheet page):

    Dimensions: 17.5" W × 87.5" D × 42.9" H (cm: 44.5 × 222.3 × 109.0)
Weight: 275 lb (124.7 kg)
Rack height: 10U equivalent
Power: up to 14.3 kW max per system

    In real-world terms:

    Height: 10U = 10 rack units = 10 × 1.75" = 17.5" tall — about the height of a bar fridge standing on its side. But that's the chassis; you'll also want ~2U of clearance for airflow/management, so plan for ~1.5U of breathing room above it in a rack.
Depth: 87.5cm = just under a metre — so it'll stick out of a standard 1m-deep server rack unless you've got deep racks (or you let the back panel hang open). Standard 42U racks are typically 1.1–1.2m deep to handle this.
Weight: 275 lb / 125kg. That's a hefty man's weight — two people minimum to lift, ideally four or a rack lift. Standard datacentre floor loading is usually fine at this density but worth checking if you're stacking multiple in a rack.

    Rack math for 7 units: 70U of just these systems — so a single 42U rack can’t hold them all (you’d need 2 racks), and at ~14kW each you’re looking at ~100kW total draw — that’s serious power/cooling, way beyond a standard office closet.

    Short version: think “fridge-sized box, half a tonne with rails, eats 14kW” — not a pizza box.

      • Spite_By_Design_LLC@lemmy.dbzer0.comOPEnglish
        1·
        1 day ago

        BRB, just getting some scratch lotto tickets.

        14.3 kw. That’s fucking obscene.

        And by all rights, the Fintech datacentre is what…small? medium?

        GLM again:

        One DGX B200 flat-out: 14.3 kW × 24h = ~343 kWh/day ≈ what a typical UK house uses in a month

        Datacentre size: a full rack of AI gear like this is ~50–100kW. Classification-wise:

        Your average office server closet: 5–20 kW
Small datacentre: <1 MW
Mid-size / enterprise: 1–10 MW
Hyperscale (AWS/Google/Microsoft): 50–500+ MW per campus

        Revolut’s 56 GPUs ≈ one dense rack or two — well under 1 MW, so firmly “small,” and that’s exactly why they colocate/rent space (“a US-based service”) rather than owning a facility. Self-hosting the GPUs doesn’t mean self-hosting the building. You don’t put 100kW of fridges in your London office — you rent rack space in a proper colo where the power and cooling already exist.

        Enjoy the lotto. If you win, you can buy an eighth of a DGX.

        Fuuuuuuuuuck