Glows.ai

Same RTX 3090. Same Qwen3.8-27B. Self-reported by one dev:

Normal chat: ~133 tok/s
Reusing the prompt: up to 381
Repeat question, prefix cache on: TTFT 22.4s → 0.56s

Your stack matters as much as your GPU.

"Which GPU fits my serving stack?" Ask us at glows.ai

One prompt to Fable 5.2: a 3D room in Blender, 13 animated shots, a 4K render on 10 GPUs in 52 minutes.

The agent didn't want a server. It wanted 10 GPUs for under an hour.

Agent jobs go wide and short.

Running agent-driven renders? Ask us: glows.ai

Before buying a machine, test the model on your actual workload.

Model quality, memory and speed all matter. Owning the box doesn't guarantee a better result.

Try a rented GPU before you commit: glows.ai

Great AI starts with a run that works.

Every workshop has a bench. Every builder needs one. For AI, the bench is compute: a GPU that is actually free, already set up, billed by the second.

That is what Glows is. The AI workshop for builders.

Build AI here. glows.ai

Mistral Large 4 is open-weight: 1T total, about 50B active.

Your build picks the hardware.

FP8: 1.06TB of weights. One 8×80GB node holds just 640GB.
NVFP4: 610GB, made for Blackwell. Mistral’s SGLang example uses 4 GPUs.

“Which build, which GPUs?” Get a quote at glows.ai