6e13451dc4
- Next.js 14 blog application with theme support - Docker containerization with volume bindings - Traefik integration with Let's Encrypt SSL - MDX support for blog posts - Theme toggle with localStorage persistence - Scheduled posts directory structure - Brand guidelines compliance with CHORUS colors 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
3.3 KiB
title, description, date, author, tags, featured
| title | description | date | author | tags | featured | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Why On-prem GPUs Still Matter for AI | Own the stack. Own your data. | 2025-08-29 |
|
|
false |
Cloud GPUs are everywhere right now, but if you’ve tried to run serious workloads, you know the story: long queues, high costs, throttling, and vendor lock-in. Renting compute might be convenient for prototypes, but at scale it gets expensive and limiting.
That’s why more teams are rethinking on-premises GPU infrastructure.
The Case for In-House Compute
- Cost at Scale – Training, fine-tuning, or heavy inference workloads rack up cloud costs quickly. Owning your own GPUs flips that equation over the long term.
- Control & Customization – You own the stack: drivers, runtimes, schedulers, cluster topology. No waiting on cloud providers.
- Latency & Data Gravity – Keeping data close to the GPUs removes bandwidth bottlenecks. If your data already lives in-house, shipping it to the cloud and back is wasteful.
- Privacy & Compliance – Your models and data stay under your governance. No shared tenancy, no external handling.
Not Just About Training Massive LLMs
It’s easy to think of GPUs as “just for training giant foundation models.” But most teams today are leveraging GPUs for:
- Inference at scale – low-latency deployments.
- Fine-tuning & adapters – customizing smaller models.
- Vector search & embeddings – powering RAG pipelines.
- Analytics & graph workloads – accelerated by frameworks like RAPIDS.
This is where recent research gets interesting. NVIDIA’s latest papers on small models show that capability doesn’t just scale with parameter count — it scales with specialization and structure. Instead of defaulting to giant black-box LLMs, we’re entering a world where smaller, domain-tuned models run faster, cheaper, and more predictably.
And with the launch of the Blackwell architecture, the GPU landscape itself is changing. Blackwell isn’t just about raw FLOPs; it’s about efficiency, memory bandwidth, and supporting mixed workloads (training + inference + data processing) on the same platform. That’s exactly the kind of balance on-prem clusters can exploit.
Where This Ties Back to Chorus
At Chorus, we think of GPUs not just as horsepower, but as the substrate that makes distributed reasoning practical. Hierarchical context and agent orchestration require low-latency, high-throughput compute — the kind that’s tough to guarantee in the cloud. On-prem clusters give us:
- Predictable performance for multi-agent reasoning.
- Dedicated acceleration for embeddings and vector ops.
- A foundation for experimenting with HRM-inspired approaches that don’t just make models bigger, but make them smarter.
The Bottom Line
The future isn’t cloud versus on-prem — it’s hybrid. Cloud for burst capacity, on-prem GPUs for sustained reasoning, privacy, and cost control. Owning your own stack is about freedom: the freedom to innovate at your pace, tune your models your way, and build intelligence on infrastructure you trust.
The real question isn’t whether you can run AI on-prem. It’s whether you can afford not to.