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- 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>
54 lines
3.3 KiB
Markdown
54 lines
3.3 KiB
Markdown
---
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title: "Why On-prem GPUs Still Matter for AI"
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description: "Own the stack. Own your data."
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date: "2025-08-29"
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author:
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name: "Anthony Rawlins"
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role: "CEO & Founder, CHORUS Services"
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tags:
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- "gpu compute"
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- "contextual-ai"
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- "infrastructure"
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featured: false
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---
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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.
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That’s why more teams are rethinking **on-premises GPU infrastructure**.
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## The Case for In-House Compute
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1. **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.
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2. **Control & Customization** – You own the stack: drivers, runtimes, schedulers, cluster topology. No waiting on cloud providers.
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3. **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.
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4. **Privacy & Compliance** – Your models and data stay under your governance. No shared tenancy, no external handling.
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## Not Just About Training Massive LLMs
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It’s easy to think of GPUs as “just for training giant foundation models.” But most teams today are leveraging GPUs for:
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* **Inference at scale** – low-latency deployments.
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* **Fine-tuning & adapters** – customizing smaller models.
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* **Vector search & embeddings** – powering RAG pipelines.
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* **Analytics & graph workloads** – accelerated by frameworks like RAPIDS.
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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.
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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.
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## Where This Ties Back to Chorus
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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:
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* Predictable performance for multi-agent reasoning.
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* Dedicated acceleration for embeddings and vector ops.
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* A foundation for experimenting with **HRM-inspired** approaches that don’t just make models bigger, but make them smarter.
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## The Bottom Line
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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.
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The real question isn’t whether you *can* run AI on-prem.
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It’s whether you can afford *not to*.
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