---
title: "GPUYard (@gpuyard) | Hashnode"
description: "Accelerate Anywhere with GPUYard. Read the latest articles by GPUYard on Hashnode."
url: "https://hashnode.com/@gpuyard"
---

@gpuyard

# GPUYard

@gpuyard · Lexington · Joined April 2026

Accelerate Anywhere with GPUYard

### [The Shift to Liquid Cooling: Preparing Datacenters for the NVIDIA GB200 NVL72](https://gpuyard.hashnode.dev/the-shift-to-liquid-cooling-preparing-datacenters-for-the-nvidia-gb200-nvl72?utm_source=hashnode&utm_medium=feed)

One GB200 NVL72 rack holds 72 Blackwell GPUs and 36 Grace CPUs. While a conventional air-cooled datacenter hall is typically designed for 20–30 kW per rack, the NVL72's power draw forces a fundamental

5 min read · [gpuyard.hashnode.dev](https://gpuyard.hashnode.dev/the-shift-to-liquid-cooling-preparing-datacenters-for-the-nvidia-gb200-nvl72?utm_source=hashnode&utm_medium=feed)

[2d ago](https://hashnode.com/posts/the-shift-to-liquid-cooling-preparing-datacenters-for-the-nvidia-gb200-nvl72/6af6b17eeccf44ca70199037)

### [From Bare-Metal to K8s: Architecting Enterprise-Grade AI Infrastructure](https://gpuyard.hashnode.dev/from-bare-metal-to-k8s-architecting-enterprise-grade-ai-infrastructure?utm_source=hashnode&utm_medium=feed)

Scaling an artificial intelligence workload from a research sandbox into a production environment breaks traditional IT infrastructure. When transitioning from prototyping a Large Language Model (LLM)

3 min read · [gpuyard.hashnode.dev](https://gpuyard.hashnode.dev/from-bare-metal-to-k8s-architecting-enterprise-grade-ai-infrastructure?utm_source=hashnode&utm_medium=feed)

[Nov 5](https://hashnode.com/posts/from-bare-metal-to-k8s-architecting-enterprise-grade-ai-infrastructure/6aec6a0ac815e0e81a486765)

### [Zero-Trust Security for Sovereign AI on GPU Clusters](https://gpuyard.hashnode.dev/zero-trust-security-for-sovereign-ai-on-gpu-clusters?utm_source=hashnode&utm_medium=feed)

Generative AI has moved from experiment to everyday tooling, bringing strict data privacy concerns with it. Where does the training data live? Who can read the model weights? For organizations handlin

5 min read · [gpuyard.hashnode.dev](https://gpuyard.hashnode.dev/zero-trust-security-for-sovereign-ai-on-gpu-clusters?utm_source=hashnode&utm_medium=feed)

[Oct 14](https://hashnode.com/posts/zero-trust-security-for-sovereign-ai-on-gpu-clusters/6acf4e073fa228374ca39121)

### [The VRAM Wall: NVIDIA H200 vs. AMD MI325X for Massive LLM Inference](https://gpuyard.hashnode.dev/the-vram-wall-nvidia-h200-vs-amd-mi325x-for-massive-llm-inference?utm_source=hashnode&utm_medium=feed)

Deploying a 400-billion parameter model like Llama 4 or a 671B Mixture-of-Experts (MoE) architecture like DeepSeek exposes an immediate hardware bottleneck. At this extreme scale, inference relies on

4 min read · [gpuyard.hashnode.dev](https://gpuyard.hashnode.dev/the-vram-wall-nvidia-h200-vs-amd-mi325x-for-massive-llm-inference?utm_source=hashnode&utm_medium=feed)

[Oct 9](https://hashnode.com/posts/the-vram-wall-nvidia-h200-vs-amd-mi325x-for-massive-llm-inference/6ac8841e288a16b2052d802a)

### [The 2026 Playbook for Scaling LLM Inference: VRAM Math, Hardware, and Bare-Metal Economics](https://gpuyard.hashnode.dev/the-2026-playbook-for-scaling-llm-inference-vram-math-hardware-and-bare-metal-economics?utm_source=hashnode&utm_medium=feed)

Getting an open-weight model like Llama 3.3 70B or DeepSeek running in a staging environment is relatively straightforward. Keeping it responsive and economically sustainable under production traffic

5 min read · [gpuyard.hashnode.dev](https://gpuyard.hashnode.dev/the-2026-playbook-for-scaling-llm-inference-vram-math-hardware-and-bare-metal-economics?utm_source=hashnode&utm_medium=feed)

[Sep 10](https://hashnode.com/posts/the-2026-playbook-for-scaling-llm-inference-vram-math-hardware-and-bare-metal-economics/6aa242e00d2b40722abf6417)