https://deploy-preview-1991--saturn-cloud.netlify.app/blog/categories/infrastructure/Recent content in Infrastructure on Saturn CloudHugo -- gohugo.ioFri, 03 Apr 2026 00:00:00 +0000https://deploy-preview-1991--saturn-cloud.netlify.app/blog/saturn-cloud-vs-aws-sagemaker-for-llm-training/Fri, 03 Apr 2026 00:00:00 +0000https://deploy-preview-1991--saturn-cloud.netlify.app/blog/saturn-cloud-vs-aws-sagemaker-for-llm-training/SageMaker is a reasonable default for teams already deep in the AWS ecosystem, building traditional ML pipelines. For teams training and deploying large language models where GPU access, setup speed, and framework flexibility are the actual constraints, it’s worth understanding exactly where SageMaker adds friction and where Saturn Cloud removes it. We’ll cover how each platform handles GPU access, what the actual setup looks like, how pricing compares for LLM workloads specifically, and the cases where SageMaker remains the better choice.https://deploy-preview-1991--saturn-cloud.netlify.app/blog/run-claude-code-on-a-cloud-gpu-in-10-minutes-no-root-workarounds-required/Thu, 02 Apr 2026 00:00:00 +0000https://deploy-preview-1991--saturn-cloud.netlify.app/blog/run-claude-code-on-a-cloud-gpu-in-10-minutes-no-root-workarounds-required/Running Claude Code in autonomous mode on a cloud GPU is a common source of friction. Most GPU cloud providers provision instances with default root shell access; however, Claude Code’s --dangerously-skip-permissions flag, which enables non-interactive execution by suppressing confirmation prompts, can’t be invoked with root privileges. On most platforms, satisfying these requirements involves manual administrative overhead: provisioning a non-privileged user, injecting public SSH keys for authentication, and delegating specific sudo permissions.https://deploy-preview-1991--saturn-cloud.netlify.app/blog/running-nvidia-nim-on-saturn-cloud/Wed, 01 Apr 2026 00:00:00 +0000https://deploy-preview-1991--saturn-cloud.netlify.app/blog/running-nvidia-nim-on-saturn-cloud/Deploying a large language model to production used to mean weeks of work: selecting an inference engine, writing custom serving code, tuning batching parameters, and benchmarking until latency was acceptable. NVIDIA NIM compresses most of that into a single container pull. This guide covers what NVIDIA NIM actually is, what it does under the hood, how it performs on H100 infrastructure, and how to run it on Saturn Cloud – from pulling the container to serving your first request.https://deploy-preview-1991--saturn-cloud.netlify.app/blog/how-to-fine-tune-llama-3-on-gpu-clusters/Tue, 31 Mar 2026 00:00:00 +0000https://deploy-preview-1991--saturn-cloud.netlify.app/blog/how-to-fine-tune-llama-3-on-gpu-clusters/Fine-tuning Llama 3 is one of the most common workloads on GPU cloud platforms today. Whether you’re adapting Llama 3 8B for a domain-specific use case or running full fine-tuning on the 70B variant, the setup decisions you make before training starts, like the GPU selection, parallelism strategy, and quantization approach, have a larger impact on your total cost and iteration speed than almost anything else. This guide covers everything you need to fine-tune Llama 3 on Saturn Cloud, including which GPU to use for which job, how to choose between LoRA, QLoRA, and FSDP, and how to get your first run off the ground quickly.https://deploy-preview-1991--saturn-cloud.netlify.app/blog/fsdp-vs-ddp-vs-deepspeed-for-llm-training/Sat, 28 Mar 2026 00:00:00 +0000https://deploy-preview-1991--saturn-cloud.netlify.app/blog/fsdp-vs-ddp-vs-deepspeed-for-llm-training/Choosing the wrong distributed training strategy is one of the most expensive mistakes you can make when training large language models. Pick DDP when you need FSDP, and you’ll hit GPU memory walls before your job completes. Use DeepSpeed when PyTorch’s native FSDP would have been simpler, and you’ll spend days debugging config files instead of training models. This guide covers what DDP, FSDP, and DeepSpeed actually do, when each one makes sense, and how to set them up for LLM training on multi-GPU clusters.https://deploy-preview-1991--saturn-cloud.netlify.app/blog/how-to-deploy-openclaw-on-saturncloud/Thu, 26 Mar 2026 00:00:00 +0000https://deploy-preview-1991--saturn-cloud.netlify.app/blog/how-to-deploy-openclaw-on-saturncloud/OpenClaw is an open-source AI agent framework that can connect to channels like WhatsApp. This article shows how to run it on Saturn Cloud. You will create one Saturn Cloud Deployment, configure OpenClaw in terminal, connect WhatsApp, then keep the gateway running from an active terminal session. This guide uses a setup-first workflow. The deployment starts with sleep infinity so you can configure OpenClaw over terminal access and run OpenClaw manually in your terminal session.https://deploy-preview-1991--saturn-cloud.netlify.app/blog/how-to-run-open-source-llm-inference-on-crusoe-from-saturn-cloud/Wed, 04 Mar 2026 00:00:00 +0000https://deploy-preview-1991--saturn-cloud.netlify.app/blog/how-to-run-open-source-llm-inference-on-crusoe-from-saturn-cloud/Crusoe’s Managed Inference service runs open-source LLMs on a proprietary inference engine powered by MemoryAlloy – a cluster-wide KV cache that shares computed context across GPUs instead of keeping it isolated per node. The result is faster time-to-first-token (up to 9.9x faster than standard vLLM) and higher throughput (up to 5x) for workloads in which prompts share common prefixes. Since Saturn Cloud runs natively on Crusoe Cloud, you can call these inference endpoints directly from your Saturn Cloud workspace – notebooks, jobs, or deployments with no extra networking or infrastructure setup.https://deploy-preview-1991--saturn-cloud.netlify.app/blog/gpu-clouds-aggregators-and-the-new-economics-of-ai-compute/Sun, 15 Feb 2026 00:00:00 +0000https://deploy-preview-1991--saturn-cloud.netlify.app/blog/gpu-clouds-aggregators-and-the-new-economics-of-ai-compute/Saturn Cloud CTO, Hugo Shi, recently joined the AI Engineering Podcast to discuss the GPU cloud landscape – how the market is structured, what services different providers offer, and how teams should think about choosing between them. You can listen to the full episode here. This post distills key insights from that conversation. The Market Has Three Distinct Tiers Hyperscalers (AWS, GCP, Azure, Oracle): Deep managed service ecosystems, but GPU pricing around $10/hour for H100s.https://deploy-preview-1991--saturn-cloud.netlify.app/blog/best-cloud-platforms-for-training-large-language-models-in-2026/Thu, 05 Feb 2026 00:00:00 +0000https://deploy-preview-1991--saturn-cloud.netlify.app/blog/best-cloud-platforms-for-training-large-language-models-in-2026/Your choice of cloud provider directly impacts training costs, iteration speed, and how much time you spend fighting infrastructure instead of shipping models. This guide evaluates platforms – hyperscalers and GPU-focused neoclouds – on multi-node cluster support, interconnect quality, H100 pricing, and operational overhead. 1. Crusoe Best for: Sustainable LLM training with carbon-conscious infrastructure Overview: Crusoe powers its GPU infrastructure with stranded or renewable energy, offering a lower-carbon option for compute-intensive training jobs.https://deploy-preview-1991--saturn-cloud.netlify.app/blog/building-models-with-saturn-cloud-and-deploying-via-nebius-token-factory/Sun, 01 Feb 2026 00:00:00 +0000https://deploy-preview-1991--saturn-cloud.netlify.app/blog/building-models-with-saturn-cloud-and-deploying-via-nebius-token-factory/Saturn Cloud now runs on Nebius AI Cloud, giving teams access to bare-metal NVIDIA H100, H200, GB200, and GB300 GPUs with InfiniBand networking – without the operational overhead of managing raw Kubernetes. This integration pairs Nebius’s hardware layer (HGX platforms, NDR400 InfiniBand, high-throughput storage) with Saturn Cloud’s orchestration (automated provisioning, environment management, cost governance). Once training is complete, models ship directly to Nebius Token Factory for production inference. This article covers the architecture and walks through the setup process step by step.https://deploy-preview-1991--saturn-cloud.netlify.app/blog/bare-metal-ai-platform-k0rdent-saturn-cloud/Wed, 21 Jan 2026 00:00:00 +0000https://deploy-preview-1991--saturn-cloud.netlify.app/blog/bare-metal-ai-platform-k0rdent-saturn-cloud/Bare metal GPU providers compete on price and availability, but customers increasingly expect more than SSH access to servers. They want the platform experience they get from AWS SageMaker or GCP Vertex AI. The trend is clear: CoreWeave acquired Weights & Biases for $1.7B, DigitalOcean acquired Paperspace, and Lightning AI merged with Voltage Park. GPU providers need platform layers, not just compute. The question is how to build this stack without replicating the engineering effort AWS put into their ML platform.