https://deploy-preview-1991--saturn-cloud.netlify.app/blog/categories/ai/ml/Recent content in AI/ML on Saturn CloudHugo -- gohugo.iohttps://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.https://deploy-preview-1991--saturn-cloud.netlify.app/blog/deploying-nvidia-nim-on-saturn-cloud/Thu, 01 Jan 2026 00:00:00 +0000https://deploy-preview-1991--saturn-cloud.netlify.app/blog/deploying-nvidia-nim-on-saturn-cloud/NVIDIA NIM packages LLM inference into containers that run on any NVIDIA GPU infrastructure. Saturn Cloud makes deploying these containers straightforward so you get a running inference endpoint without managing Kubernetes, container registries, or GPU scheduling yourself. This guide covers what NIM is, why it matters for production inference, and how to get a model running on Saturn Cloud. What is NVIDIA NIM? NIM (NVIDIA Inference Microservices) is a set of containerized inference services for AI models.https://deploy-preview-1991--saturn-cloud.netlify.app/blog/gpu-cloud-providers-owners-vs-aggregators-vs-colocation/Mon, 22 Dec 2025 00:00:00 +0000https://deploy-preview-1991--saturn-cloud.netlify.app/blog/gpu-cloud-providers-owners-vs-aggregators-vs-colocation/For AI teams scaling their models, the biggest variable isn’t the GPU, it’s the underlying infrastructure. Not all GPU cloud providers are built the same. Understanding a provider’s infrastructure ownership model is crucial for guaranteeing pricing stability, consistent capacity, and reliable support for your production workloads. GPU cloud providers fall into three categories based on how they source and control their infrastructure. 1. Owners These providers build and operate their own data centers, own their GPU hardware, and control the whole stack.https://deploy-preview-1991--saturn-cloud.netlify.app/blog/infiniband-vs-roce-for-ai-training/Fri, 19 Dec 2025 00:00:00 +0000https://deploy-preview-1991--saturn-cloud.netlify.app/blog/infiniband-vs-roce-for-ai-training/If you’re evaluating GPU cloud providers, you’ll see “400Gb/s InfiniBand” mentioned constantly. But what does it actually mean for your workloads, and when should you care? The short answer: InfiniBand matters for distributed training across 16+ GPUs. If you’re running inference, fine-tuning on a single node, or training smaller models on 1-8 GPUs, standard networking is fine. Skip the premium tiers and save your budget. For everyone else running serious, multi-node jobs, this guide explains what to look for.https://deploy-preview-1991--saturn-cloud.netlify.app/blog/slurm-on-kubernetes-nebius/Wed, 17 Dec 2025 00:00:00 +0000https://deploy-preview-1991--saturn-cloud.netlify.app/blog/slurm-on-kubernetes-nebius/Running SLURM on Kubernetes with Nebius You have a Kubernetes cluster with H100s. Pods can request GPUs. Prometheus is scraping metrics. Everything works. So why would anyone want to run SLURM on top of it? The answer is that Kubernetes and SLURM solve different scheduling problems. Kubernetes excels at running stateless services, keeping them healthy, and scaling them up or down. SLURM excels at running batch compute jobs with complex dependencies, resource quotas, and fair access across teams.https://deploy-preview-1991--saturn-cloud.netlify.app/blog/crusoe-nccl-cluster-validation/Mon, 15 Dec 2025 00:00:00 +0000https://deploy-preview-1991--saturn-cloud.netlify.app/blog/crusoe-nccl-cluster-validation/You’ve provisioned a multi-node GPU cluster with InfiniBand. Before you spend GPU-hours on training, run a 2-minute NCCL benchmark to confirm your interconnect is actually performing at spec. This is the difference between “is it working?” (ibstat shows Active) and “is it working well?” (measured bandwidth matches hardware capability). Discovering a misconfigured cluster 4 hours into a training run is expensive. Comparing GPU cloud providers? Download our GPU Cloud Comparison Report analyzing 17 providers across pricing, InfiniBand networking, storage, and enterprise readiness.https://deploy-preview-1991--saturn-cloud.netlify.app/blog/crusoe-terraform-multi-node-gpu-training/Sat, 13 Dec 2025 00:00:00 +0000https://deploy-preview-1991--saturn-cloud.netlify.app/blog/crusoe-terraform-multi-node-gpu-training/This article walks through provisioning a 2-node GPU training cluster on Crusoe using Terraform. By the end, you’ll have two 8-GPU A100 nodes connected via InfiniBand, ready for distributed training with PyTorch DDP or DeepSpeed. Why InfiniBand Matters Multi-node training synchronizes gradients between GPUs every iteration. The interconnect becomes the bottleneck in distributed computing: Interconnect Bandwidth All-reduce time (1GB) 100GbE 100 Gbps ~80ms InfiniBand HDR 200 Gbps ~40ms InfiniBand NDR 400 Gbps ~20ms At thousands of iterations, this compounds.https://deploy-preview-1991--saturn-cloud.netlify.app/blog/getting-started-with-saturn-cloud-on-crusoe/Fri, 12 Dec 2025 00:00:00 +0000https://deploy-preview-1991--saturn-cloud.netlify.app/blog/getting-started-with-saturn-cloud-on-crusoe/The Platform Engineer’s Problem If you’re running platform for an AI team, you likely have a backlog of projects: integrating the training pipeline with your feature store, building compliance automation for your industry’s data retention requirements, setting up model drift monitoring specific to your domain, optimizing the data loader for your particular workloads. Before you can work on that backlog, you’re frontline support for issues that eat up your week:https://deploy-preview-1991--saturn-cloud.netlify.app/blog/crusoe-terraform-infiniband-network-planning/Fri, 05 Dec 2025 00:00:00 +0000https://deploy-preview-1991--saturn-cloud.netlify.app/blog/crusoe-terraform-infiniband-network-planning/This guide answers the questions that come up when provisioning InfiniBand-connected GPU clusters on Crusoe with Terraform. Why Crusoe GPU clouds like Crusoe offer access to top-tier GPUs without the quota approvals and multi-month waitlists common on hyperscalers. Crusoe has H100, H200, GB200, and AMD MI355X instances available. Pricing runs $3.90/GPU-hour for H100 and $4.29/GPU-hour for H200 on-demand, compared to $6-7/GPU-hour on Azure. Spot pricing drops to $1.60/GPU-hour for H100.https://deploy-preview-1991--saturn-cloud.netlify.app/blog/gpu-cloud-comparison-neoclouds-2025/Fri, 05 Dec 2025 00:00:00 +0000https://deploy-preview-1991--saturn-cloud.netlify.app/blog/gpu-cloud-comparison-neoclouds-2025/If you’re running AI training workloads and hitting GPU availability limits or cost walls on AWS, GCP, or Azure, a wave of specialized GPU cloud providers (often called “neoclouds”) offer an alternative. These providers focus exclusively on GPU infrastructure, often with simpler pricing, immediate availability, and hardware optimized for AI workloads. This guide compares 17 GPU cloud providers across the dimensions that matter for production AI training: GPU pricing, InfiniBand networking, storage options, and platform capabilities.https://deploy-preview-1991--saturn-cloud.netlify.app/blog/getting-started-with-saturn-cloud-on-nebius/Mon, 24 Nov 2025 00:00:00 +0000https://deploy-preview-1991--saturn-cloud.netlify.app/blog/getting-started-with-saturn-cloud-on-nebius/The Platform Engineer’s Problem If you’re running platform for an AI team, you likely have a backlog of projects: integrating the training pipeline with your feature store, building compliance automation for your industry’s data retention requirements, setting up model drift monitoring specific to your domain, optimizing the data loader for your particular workloads. Before you can work on that backlog, you’re frontline support for issues that eat up your week:https://deploy-preview-1991--saturn-cloud.netlify.app/blog/migrating-gen-ai-workloads-from-hyperscalers-to-crusoe/Thu, 20 Nov 2025 00:00:00 +0000https://deploy-preview-1991--saturn-cloud.netlify.app/blog/migrating-gen-ai-workloads-from-hyperscalers-to-crusoe/If you’re running gen AI workloads on AWS, GCP, or Azure, you’re likely experiencing the GPU availability crunch: multi-month waitlists for H100s, capacity reservations that require long-term commitments, and pricing that can reach $12+ per GPU-hour. Crusoe Cloud offers immediate access to NVIDIA’s latest GPUs (H100, H200, B200, GB200) and AMD’s MI300X/MI355X starting at $3.90/hour per GPU for on-demand and $1.60/hour for spot instances, with managed Kubernetes, managed inference, and 99.https://deploy-preview-1991--saturn-cloud.netlify.app/blog/migrating-gen-ai-workloads-from-hyperscalers-to-nebius/Thu, 20 Nov 2025 00:00:00 +0000https://deploy-preview-1991--saturn-cloud.netlify.app/blog/migrating-gen-ai-workloads-from-hyperscalers-to-nebius/If you’re running gen AI workloads on AWS, GCP, or Azure, you’re likely experiencing the GPU availability crunch: multi-month waitlists for H100s, capacity reservations that require long-term commitments, and pricing that can reach $12+ per GPU-hour. Nebius offers immediate access to NVIDIA’s latest GPUs (H100, H200, B200, GB200) at $2.95/hour per GPU, with managed Kubernetes, PostgreSQL, and MLflow. Operating in US and EU regions. The Hyperscaler GPU Bottleneck The challenge with running AI workloads on traditional hyperscalers isn’t capability—it’s access and economics.https://deploy-preview-1991--saturn-cloud.netlify.app/blog/moving-gen-ai-workloads-to-neo-clouds/Thu, 20 Nov 2025 00:00:00 +0000https://deploy-preview-1991--saturn-cloud.netlify.app/blog/moving-gen-ai-workloads-to-neo-clouds/If you’re running gen AI workloads, you’re likely familiar with the constraints of traditional hyperscalers: limited GPU availability, long quota approval cycles, long-term contracts required to access top-tier GPUs like H100s, and infrastructure designed for general compute rather than AI-specific needs. Neo clouds, GPU-specialized providers like Nebius, Crusoe, and CoreWeave, offer an alternative: direct access to newer GPU hardware, different pricing structures, and infrastructure built around high-performance compute workloads. The tradeoff is that you’re working with smaller providers that have different APIs, tooling, and operational models than AWS, GCP, or Azure.