{"id":2163,"date":"2026-08-11T06:06:09","date_gmt":"2026-08-11T06:06:09","guid":{"rendered":"https:\/\/convly.ai\/?p=2163"},"modified":"2026-08-11T06:06:09","modified_gmt":"2026-08-11T06:06:09","slug":"ollama-cloud-explained","status":"publish","type":"post","link":"https:\/\/convly.ai\/de\/ollama-cloud-explained\/","title":{"rendered":"Ollama Cloud: Modelle in der Cloud gegen\u00fcber lokalem Betrieb ausf\u00fchren"},"content":{"rendered":"<div class=\"convly-tldr\"><strong>TL;DR<\/strong><\/p>\n<ul>\n<li><strong>Ollama Cloud<\/strong> refers to running Ollama on cloud infrastructure (AWS, GCP, Azure) rather than local hardware\u2014same CLI and API, remote execution.<\/li>\n<li>All models in the <a href=\"https:\/\/convly.ai\/ollama-models-list-2026\/\">Ollama library<\/a> work on cloud instances; you pay hourly for GPU compute instead of buying hardware.<\/li>\n<li>Break-even point varies by usage: the <a href=\"https:\/\/convly.ai\/self-hosting-vs-api-calculator\/\">self-hosting vs API calculator<\/a> shows when cloud GPUs beat local hardware purchases.<\/li>\n<li>Privacy trade-off: cloud hosting means your prompts and responses transit the network and touch provider infrastructure, unlike fully local inference.<\/li>\n<\/ul>\n<\/div>\n<p>Ollama cloud deployments run the same Ollama server you&#8217;d install locally, but on rented GPU instances from AWS, Google Cloud, Azure, or other providers. You get the same model library, the same <code>ollama run<\/code> commands, and the same REST API\u2014but inference happens on remote hardware you pay for by the hour instead of hardware you own. This guide explains when cloud hosting makes sense, how pricing compares to local GPUs, and what you trade away in privacy and control.<\/p>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_86 counter-flat ez-toc-counter ez-toc-container-direction\">\n<label for=\"ez-toc-cssicon-toggle-item-6a7c5eb39f72c\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #000000;color:#000000\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #000000;color:#000000\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/label><input type=\"checkbox\"  id=\"ez-toc-cssicon-toggle-item-6a7c5eb39f72c\"  aria-label=\"Toggle\" \/><nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/convly.ai\/de\/ollama-cloud-explained\/#What_Ollama_Cloud_Means\" >What Ollama Cloud Means<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/convly.ai\/de\/ollama-cloud-explained\/#How_Cloud_Ollama_Differs_from_Local_Ollama\" >How Cloud Ollama Differs from Local Ollama<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/convly.ai\/de\/ollama-cloud-explained\/#CLI_and_API_Compatibility\" >CLI and API Compatibility<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/convly.ai\/de\/ollama-cloud-explained\/#Which_Models_Are_Available\" >Which Models Are Available<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/convly.ai\/de\/ollama-cloud-explained\/#Pricing_Cloud_vs_Local_vs_API_Services\" >Pricing: Cloud vs Local vs API Services<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/convly.ai\/de\/ollama-cloud-explained\/#Privacy_and_Data_Control\" >Privacy and Data Control<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/convly.ai\/de\/ollama-cloud-explained\/#When_to_Choose_Cloud_Over_Local_Hardware\" >When to Choose Cloud Over Local Hardware<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/convly.ai\/de\/ollama-cloud-explained\/#Setting_Up_Ollama_on_a_Cloud_Instance\" >Setting Up Ollama on a Cloud Instance<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/convly.ai\/de\/ollama-cloud-explained\/#Performance_Considerations\" >Performance Considerations<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/convly.ai\/de\/ollama-cloud-explained\/#Frequently_Asked_Questions\" >Frequently Asked Questions<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"What_Ollama_Cloud_Means\"><\/span>What Ollama Cloud Means<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>There is no standalone &#8220;Ollama Cloud&#8221; product as of early 2026. When developers say &#8220;Ollama cloud,&#8221; they mean one of two things:<\/p>\n<ol>\n<li><strong>Self-hosted Ollama on cloud VMs:<\/strong> You rent a GPU-equipped virtual machine from AWS EC2, Google Compute Engine, Azure, Lambda Labs, or RunPod, install Ollama yourself, and run models there. You manage the instance, but you&#8217;re not buying hardware.<\/li>\n<li><strong>Managed Ollama services:<\/strong> Third-party platforms that pre-install Ollama, handle scaling, and bill you per request or per minute. These are less common and typically built on top of approach #1.<\/li>\n<\/ol>\n<p>Both contrast with running <a href=\"https:\/\/convly.ai\/what-is-ollama-complete-guide-2026\/\">Ollama locally<\/a> on your own desktop or server. The Ollama binary, the model library, the CLI, and the API remain identical\u2014only the execution location changes.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"How_Cloud_Ollama_Differs_from_Local_Ollama\"><\/span>How Cloud Ollama Differs from Local Ollama<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<table>\n<thead>\n<tr>\n<th>Dimension<\/th>\n<th>Local Ollama<\/th>\n<th>Cloud Ollama<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><strong>Hardware cost<\/strong><\/td>\n<td>Upfront GPU purchase ($500\u2013$2500)<\/td>\n<td>Hourly rental ($0.50\u2013$5\/hour depending on GPU)<\/td>\n<\/tr>\n<tr>\n<td><strong>Inference speed<\/strong><\/td>\n<td>Depends on your GPU; no network latency<\/td>\n<td>Depends on rented GPU; adds 20\u2013100ms network round-trip<\/td>\n<\/tr>\n<tr>\n<td><strong>Privacy<\/strong><\/td>\n<td>Prompts never leave your machine<\/td>\n<td>Prompts and responses travel over the network; cloud provider sees traffic metadata<\/td>\n<\/tr>\n<tr>\n<td><strong>Scaling<\/strong><\/td>\n<td>Fixed by your hardware<\/td>\n<td>Spin up larger or additional instances on demand<\/td>\n<\/tr>\n<tr>\n<td><strong>Maintenance<\/strong><\/td>\n<td>You manage OS, drivers, Ollama updates<\/td>\n<td>You manage the VM (self-hosted) or the platform manages it (managed services)<\/td>\n<\/tr>\n<tr>\n<td><strong>Availability<\/strong><\/td>\n<td>Tied to your machine&#8217;s uptime<\/td>\n<td>Always-on if you keep the instance running; you pay for idle time<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>The functional experience is identical. A script that calls <code>curl http:\/\/localhost:11434\/api\/generate<\/code> works unchanged if you swap <code>localhost<\/code> for your cloud instance&#8217;s IP address.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"CLI_and_API_Compatibility\"><\/span>CLI and API Compatibility<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Ollama&#8217;s CLI and REST API are transport-agnostic. To point the CLI at a cloud instance instead of a local server:<\/p>\n<pre><code>export OLLAMA_HOST=http:\/\/203.0.113.42:11434\nollama run llama3.1:8b<\/code><\/pre>\n<p>Replace <code>203.0.113.42<\/code> with your cloud VM&#8217;s public IP. The model downloads to the cloud instance, and inference runs there. Your terminal streams responses back over the network.<\/p>\n<p>For API clients, change the base URL:<\/p>\n<pre><code>curl http:\/\/203.0.113.42:11434\/api\/generate -d '{\n  \"model\": \"llama3.1:8b\",\n  \"prompt\": \"Explain neural networks in one sentence.\"\n}'<\/code><\/pre>\n<p>All endpoints (<code>\/api\/generate<\/code>, <code>\/api\/chat<\/code>, <code>\/api\/embeddings<\/code>) behave identically. Client libraries (Python, JavaScript, Go) accept a custom host parameter:<\/p>\n<pre><code>import ollama\nclient = ollama.Client(host='http:\/\/203.0.113.42:11434')\nresponse = client.chat(model='llama3.1:8b', messages=[...])<\/code><\/pre>\n<p>No code changes are required beyond the endpoint URL.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Which_Models_Are_Available\"><\/span>Which Models Are Available<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Every model in the Ollama library works on cloud instances. The <a href=\"https:\/\/convly.ai\/ollama-models-list-2026\/\">Ollama models list<\/a> includes Llama 3.1, Mistral, Gemma 2, Qwen, Phi, DeepSeek, and dozens of others. Model availability is not restricted by where Ollama runs.<\/p>\n<p>The constraint is GPU VRAM. A cloud instance with an NVIDIA L4 (24 GB VRAM) can run the same quantized models as a local RTX 4090. A smaller instance with 16 GB VRAM is limited to smaller models or heavier quantization, just like local hardware. Use the <a href=\"https:\/\/convly.ai\/llm-vram-calculator\/\">VRAM calculator<\/a> to determine which instance type you need for a given model and quantization level.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Pricing_Cloud_vs_Local_vs_API_Services\"><\/span>Pricing: Cloud vs Local vs API Services<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Cloud GPU pricing varies by provider and GPU type. Representative hourly rates as of early 2026:<\/p>\n<table>\n<thead>\n<tr>\n<th>Provider<\/th>\n<th>GPU<\/th>\n<th>VRAM<\/th>\n<th>Cost\/hour<\/th>\n<th>Suitable for<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>AWS EC2 g5.xlarge<\/td>\n<td>NVIDIA A10G<\/td>\n<td>24 GB<\/td>\n<td>~$1.00<\/td>\n<td>Llama 3.1 8B, Mistral 7B<\/td>\n<\/tr>\n<tr>\n<td>GCP n1 + T4<\/td>\n<td>NVIDIA T4<\/td>\n<td>16 GB<\/td>\n<td>~$0.50<\/td>\n<td>Smaller models, quantized 7B<\/td>\n<\/tr>\n<tr>\n<td>Lambda Labs A10<\/td>\n<td>NVIDIA A10<\/td>\n<td>24 GB<\/td>\n<td>~$0.60<\/td>\n<td>Llama 3.1 8B, Mistral 7B<\/td>\n<\/tr>\n<tr>\n<td>RunPod RTX 4090<\/td>\n<td>RTX 4090<\/td>\n<td>24 GB<\/td>\n<td>~$0.69<\/td>\n<td>Llama 3.1 8B, Mistral 7B<\/td>\n<\/tr>\n<tr>\n<td>Azure NC6s v3<\/td>\n<td>NVIDIA V100<\/td>\n<td>16 GB<\/td>\n<td>~$3.00<\/td>\n<td>Legacy option, often cheaper alternatives exist<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>If you run a cloud instance 24\/7, a $0.60\/hour instance costs $432\/month or $5,184\/year. A local RTX 4090 (~$1,600) breaks even in under four months of continuous use. The <a href=\"https:\/\/convly.ai\/self-hosting-vs-api-calculator\/\">self-hosting vs API calculator<\/a> models this across different usage patterns.<\/p>\n<p>The break-even point shifts based on utilization:<\/p>\n<ul>\n<li><strong>Heavy usage (8+ hours\/day):<\/strong> Local hardware pays for itself in months.<\/li>\n<li><strong>Intermittent usage (a few hours\/week):<\/strong> Cloud instances win; you only pay for active hours.<\/li>\n<li><strong>Burst workloads:<\/strong> Cloud lets you rent a high-end GPU for a short task without buying one.<\/li>\n<\/ul>\n<p>Compare this to hosted API services like OpenAI, Anthropic, or Groq, which charge per token. For Llama 3.1 8B via a hosted API, typical pricing is $0.10\u20130.30 per million input tokens and $0.30\u20130.60 per million output tokens. Whether that&#8217;s cheaper than cloud Ollama depends on your request volume and average response length. The <a href=\"https:\/\/convly.ai\/ai-api-cost-calculator\/\">API cost calculator<\/a> breaks this down by model and monthly usage.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Privacy_and_Data_Control\"><\/span>Privacy and Data Control<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Running Ollama locally means your prompts, model outputs, and any documents you process never leave your machine. This is critical for regulated industries (healthcare, legal, finance) or proprietary data.<\/p>\n<p>Running Ollama on a cloud VM introduces these risks:<\/p>\n<ul>\n<li><strong>Network transit:<\/strong> Prompts and responses travel between your client and the cloud instance, potentially over the public internet unless you use a VPN or private network.<\/li>\n<li><strong>Cloud provider access:<\/strong> AWS, Google, and Azure have technical access to your VM&#8217;s memory and disk. While they contractually commit not to inspect customer data, the possibility exists.<\/li>\n<li><strong>Logs and metadata:<\/strong> Cloud providers log network connections, API calls to their management APIs, and billing events. These logs reveal when you&#8217;re running inference and how much compute you&#8217;re using, even if they don&#8217;t see prompt content.<\/li>\n<li><strong>Data residency:<\/strong> Your VM runs in a specific AWS region or GCP zone. If your compliance framework restricts data location, you must choose a region accordingly.<\/li>\n<\/ul>\n<p>If your threat model includes nation-state actors or cloud provider subpoenas, local inference is the only option. If you&#8217;re optimizing cost and convenience and your data is not sensitive, cloud hosting is viable.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"When_to_Choose_Cloud_Over_Local_Hardware\"><\/span>When to Choose Cloud Over Local Hardware<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Choose <strong>cloud Ollama<\/strong> when:<\/p>\n<ul>\n<li>You need access to LLMs but don&#8217;t own a GPU and can&#8217;t justify the upfront cost of a <a href=\"https:\/\/convly.ai\/best-gpus-for-local-llms-2026\/\">capable GPU<\/a> ($800+).<\/li>\n<li>Your usage is intermittent\u2014a few hours per week or month\u2014and you&#8217;d rather pay for compute as you use it.<\/li>\n<li>You need to scale up temporarily for a large batch job, then scale back down.<\/li>\n<li>You&#8217;re prototyping and want to test different GPU types (16 GB, 24 GB, 40 GB) before committing to hardware.<\/li>\n<li>You&#8217;re building a service that needs 24\/7 uptime and redundancy, and managing your own server hardware isn&#8217;t feasible.<\/li>\n<\/ul>\n<p>Choose <strong>local Ollama<\/strong> when:<\/p>\n<ul>\n<li>You already own a GPU with 12+ GB VRAM, or you&#8217;re willing to buy one.<\/li>\n<li>You run inference daily for multiple hours; the hardware pays for itself quickly.<\/li>\n<li>Privacy is non-negotiable\u2014your data cannot leave your premises.<\/li>\n<li>You want zero per-request costs and predictable expenses.<\/li>\n<li>You&#8217;re offline or on a network with restricted outbound access.<\/li>\n<\/ul>\n<p>The <a href=\"https:\/\/convly.ai\/self-hosting-vs-api-calculator\/\">self-hosting vs API calculator<\/a> lets you input your expected monthly usage (hours of inference, number of requests, average tokens per request) and compare the cost of buying a GPU, renting a cloud instance, or using a hosted API service. For most developers running models a few hours a day, local hardware wins after 3\u20136 months.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Setting_Up_Ollama_on_a_Cloud_Instance\"><\/span>Setting Up Ollama on a Cloud Instance<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>The process is the same across providers: launch a GPU instance, SSH in, install Ollama, and expose port 11434.<\/p>\n<h3>AWS EC2<\/h3>\n<ol>\n<li>Launch a <code>g5.xlarge<\/code> or <code>g5.2xlarge<\/code> instance (Ubuntu 22.04 LTS, NVIDIA A10G GPU).<\/li>\n<li>SSH into the instance: <code>ssh -i your-key.pem ubuntu@&lt;instance-ip&gt;<\/code><\/li>\n<li>Install Ollama: <code>curl -fsSL https:\/\/ollama.com\/install.sh | sh<\/code><\/li>\n<li>Start Ollama: <code>ollama serve<\/code> (or set it up as a systemd service).<\/li>\n<li>Pull a model: <code>ollama pull llama3.1:8b<\/code><\/li>\n<li>Configure the security group to allow inbound TCP on port 11434 from your IP.<\/li>\n<\/ol>\n<h3>Google Cloud Platform<\/h3>\n<ol>\n<li>Create a Compute Engine VM with a T4 or A100 GPU (select a GPU-enabled machine family).<\/li>\n<li>SSH via the GCP console or <code>gcloud compute ssh<\/code>.<\/li>\n<li>Install Ollama: <code>curl -fsSL https:\/\/ollama.com\/install.sh | sh<\/code><\/li>\n<li>Start Ollama: <code>ollama serve<\/code><\/li>\n<li>Update firewall rules to allow TCP 11434 from your IP range.<\/li>\n<\/ol>\n<h3>Lambda Labs or RunPod<\/h3>\n<ol>\n<li>Rent an instance with an RTX 4090 or A10.<\/li>\n<li>SSH in using the provided credentials.<\/li>\n<li>Install Ollama: <code>curl -fsSL https:\/\/ollama.com\/install.sh | sh<\/code><\/li>\n<li>Start Ollama and pull models as above.<\/li>\n<\/ol>\n<p>For production use, run Ollama as a systemd service so it restarts on reboot, and use a reverse proxy (Nginx or Caddy) with TLS if you&#8217;re exposing it to the public internet.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Performance_Considerations\"><\/span>Performance Considerations<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Cloud instances add network latency. A local Ollama server responds in under 5ms for the first token (after model load). A cloud instance adds the round-trip time from your machine to the data center\u2014typically 20\u201350ms within the same region, 80\u2013150ms cross-continent. For interactive chat, this is perceptible but not disabling. For batch workloads, it&#8217;s negligible.<\/p>\n<p>Token generation speed depends on the GPU, not the location. An A10G in AWS generates tokens at the same rate as an A10G on your desk. However, cloud instances may have noisy-neighbor effects: other VMs on the same physical host can degrade performance. Dedicated instances or bare-metal GPU rentals eliminate this but cost more.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Frequently_Asked_Questions\"><\/span>Frequently Asked Questions<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3>Is there an official Ollama Cloud service?<\/h3>\n<p>As of early 2026, Ollama does not offer a managed cloud service. The term &#8220;Ollama cloud&#8221; refers to running the open-source Ollama server on cloud infrastructure you rent and manage yourself, or using a third-party platform that hosts Ollama for you. The Ollama project provides the software; you or a hosting provider supplies the compute.<\/p>\n<h3>Can I use Ollama Cloud with the same models I run locally?<\/h3>\n<p>Yes. The model library is identical. Any model you pull with <code>ollama pull<\/code> locally works on a cloud instance. The only constraint is VRAM: ensure your cloud GPU has enough memory for the model and quantization level you want. Check <a href=\"https:\/\/convly.ai\/vram-requirements-every-major-llm-2026\/\">VRAM requirements by model<\/a> to match models to instance types.<\/p>\n<h3>How do I secure Ollama running on a cloud instance?<\/h3>\n<p>By default, Ollama listens on <code>127.0.0.1:11434<\/code>, which is not accessible from outside the VM. To expose it, set <code>OLLAMA_HOST=0.0.0.0:11434<\/code> before starting the server. Then restrict access via cloud firewall rules (AWS security groups, GCP firewall rules) to allow only your IP address or your VPN. For production, place Ollama behind a reverse proxy with TLS and authentication (HTTP basic auth, API keys, or OAuth). Never expose an unauthenticated Ollama server to the public internet\u2014it allows anyone to run arbitrary models at your expense.<\/p>\n<h3>What&#8217;s cheaper: Ollama on a cloud GPU or using OpenAI&#8217;s API?<\/h3>\n<p>It depends on usage. For a 7B parameter model, a cloud GPU costs roughly $0.50\u20131.00\/hour. If you generate 10 million tokens\/hour, that&#8217;s $0.05\u20130.10 per million tokens\u2014cheaper than most hosted APIs for equivalent-size models. But if you generate only 1 million tokens\/hour, you&#8217;re paying $0.50\u20131.00 per million tokens, which is more expensive than API services. Hosted APIs also handle scaling, uptime, and model updates for you. Use the <a href=\"https:\/\/convly.ai\/self-hosting-vs-api-calculator\/\">self-hosting vs API calculator<\/a> to model your specific workload.<\/p>\n<h3>Does running Ollama in the cloud make my data less private?<\/h3>\n<p>Yes, compared to fully local inference. Your prompts and model outputs travel over the network and land on a VM that the cloud provider has root access to. If privacy is critical\u2014handling HIPAA-protected health data, legal documents under attorney-client privilege, or proprietary code\u2014run Ollama locally. If your data is not sensitive or you trust your cloud provider&#8217;s contractual commitments, cloud hosting is a reasonable trade-off for cost and convenience.<\/p>\n<h3>Can I run multiple models on one cloud instance?<\/h3>\n<p>Yes, as long as the instance has enough VRAM to hold the models in memory simultaneously. Ollama loads models on demand and keeps them in VRAM until memory pressure evicts them. A 24 GB instance can hold Llama 3.1 8B (roughly 8 GB for Q8 quantization) and Mistral 7B (similar size) at the same time. Switching between loaded models is instant; loading a new model from disk takes a few seconds.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>TL;DR Ollama Cloud refers to running Ollama on cloud infrastructure (AWS, GCP, Azure) rather than local hardware\u2014same CLI and API, [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":2164,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[7],"tags":[],"class_list":["post-2163","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-news"],"_links":{"self":[{"href":"https:\/\/convly.ai\/de\/wp-json\/wp\/v2\/posts\/2163","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/convly.ai\/de\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/convly.ai\/de\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/convly.ai\/de\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/convly.ai\/de\/wp-json\/wp\/v2\/comments?post=2163"}],"version-history":[{"count":1,"href":"https:\/\/convly.ai\/de\/wp-json\/wp\/v2\/posts\/2163\/revisions"}],"predecessor-version":[{"id":2165,"href":"https:\/\/convly.ai\/de\/wp-json\/wp\/v2\/posts\/2163\/revisions\/2165"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/convly.ai\/de\/wp-json\/wp\/v2\/media\/2164"}],"wp:attachment":[{"href":"https:\/\/convly.ai\/de\/wp-json\/wp\/v2\/media?parent=2163"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/convly.ai\/de\/wp-json\/wp\/v2\/categories?post=2163"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/convly.ai\/de\/wp-json\/wp\/v2\/tags?post=2163"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}