{"id":472,"date":"2025-12-20T08:14:00","date_gmt":"2025-12-20T07:14:00","guid":{"rendered":"https:\/\/simplepod.ai\/blog\/?p=472"},"modified":"2025-10-28T08:49:11","modified_gmt":"2025-10-28T07:49:11","slug":"how-simplepods-preconfigured-templates-ollama-jupyter-etc-perform-on-different-gpus-3060-4090-a4000","status":"publish","type":"post","link":"https:\/\/simplepod.ai\/blog\/how-simplepods-preconfigured-templates-ollama-jupyter-etc-perform-on-different-gpus-3060-4090-a4000\/","title":{"rendered":"How SimplePod\u2019s Preconfigured Templates (Ollama, Jupyter, etc.) Perform on Different GPUs (3060 \/ 4090 \/ A4000)"},"content":{"rendered":"\n

Introduction<\/strong><\/h2>\n\n\n\n

When you spin up an environment on SimplePod<\/strong>, you can launch powerful pre-configured templates \u2014 from Jupyter notebooks<\/strong> to Ollama for local LLMs<\/strong> \u2014 in just seconds.
But not all GPUs behave the same way. Some start faster, others handle heavier inference loads, and a few balance memory and cost better than expected.<\/p>\n\n\n\n

In this post, we\u2019ll explore how these environments perform on three popular GPU types available on SimplePod \u2014 the RTX 3060<\/strong>, RTX A4000<\/strong>, and RTX 4090<\/strong> \u2014 so you can choose the best fit for your workflow.<\/p>\n\n\n\n

\n\n\n\n

Why Pre-Configured Environments Matter<\/strong><\/h2>\n\n\n\n

One of SimplePod\u2019s biggest advantages is that everything is ready out of the box<\/strong>.
No driver installs, CUDA mismatches, or dependency errors \u2014 you pick a template, click Launch<\/strong>, and get coding within minutes.<\/p>\n\n\n\n

These templates cover:<\/p>\n\n\n\n

    \n
  • Ollama<\/strong> \u2013 run LLMs locally in the cloud (e.g., LLaMA, Mistral, Phi).<\/li>\n\n\n\n
  • Jupyter Notebooks<\/strong> \u2013 for data science, model training, and visualization.<\/li>\n\n\n\n
  • Automatic1111 \/ Diffusers<\/strong> \u2013 for Stable Diffusion and image generation.<\/li>\n<\/ul>\n\n\n\n

    But performance and experience can vary depending on which GPU you select.<\/p>\n\n\n\n

    \n\n\n\n

    How We Tested<\/strong><\/h2>\n\n\n\n

    To compare GPUs fairly, we launched the same set of environments and measured:<\/p>\n\n\n\n

    Metric<\/th>Description<\/th><\/tr><\/thead>
    Startup time<\/strong><\/td>Time from launch \u2192 ready state<\/td><\/tr>
    Inference speed<\/strong><\/td>Tokens\/sec (Ollama) or images\/min (diffusion)<\/td><\/tr>
    Memory usage<\/strong><\/td>VRAM consumed during active workload<\/td><\/tr>
    Responsiveness<\/strong><\/td>Subjective smoothness for Jupyter or UI tools<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

    All tests used SimplePod\u2019s default templates with clean sessions.<\/p>\n\n\n\n

    \n\n\n\n

    Performance Overview by GPU<\/strong><\/h2>\n\n\n\n

    1. RTX 3060 \u2013 The Budget Starter<\/strong><\/h3>\n\n\n\n
      \n
    • Startup time:<\/strong> ~25\u201330 seconds<\/li>\n\n\n\n
    • Ollama inference (7B models):<\/strong> 12\u201315 tokens\/sec<\/li>\n\n\n\n
    • Stable Diffusion (512\u00d7512):<\/strong> ~2.5 images\/min<\/li>\n\n\n\n
    • Memory use:<\/strong> Up to 11.2 GB (of 12 GB total)<\/li>\n<\/ul>\n\n\n\n

      \u2705 Best for:<\/strong> Students, hobbyists, and developers running lightweight workloads.
      \u26a0\ufe0f Limitations:<\/strong> Models over 13B parameters or high-res diffusion will hit VRAM limits quickly.<\/p>\n\n\n\n

      \ud83d\udca1 Tip:<\/em> Use quantized LLMs (like Q4_K_M in Ollama) and smaller batch sizes to avoid out-of-memory crashes.<\/p>\n\n\n\n

      \n\n\n\n

      2. RTX A4000 \u2013 The Mid-Range Balance<\/strong><\/h3>\n\n\n\n
        \n
      • Startup time:<\/strong> ~20\u201325 seconds<\/li>\n\n\n\n
      • Ollama inference (7B):<\/strong> ~18 tokens\/sec<\/li>\n\n\n\n
      • Stable Diffusion (512\u00d7512):<\/strong> ~3.3 images\/min<\/li>\n\n\n\n
      • Memory use:<\/strong> ~14 GB of 16 GB total<\/li>\n<\/ul>\n\n\n\n

        \u2705 Best for:<\/strong> Data scientists, educators, and developers who need slightly faster performance but want to stay budget-friendly.
        \u2705 Handles multi-notebook sessions in Jupyter<\/strong> more smoothly than the 3060.
        \u2699\ufe0f Slightly cooler running and more efficient for long inference loops.<\/p>\n\n\n\n

        \ud83d\udca1 Tip:<\/em> The A4000\u2019s larger VRAM lets you run heavier diffusion checkpoints or simultaneous Jupyter kernels without restarts.<\/p>\n\n\n\n

        \n\n\n\n

        3. RTX 4090 \u2013 The Performance Beast<\/strong><\/h3>\n\n\n\n
          \n
        • Startup time:<\/strong> ~15\u201320 seconds<\/li>\n\n\n\n
        • Ollama inference (7B\u201313B):<\/strong> ~30\u201335 tokens\/sec<\/li>\n\n\n\n
        • Stable Diffusion (1024\u00d71024):<\/strong> ~6\u20137 images\/min<\/li>\n\n\n\n
        • Memory use:<\/strong> 18\u201322 GB typical<\/li>\n<\/ul>\n\n\n\n

          \u2705 Best for:<\/strong> Power users, researchers, and teams running high-end image generation or large-model inference.
          \u2705 Great for multi-model workflows<\/strong> \u2014 run an Ollama LLM and a Jupyter notebook side-by-side without slowdown.
          \u26a1 Extremely fast at loading weights and regenerating sessions.<\/p>\n\n\n\n

          \ud83d\udca1 Tip:<\/em> For large LLMs (13B+), enable --num-thread<\/code> tuning in Ollama for smoother streaming inference.<\/p>\n\n\n\n

          \n\n\n\n

          Performance Summary<\/strong><\/h2>\n\n\n\n
          GPU<\/th>Startup Time<\/th>Inference (Ollama 7B)<\/th>Diffusion Speed<\/th>Memory Use<\/th>Ideal Use Case<\/th><\/tr><\/thead>
          RTX 3060<\/strong><\/td>25\u201330 s<\/td>12\u201315 t\/s<\/td>2.5 img\/min<\/td>~11 GB<\/td>Learning & hobby projects<\/td><\/tr>
          RTX A4000<\/strong><\/td>20\u201325 s<\/td>18 t\/s<\/td>3.3 img\/min<\/td>~14 GB<\/td>Prototyping & teaching<\/td><\/tr>
          RTX 4090<\/strong><\/td>15\u201320 s<\/td>30+ t\/s<\/td>6\u20137 img\/min<\/td>~20 GB<\/td>Heavy workloads & research<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

          As you move up the GPU line, you don\u2019t just get faster inference \u2014 you also get smoother multitasking<\/strong>, better VRAM headroom<\/strong>, and shorter template startup times<\/strong>.<\/p>\n\n\n\n

          \n\n\n\n

          Choosing the Right GPU for Your Environment<\/strong><\/h2>\n\n\n\n
            \n
          • Pick RTX 3060<\/strong> if you\u2019re learning, testing small models, or doing short inference runs.<\/li>\n\n\n\n
          • Choose RTX A4000<\/strong> if you want a balance between cost and steady multitasking.<\/li>\n\n\n\n
          • Go for RTX 4090<\/strong> when you need top-tier speed for training, rendering, or large LLM inference.<\/li>\n<\/ul>\n\n\n\n

            \ud83d\udca1 Rule of thumb:<\/em> If your environment consistently uses more than 70% VRAM, it\u2019s time to move up a tier.<\/p>\n\n\n\n

            \n\n\n\n

            Conclusion<\/strong><\/h2>\n\n\n\n

            SimplePod\u2019s pre-configured environments make GPU work painless \u2014 and now you know which card fits your workflow best.
            From the student running their first Jupyter notebook on a 3060<\/strong>, to the researcher benchmarking multi-LLM pipelines on a 4090<\/strong>, each GPU offers a unique sweet spot.<\/p>\n\n\n\n

            The key is matching your environment demands<\/strong> to the GPU\u2019s strengths<\/strong> \u2014 and with SimplePod\u2019s pay-as-you-go flexibility, you can scale up or down anytime.<\/p>\n","protected":false},"excerpt":{"rendered":"

            See how SimplePod\u2019s pre-configured templates \u2014 Ollama, Jupyter, and more \u2014 perform across different GPUs. Compare startup times, inference speed, and memory use for the 3060, A4000, and 4090 to find the right fit for your workflow.<\/p>\n","protected":false},"author":10,"featured_media":473,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-container-style":"default","site-container-layout":"default","site-sidebar-layout":"default","disable-article-header":"default","disable-site-header":"default","disable-site-footer":"default","disable-content-area-spacing":"default","footnotes":""},"categories":[1],"tags":[],"class_list":["post-472","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-no-category"],"_links":{"self":[{"href":"https:\/\/simplepod.ai\/blog\/wp-json\/wp\/v2\/posts\/472","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/simplepod.ai\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/simplepod.ai\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/simplepod.ai\/blog\/wp-json\/wp\/v2\/users\/10"}],"replies":[{"embeddable":true,"href":"https:\/\/simplepod.ai\/blog\/wp-json\/wp\/v2\/comments?post=472"}],"version-history":[{"count":1,"href":"https:\/\/simplepod.ai\/blog\/wp-json\/wp\/v2\/posts\/472\/revisions"}],"predecessor-version":[{"id":474,"href":"https:\/\/simplepod.ai\/blog\/wp-json\/wp\/v2\/posts\/472\/revisions\/474"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/simplepod.ai\/blog\/wp-json\/wp\/v2\/media\/473"}],"wp:attachment":[{"href":"https:\/\/simplepod.ai\/blog\/wp-json\/wp\/v2\/media?parent=472"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/simplepod.ai\/blog\/wp-json\/wp\/v2\/categories?post=472"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/simplepod.ai\/blog\/wp-json\/wp\/v2\/tags?post=472"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}