{"id":687,"date":"2026-04-12T06:07:00","date_gmt":"2026-04-12T04:07:00","guid":{"rendered":"https:\/\/voxmachina.co.za\/?p=687"},"modified":"2026-04-14T10:25:04","modified_gmt":"2026-04-14T08:25:04","slug":"top-11-open-source-ai-models-you-can-run-today","status":"publish","type":"post","link":"https:\/\/voxmachina.co.za\/blog\/top-11-open-source-ai-models-you-can-run-today\/","title":{"rendered":"Top 11 Open Source AI Models You Can Run Today"},"content":{"rendered":"\n

Open source AI has exploded over the last few years, putting serious power into the hands of developers, researchers, and businesses. What used to require access to massive cloud infrastructure can now, at least in some cases, run locally on a decent workstation.<\/p>\n\n\n\n

In this guide, we\u2019ll break down the top 11 open source AI models<\/strong>, what they\u2019re best at, whether you can run them locally, and what kind of hardware you\u2019ll need.<\/p>\n\n\n\n

<\/div>\n\n\n
\n
\"Gemma<\/figure>\n<\/div>\n\n\n
<\/div>\n\n\n\n

1. Gemma \u2013 Google’s Open Model<\/h4>\n\n\n\n

Google\u2019s Gemma family is one of the most important additions to the open-source AI ecosystem. Built from the same research behind Gemini, Gemma is designed specifically to be lightweight, efficient, and runnable on everyday hardware\u2014a major shift from traditional large-scale models.<\/p>\n\n\n\n

Unlike many high-end LLMs that require server-grade infrastructure, Gemma was intentionally engineered to run locally on laptops, desktops, and even mobile devices.<\/p>\n\n\n\n

Gemma is a general-purpose multimodal AI model, meaning it can handle:<\/p>\n\n\n\n

    \n
  • Text generation and chat<\/li>\n\n\n\n
  • Code generation<\/li>\n\n\n\n
  • Reasoning and problem solving<\/li>\n\n\n\n
  • (Newer versions) image, audio, and video understanding<\/li>\n<\/ul>\n\n\n\n

    It\u2019s particularly strong for:<\/strong><\/p>\n\n\n\n

      \n
    • Agent-based workflows<\/li>\n\n\n\n
    • Low-latency applications<\/li>\n\n\n\n
    • On-device AI (privacy-first use cases)<\/li>\n<\/ul>\n\n\n\n

      Local Run:<\/strong> Yes
      Hardware:<\/strong><\/p>\n\n\n\n

        \n
      • 2B \u2013 4B parameters Models : 4GB\u20138GB RAM (CPU or integrated GPU)<\/li>\n\n\n\n
      • 31B – Large Model : ~18GB VRAM (quantized) \/ 24GB\u201348GB VRAM<\/li>\n<\/ul>\n\n\n\n
        <\/div>\n\n\n
        \n
        \"Llama<\/figure>\n<\/div>\n\n\n
        <\/div>\n\n\n\n

        2. LLaMA 3 \u2013 Meta\u2019s Flagship Open Model<\/h4>\n\n\n\n

        LLaMA 3 is one of the most powerful open-weight language models available today, competing closely with proprietary models.<\/p>\n\n\n\n

        It excels at general text generation, reasoning, summarisation, and chat applications. It\u2019s widely supported across tools like Ollama, LM Studio, and Hugging Face.<\/p>\n\n\n\n

        Local Run:<\/strong> Yes (quantized versions)
        Hardware:<\/strong><\/p>\n\n\n\n

          \n
        • 8B model: 16GB RAM (runs on CPU\/GPU)<\/li>\n\n\n\n
        • 70B model: 48GB+ VRAM or distributed setup<\/li>\n<\/ul>\n\n\n\n
          <\/div>\n\n\n
          \n
          \"Mistral<\/figure>\n<\/div>\n\n\n
          <\/div>\n\n\n\n

          3. Mistral & Mixtral \u2013 Efficient and Powerful<\/h4>\n\n\n\n

          Mixtral 8x7B uses a mixture-of-experts (MoE) architecture, meaning only parts of the model activate at once\u2014making it highly efficient.<\/p>\n\n\n\n

          It performs extremely well for reasoning and coding tasks relative to its size.<\/p>\n\n\n\n

          Local Run:<\/strong> Yes
          Hardware:<\/strong><\/p>\n\n\n\n

            \n
          • 7B: 16GB RAM<\/li>\n\n\n\n
          • Mixtral: 32GB\u201364GB RAM or GPU with 24GB+ VRAM<\/li>\n<\/ul>\n\n\n\n
            <\/div>\n\n\n
            \n
            \"Falcon<\/figure>\n<\/div>\n\n\n
            <\/div>\n\n\n\n

            4. Falcon 180B \u2013 Massive Open Model<\/h3>\n\n\n\n

            Falcon 180B is one of the largest open models ever released.<\/p>\n\n\n\n

            While extremely powerful, it\u2019s not practical for most local setups unless heavily quantized.<\/p>\n\n\n\n

            Local Run:<\/strong> Limited (quantized only)
            Hardware:<\/strong><\/p>\n\n\n\n

              \n
            • Full model: Multi-GPU cluster (A100\/H100)<\/li>\n\n\n\n
            • Quantized: 64GB+ RAM minimum<\/li>\n<\/ul>\n\n\n\n
              <\/div>\n\n\n
              \n
              \"GPT-NeoX<\/figure>\n<\/div>\n\n\n
              <\/div>\n\n\n\n

              5. GPT-NeoX \u2013 Research Friendly LLM<\/h4>\n\n\n\n

              GPT-NeoX is a strong open-source alternative designed for experimentation and customization.<\/p>\n\n\n\n

              It\u2019s widely used in academic and research settings.<\/p>\n\n\n\n

              Local Run:<\/strong> Yes
              Hardware:<\/strong><\/p>\n\n\n\n

                \n
              • 20B model: 24GB\u201348GB VRAM<\/li>\n\n\n\n
              • Smaller variants: 16GB RAM possible<\/li>\n<\/ul>\n\n\n\n
                <\/div>\n\n\n
                \n
                \"Stable<\/figure>\n<\/div>\n\n\n
                <\/div>\n\n\n\n

                6. Stable Diffusion XL \u2013 Image Generation King<\/h4>\n\n\n\n

                Stable Diffusion XL (SDXL) is one of the most widely used open-source image generation models.<\/p>\n\n\n\n

                It powers everything from AI art tools to design pipelines.<\/p>\n\n\n\n

                Local Run:<\/strong> Yes (very popular locally)
                Hardware:<\/strong><\/p>\n\n\n\n

                  \n
                • Minimum: 8GB VRAM<\/li>\n\n\n\n
                • Recommended: 12GB\u201316GB VRAM GPU<\/li>\n<\/ul>\n\n\n\n
                  <\/div>\n\n\n
                  \n
                  \"Whisper<\/figure>\n<\/div>\n\n\n
                  <\/div>\n\n\n\n

                  7. Whisper \u2013 Speech Recognition by OpenAI<\/h4>\n\n\n\n

                  Whisper is one of the best open models for speech-to-text transcription.<\/p>\n\n\n\n

                  It supports multiple languages and works surprisingly well even on modest hardware.<\/p>\n\n\n\n

                  Local Run:<\/strong> Yes
                  Hardware:<\/strong><\/p>\n\n\n\n

                    \n
                  • Small models: CPU-friendly<\/li>\n\n\n\n
                  • Large model: 10GB+ VRAM recommended<\/li>\n<\/ul>\n\n\n\n
                    <\/div>\n\n\n
                    \n
                    \"Code<\/figure>\n<\/div>\n\n\n
                    <\/div>\n\n\n\n

                    8. Code Llama \u2013 Coding Specialist<\/h4>\n\n\n\n

                    Code Llama is fine-tuned specifically for programming tasks.<\/p>\n\n\n\n

                    It can generate, debug, and explain code across multiple languages.<\/p>\n\n\n\n

                    Local Run:<\/strong> Yes
                    Hardware:<\/strong><\/p>\n\n\n\n

                      \n
                    • 7B: 16GB RAM<\/li>\n\n\n\n
                    • 34B: 24GB+ VRAM recommended<\/li>\n<\/ul>\n\n\n\n
                      <\/div>\n\n\n
                      \n
                      \"Deepseek<\/figure>\n<\/div>\n\n\n
                      <\/div>\n\n\n\n

                      9. DeepSeek Coder \u2013 High Performance Coding Model<\/h4>\n\n\n\n

                      DeepSeek Coder is rapidly gaining popularity for outperforming many models in coding benchmarks.<\/p>\n\n\n\n

                      It\u2019s especially strong in Python and backend development.<\/p>\n\n\n\n

                      Local Run:<\/strong> Yes
                      Hardware:<\/strong><\/p>\n\n\n\n

                        \n
                      • 6B\u201333B variants<\/li>\n\n\n\n
                      • 16GB\u201332GB RAM or GPU equivalent<\/li>\n<\/ul>\n\n\n\n
                        <\/div>\n\n\n
                        \n
                        \"Meta<\/figure>\n<\/div>\n\n\n
                        <\/div>\n\n\n\n

                        10. Segment Anything Model (SAM) \u2013 Vision AI Breakthrough<\/h4>\n\n\n\n

                        Segment Anything Model can identify and segment objects in images with minimal input.<\/p>\n\n\n\n

                        It\u2019s widely used in computer vision pipelines.<\/p>\n\n\n\n

                        Local Run:<\/strong> Yes
                        Hardware:<\/strong><\/p>\n\n\n\n

                          \n
                        • 8GB\u201316GB VRAM recommended<\/li>\n\n\n\n
                        • CPU possible but slower<\/li>\n<\/ul>\n\n\n\n
                          <\/div>\n\n\n
                          \n
                          \"Stable<\/figure>\n<\/div>\n\n\n
                          <\/div>\n\n\n\n

                          11. Stable Video Diffusion \u2013 Open Video Generation<\/h4>\n\n\n\n

                          Stable Video Diffusion brings generative AI into video.<\/p>\n\n\n\n

                          Still early-stage, but extremely promising.<\/p>\n\n\n\n

                          Local Run:<\/strong> Yes (limited)
                          Hardware:<\/strong><\/p>\n\n\n\n

                          High-end GPU strongly recommended<\/p>\n\n\n\n

                          16GB\u201324GB VRAM minimum<\/p>\n\n\n\n

                          <\/div>\n\n\n\n
                          \n\n\n\n
                          <\/div>\n\n\n\n

                          Breakdown by Category<\/h3>\n\n\n\n

                          Code Models<\/h4>\n\n\n\n
                            \n
                          • Code Llama<\/li>\n\n\n\n
                          • Gemma<\/li>\n\n\n\n
                          • DeepSeek Coder<\/li>\n<\/ul>\n\n\n\n

                            Text \/ LLM Models<\/h4>\n\n\n\n
                              \n
                            • Gemma<\/li>\n\n\n\n
                            • LLaMA 3<\/li>\n\n\n\n
                            • Mixtral 8x7B<\/li>\n\n\n\n
                            • Falcon 180B<\/li>\n\n\n\n
                            • GPT-NeoX<\/li>\n<\/ul>\n\n\n\n

                              Image Models<\/h4>\n\n\n\n
                                \n
                              • Stable Diffusion XL<\/li>\n\n\n\n
                              • Segment Anything Model<\/li>\n<\/ul>\n\n\n\n

                                Audio Models<\/h4>\n\n\n\n
                                  \n
                                • Whisper<\/li>\n<\/ul>\n\n\n\n

                                  Video Models<\/h4>\n\n\n\n

                                  Stable Video Diffusion<\/p>\n\n\n\n

                                  <\/div>\n\n\n\n
                                  \n\n\n\n
                                  <\/div>\n\n\n\n

                                  Open Source AI Models \u2013 Comparison Table<\/h3>\n\n\n\n

                                  This table gives you a real-world deployment view<\/strong>\u2014not just theory, but what actually matters when choosing a model.<\/p>\n\n\n\n

                                  Model<\/strong><\/th>Type<\/strong><\/th>Strengths<\/strong><\/th>Local Friendly<\/strong><\/th>Minimum Hardware<\/strong><\/th>Recommended Hardware<\/strong><\/th><\/tr><\/thead>
                                  Gemma<\/td>Text \/ Multimodal<\/td>Lightweight, efficient, scalable<\/td>\u2705 Yes<\/td>4\u20138GB RAM (small)<\/td>24GB VRAM (large)<\/td><\/tr>
                                  LLaMA 3<\/td>Text<\/td>Best all-rounder, strong reasoning<\/td>\u2705 Yes<\/td>16GB RAM (8B)<\/td>24\u201348GB VRAM (70B)<\/td><\/tr>
                                  Mixtral 8x7B<\/td>Text<\/td>Efficient MoE, great performance<\/td>\u2705 Yes<\/td>16GB VRAM<\/td>24\u201332GB VRAM<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
                                  Falcon 180B<\/td>Text<\/td>Massive scale, strong benchmarks<\/td>\u26a0\ufe0f Limited<\/td>64GB+ RAM (quantized)<\/td>Multi-GPU cluster<\/td><\/tr>
                                  GPT-NeoX<\/td>Text<\/td>Research flexibility<\/td>\u2705 Yes<\/td>16GB RAM<\/td>24\u201348GB VRAM<\/td><\/tr>
                                  Stable Diffusion XL<\/td>Image<\/td>High-quality image generation<\/td>\u2705 Yes<\/td>8GB VRAM<\/td>12\u201316GB VRAM<\/td><\/tr>
                                  Whisper<\/td>Audio<\/td>Best open speech-to-text<\/td>\u2705 Yes<\/td>CPU \/ 8GB RAM<\/td>10GB+ VRAM<\/td><\/tr>
                                  Code Llama<\/td>Code<\/td>Strong coding assistant<\/td>\u2705 Yes<\/td>16GB RAM<\/td>24GB VRAM<\/td><\/tr>
                                  DeepSeek Coder<\/td>Code<\/td>Excellent coding benchmarks<\/td>\u2705 Yes<\/td>16GB RAM<\/td>32GB RAM \/ GPU<\/td><\/tr>
                                  Segment Anything Model<\/td>Vision<\/td>Advanced segmentation<\/td>\u2705 Yes<\/td>8GB VRAM<\/td>16GB VRAM<\/td><\/tr>
                                  Stable Video Diffusion<\/td>Video<\/td>Early-stage video AI<\/td>\u26a0\ufe0f Limited<\/td>16GB VRAM<\/td>24GB+ VRAM<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
                                  <\/div>\n\n\n\n
                                  \n\n\n\n
                                  <\/div>\n\n\n\n

                                  Best Open Source AI Models for Your Hardware<\/h3>\n\n\n\n

                                  This is where things get practical. Choosing the right model for your machine matters more than choosing the \u201cbest\u201d model overall.<\/p>\n\n\n\n

                                  <\/div>\n\n\n\n

                                  8GB RAM \/ No GPU (Entry-Level Systems)<\/h4>\n\n\n\n

                                  At this level, you\u2019re limited – but still capable.<\/p>\n\n\n\n

                                  Best Choices:<\/strong><\/p>\n\n\n\n

                                    \n
                                  • Gemma : Best lightweight local AI<\/li>\n\n\n\n
                                  • Whisper : (small models)<\/li>\n\n\n\n
                                  • Quantized : LLaMA 3 8B<\/li>\n\n\n\n
                                  • Small coding models (light DeepSeek Coder variants)<\/li>\n<\/ul>\n\n\n\n

                                    What to Expect:<\/strong><\/p>\n\n\n\n

                                      \n
                                    • Slower speeds (CPU-bound)<\/li>\n\n\n\n
                                    • Good for experimentation, not production<\/li>\n\n\n\n
                                    • ~5\u201310 tokens\/sec typical on CPU<\/li>\n<\/ul>\n\n\n\n
                                      <\/div>\n\n\n\n

                                      16GB RAM \/ Mid-Range GPU (RTX 3060 \/ 4060)<\/h4>\n\n\n\n

                                      This is the sweet spot for most developers<\/strong>.<\/p>\n\n\n\n

                                      Best Choices:<\/strong><\/p>\n\n\n\n

                                        \n
                                      • Gemma<\/li>\n\n\n\n
                                      • LLaMA 3 8B (runs smoothly locally)<\/li>\n\n\n\n
                                      • Stable Diffusion XL (entry-level)<\/li>\n\n\n\n
                                      • Code Llama 7B<\/li>\n\n\n\n
                                      • GPT-NeoX (smaller variants)<\/li>\n<\/ul>\n\n\n\n

                                        Why this tier matters:<\/strong><\/p>\n\n\n\n

                                          \n
                                        • 6\u20138GB VRAM can run quantized LLMs efficiently<\/li>\n\n\n\n
                                        • Ideal for:\n
                                            \n
                                          • Chatbots<\/li>\n\n\n\n
                                          • Local dev assistants<\/li>\n\n\n\n
                                          • Basic image generation<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n
                                            <\/div>\n\n\n\n

                                            24GB VRAM (RTX 4090 \/ A5000 Tier)<\/h4>\n\n\n\n

                                            This is where things start getting seriously powerful<\/strong>.<\/p>\n\n\n\n

                                            Best Choices:<\/strong><\/p>\n\n\n\n

                                              \n
                                            • Mixtral 8x7B<\/li>\n\n\n\n
                                            • Gemma<\/li>\n\n\n\n
                                            • DeepSeek Coder (larger variants)<\/li>\n\n\n\n
                                            • Stable Diffusion XL (full capability)<\/li>\n\n\n\n
                                            • Segment Anything Model<\/li>\n<\/ul>\n\n\n\n

                                              Performance Insight:<\/strong><\/p>\n\n\n\n

                                                \n
                                              • Mixtral typically needs 24\u201332GB VRAM for smooth inference<\/strong><\/li>\n\n\n\n
                                              • You can now:\n
                                                  \n
                                                • Run multiple models simultaneously<\/li>\n\n\n\n
                                                • Build real applications (not just demos)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n
                                                  <\/div>\n\n\n\n

                                                  48GB+ VRAM \/ Multi-GPU (Enterprise \/ Lab Setup)<\/h4>\n\n\n\n

                                                  This is serious infrastructure<\/strong>\u2014not typical desktop setups.<\/p>\n\n\n\n

                                                  Best Choices:<\/strong><\/p>\n\n\n\n

                                                    \n
                                                  • LLaMA 3 70B<\/li>\n\n\n\n
                                                  • Gemma<\/li>\n\n\n\n
                                                  • Falcon 180B<\/li>\n\n\n\n
                                                  • Stable Video Diffusion<\/li>\n<\/ul>\n\n\n\n

                                                    Reality Check:<\/strong><\/p>\n\n\n\n

                                                      \n
                                                    • LLaMA 70B can require ~140GB VRAM unoptimized<\/strong><\/li>\n\n\n\n
                                                    • Typically run via:\n
                                                        \n
                                                      • Multi-GPU clusters<\/li>\n\n\n\n
                                                      • Distributed inference<\/li>\n\n\n\n
                                                      • Cloud or hybrid setups<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n
                                                        <\/div>\n\n\n\n

                                                        Key Takeaways<\/h3>\n\n\n\n

                                                        Bigger models \u2260 always better<\/strong> (efficiency matters more)<\/p>\n\n\n\n

                                                        Quantization is everything<\/strong> \u2192 turns \u201cimpossible\u201d models into runnable ones<\/p>\n\n\n\n

                                                        8B – 13B models are the real sweet spot<\/strong> for local use<\/p>\n\n\n\n

                                                        24GB VRAM is the turning point<\/strong> from hobby \u2192 production<\/p>\n\n\n\n

                                                        <\/div>\n","protected":false},"excerpt":{"rendered":"

                                                        Open source AI has exploded over the last few years, putting serious power into the hands of developers, researchers, and businesses. What used to require access to massive\u2026<\/p>\n","protected":false},"author":1,"featured_media":826,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[17,9],"tags":[54,55],"class_list":["post-687","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-models","category-changelog","tag-opensource","tag-tech-specs"],"yoast_head":"\nTop 11 Open Source AI Models You Can Run Today - Vox Machina<\/title>\n<meta name=\"description\" content=\"Discover the top 10 open source AI models, compare performance, and learn what hardware you need to run them locally\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/voxmachina.co.za\/blog\/top-11-open-source-ai-models-you-can-run-today\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Top 11 Open Source AI Models You Can Run Today - 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