Local models

Extracted Attributes

• Model Types

  Large Language Models (LLMs), Embedding models, and Quantized models

• Core Benefits

  Data privacy, offline capability, zero API costs, and decentralization

• Operating Systems

  Windows, macOS, Linux

• Strategic Framework

  Hybrid AI

• Hardware Requirements

  GPUs, NPUs, and 16GB-32GB+ RAM

Timeline

• Microsoft CEO Satya Nadella promotes Hybrid AI and local models running on Windows during a fireside chat at Davos. (Source: Document 4e50eb82-56c2-4d20-910f-9a43912c1cd7)

  2024-01-15

• Industry analysis highlights that local LLMs have gained dependable support for tool calling and are becoming a mainstream alternative to cloud APIs. (Source: Web Search Result: Why You Should Use Local Models - Medium)

  2025-05-30

Local regression

Local regression or local polynomial regression, also known as moving regression, is a generalization of the moving average and polynomial regression. Its most common methods, initially developed for scatterplot smoothing, are LOESS (locally estimated scatterplot smoothing) and LOWESS (locally weighted scatterplot smoothing), both pronounced LOH-ess. They are two strongly related non-parametric regression methods that combine multiple regression models in a k-nearest-neighbor-based meta-model. In some fields, LOESS is known and commonly referred to as Savitzky–Golay filter (proposed 15 years before LOESS). LOESS and LOWESS thus build on "classical" methods, such as linear and nonlinear least squares regression. They address situations in which the classical procedures do not perform well or cannot be effectively applied without undue labor. LOESS combines much of the simplicity of linear least squares regression with the flexibility of nonlinear regression. It does this by fitting simple models to localized subsets of the data to build up a function that describes the deterministic part of the variation in the data, point by point. In fact, one of the chief attractions of this method is that the data analyst is not required to specify a global function of any form to fit a model to the data, only to fit segments of the data. The trade-off for these features is increased computation. Because it is so computationally intensive, LOESS would have been practically impossible to use in the era when least squares regression was being developed. Most other modern methods for process modelling are similar to LOESS in this respect. These methods have been consciously designed to use our current computational ability to the fullest possible advantage to achieve goals not easily achieved by traditional approaches. A smooth curve through a set of data points obtained with this statistical technique is called a loess curve, particularly when each smoothed value is given by a weighted quadratic least squares regression over the span of values of the y-axis scattergram criterion variable. When each smoothed value is given by a weighted linear least squares regression over the span, this is known as a lowess curve. However, some authorities treat lowess and loess as synonyms.

Web Search Results

• Why You Should Use Local Models - Medium

  ## Why Use Local Models? There are many reasons to prefer local models: [...] Sitemap Open in app Sign in Search Sign in # Why You Should Use Local Models Rod Johnson 3 min read · May 30, 2025 -- When building Gen AI applications, it’s natural to default to familiar models in the cloud, from familiar providers. This is a mistake. It’s important to understand available model choices and their strengths and weaknesses. Mature Gen AI applications should use a mix of models for different things. Local LLMs are now very useful for many tasks, and their capabilities are growing faster than those of large closed models. Many local LLMs now offer dependable support for tool calling, which was not the case only a few months ago. Local embedding models are often the best choice, period. These facts have important implications for building applications. [...] ## Why Not Use Local Models? For some tasks, local models just aren’t an option. Unless you have very special hardware, the best models you can run locally at present may not match even a smallish cloud model like GPT-4.1 nano for handling complex prompts. But different models are good at different things. Some local models are much closer to parity to the big closed models at certain tasks. You should never stay away from local models because of perceived difficulty in mixing them into your applications. > It should be easy to change your code or configuration to use local models. And there are a variety of choices for running them. So if it’s too hard for you to try local models in your architecture, reconsider that architecture. ## How to Get The Most From Local Models

• How to Quickly Find the Best Local Model that Suits Your ...

  ## Pre-History When I initially attempted to find local models for local inference, I turned to HuggingFace and commenced my search. After numerous searches, I found myself on the HuggingFace Leaderboard. However, I soon realized that it didn’t provide an efficient way to search through all the available models on HuggingFace. The Leaderboard only lists approximately 1200 models, each with an HF score. However, there are so many more, like quantized models or recently released fine-tuned models, that aren’t listed on the Leaderboard, yet better fit my needs. [...] You’ll find a list of alternatives on the right-hand side, each has the same model architecture and similar parameters, but with different scores. If the core color is green, it signifies that the particular model outperforms the currently selected one. Let’s proceed by clicking on the first option in the list. It’s the best one in the group. This way you can select the better model satisfying the same criteria you’re looking for. ## Use Case #2: Seeking a model that’s compatible with Llama, fits 16GB MacOS RAM, boasts a large context window, and exhibits excellent Hugging Face Score [...] Now, let’s delve deeper into the search process. This time, my goal is to find the optimal commercial use model that can efficiently operate on my 16GB MacOS RAM and is compatible with the llama.cpp library (you can refer to my other article for the inference setup). So what do I consider “the best” model in this context? It’s the one with the highest score on the HF Leaderboard, the minimum VRAM required for inference (less than 16GB), and the maximum context length. You can begin your search here: First, I’ll hide all the columns that I won’t be using. Next, I will filter the models based on their architecture type, retaining only those that are compatible with Llama.

• Local Models Overview - Cline Docs

  Running Models Locally with Cline Quick Start Hardware Requirements Recommended Models Primary Recommendation: Qwen3 Coder 30B Why Not Smaller Models? Runtime Options LM Studio Ollama Critical Configuration Required Settings Understanding Quantization Model Formats Performance Expectations What’s Normal Performance Tips Use Case Comparison When to Use Local Models When to Use Cloud Models Troubleshooting Common Issues & Solutions Performance Optimization Advanced Configuration Multi-GPU Setup Custom Models Community & Support Next Steps Summary Model & Provider Configuration Running Models Locally # Local Models Overview ## Running Models Locally with Cline [...] ## Running Models Locally with Cline Run Cline completely offline with genuinely capable models on your own hardware. No API costs, no data leaving your machine, no internet dependency. Local models have reached a turning point where they’re now practical for real development work. This guide covers everything you need to know about running Cline with local models. ## ​ Quick Start 1. Check your hardware - 32GB+ RAM minimum 2. Choose your runtime - LM Studio or Ollama 3. Download Qwen3 Coder 30B - The recommended model 4. Configure settings - Enable compact prompts, set max context 5. Start coding - Completely offline ## ​ Hardware Requirements Your RAM determines which models you can run effectively: [...] ## ​ Summary Local models with Cline are now genuinely practical. While they won’t match top-tier cloud APIs in speed, they offer complete privacy, zero costs, and offline capability. With proper configuration and the right hardware, Qwen3 Coder 30B can handle most coding tasks effectively. The key is proper setup: adequate RAM, correct configuration, and realistic expectations. Follow this guide, and you’ll have a capable coding assistant running entirely on your hardware. Was this page helpful? CLI Profile (SSO)Ollama

• Local AI Models: Your Guide to Running Them Smoothly

  ### Local AI as a Pillar of Decentralization Running AI models locally embodies decentralization because: Runs locally without a constant internet connection. Encourages use of open source models over closed APIs. Promotes fully customizable AI tools where you control the model files, advanced configurations, and output. ### Real-World Trends Open source models like LLaMa and Falcon are gaining traction. LM Studio communities already count tens of thousands of developers. Startups are integrating AI strategies and local AI into existing applications to reduce bandwidth costs and dependency on cloud services. [...] ### Key Takeaways Running AI models locally ensures complete privacy by keeping sensitive data off third-party servers. Tools like Ollama, LM Studio, and LLaMa.cpp simplify the process of running local LLMs. You can fine-tune open source models to meet specific tasks in industries like finance, healthcare, or education. Decentralization of AI services empowers developers to innovate without depending on cloud services. ### What You Can Do Next? 1. Download a tool like Ollama or LM Studio. 2. Experiment with different models — start with smaller models before moving to large language models. 3. Try semantic search or language understanding tasks with your own datasets. 4. Share your experiences with the growing community to help advance local AI technology. [...] The decentralization of AI services isn’t just a technical shift — it’s a cultural one. It signals a move toward democratized AI solutions, where developers, businesses, and even individuals have the ability to run their own local models and create specific tasks without gatekeepers. ## Conclusion and Next Steps Local AI models are no longer a niche experiment; they are quickly becoming a mainstream way to leverage artificial intelligence. Whether you’re a developer seeking software development freedom, a researcher worried about privacy concerns, or a business aiming to cut costs, the ability to run models locally provides unmatched advantages. ### Key Takeaways

• Local LLMs Explained: Benefits, Internet Access & Uses - LocalXpose

  ## What are the best local LLMs? Several impressive local LLMs have emerged, each with its own strengths. Here are some of the top contenders: 1. Mistral: Known for its efficiency and strong performance across various tasks, Mistral models offer a great balance of size and capability. 2. Llama: Meta’s open-source LLM has gained significant traction due to its versatility and the ability to run on consumer hardware. 3. Gemma: Google’s latest offering in the local LLM space, Gemma models are designed to be lightweight yet powerful. 4. Phi: Microsoft’s Phi series focuses on smaller, more efficient models that can still handle complex tasks effectively. Each of these models offers unique advantages, and the best choice often depends on specific use cases and hardware constraints.