Ollama Tutorial: The Simplest Way to Run DeepSeek and Other LLMs Locally

What Is Ollama? A One-Minute Overview of This Local LLM Powerhouse

Ollama is an open-source project built with Go that enables users to easily run various large language models (LLMs) on their own computers. Large language models are deep learning models based on the Transformer architecture, trained on massive text datasets, capable of text generation, code writing, logical reasoning, and more, with parameter scales ranging from billions to trillions. The project has earned over 170,000 Stars on GitHub with nearly 16,000 forks, making it the most mainstream local LLM runtime tool available today.

As of now, Ollama supports Kimi-K2.5, GLM-5, MiniMax, DeepSeek^Ad, gpt-oss, Qwen, Gemma, and many other models, covering virtually all popular open-source LLMs from around the world.

Why Choose Ollama? Three Core Advantages

Extremely Low Barrier to Entry

Ollama's design philosophy is "Get up and running" — making local LLM deployment as simple as installing regular software. You don't need to understand model quantization, CUDA configuration, or other low-level details. Just a few commands and you'll have a full large language model running.

It's worth explaining these two concepts here: Model quantization is the technique of converting large language models from high-precision floating-point numbers (such as FP32, FP16) to low-precision representations (such as INT8, INT4), with the goal of drastically reducing the GPU memory and RAM the model occupies, enabling models that would normally require hundreds of GB of VRAM to run on consumer-grade hardware. CUDA is NVIDIA's parallel computing platform and programming model, serving as the infrastructure for GPU-accelerated inference — in traditional deployment workflows, users need to manually install specific versions of the CUDA Toolkit and cuDNN libraries while ensuring compatibility with the model framework version, a process that often intimidates non-expert users. Ollama encapsulates all these complex low-level configurations internally, automatically detecting the hardware environment and selecting the optimal inference backend.

This out-of-the-box experience is the fundamental reason it has rapidly accumulated a massive user base.

Continuously Updated Model Library Covering Mainstream Choices

Ollama's supported model lineup keeps expanding:

• Chinese models: Kimi-K2.5 (Moonshot AI), GLM-5 (Zhipu AI), DeepSeek, Qwen (Tongyi Qianwen), MiniMax
• International models: Gemma (Google), gpt-oss, and more
• Plus hundreds of community-contributed open-source models

The open-source LLM landscape underwent fundamental changes in 2024-2025. DeepSeek achieved breakthroughs in inference efficiency with its MoE (Mixture of Experts) architecture — the core idea of MoE is to divide model parameters into multiple "expert" sub-networks, activating only a subset during each inference pass, thereby dramatically reducing actual computational overhead while maintaining an enormous total parameter count. Alibaba's Qwen series has iterated to Qwen3, with continuous improvements in multilingual capabilities and long-context processing; Moonshot AI's Kimi-K2.5 adopts a trillion-parameter MoE architecture; and Zhipu's GLM series excels in Chinese comprehension and tool calling. These models generally adopt Apache 2.0 or similar permissive open-source licenses allowing commercial use, significantly lowering enterprise adoption barriers.

Model weights are typically distributed in GGUF format, a model file format defined by the llama.cpp project and optimized specifically for CPU/GPU hybrid inference. Ollama internally uses llama.cpp as its core inference engine.

The latest models like Kimi-K2.5 and GLM-5 can be run through Ollama almost immediately after release — the team's adaptation speed is truly impressive.

Engineering Advantages from Go

Building with Go gives Ollama several practical benefits: it compiles to a single binary, making distribution and deployment extremely convenient; Go's concurrency model is naturally suited for handling multi-request scenarios in inference services; and its cross-compilation capabilities allow seamless use across macOS, Linux, and Windows.

Go (Golang) was released by Google in 2009, designed specifically for building high-performance network services and system tools. Its goroutine mechanism is a lightweight coroutine implementation — a single process can easily create hundreds of thousands of goroutines, each occupying only about 2KB of stack space. Combined with the channel communication mechanism, it's ideal for handling large volumes of concurrent inference requests — when multiple users or applications simultaneously send inference tasks to Ollama, Go's scheduler efficiently dispatches these requests across a limited number of OS threads. Go's static compilation means all dependencies are packaged into a single executable, eliminating the need for users to install runtime environments (unlike Python, which requires managing virtual environments and dependency packages), which is particularly important for scenarios requiring rapid deployment across different servers. Additionally, Go natively supports cross-compilation — developers only need to set the GOOS and GOARCH environment variables to generate binaries for different operating systems and CPU architectures.

Behind 170K Stars: Three Trends in Local AI Inference

Data Privacy Driving Explosive Demand for Local Inference

Ollama's growth curve reflects a clear signal: more and more developers and enterprises need to run LLMs locally. Core drivers include data privacy compliance, reducing API call costs, offline scenario support, and the pursuit of low-latency inference.

Globally, regulations like GDPR (EU General Data Protection Regulation), China's Data Security Law, and Personal Information Protection Law impose strict requirements on cross-border data transfers and third-party processing. When enterprises send sensitive data (such as customer information, internal documents, code repositories) to cloud AI APIs, they face data breach risks and compliance audit pressure. Local inference fundamentally eliminates the possibility of data leaving the enterprise network boundary. For heavily regulated industries like finance, healthcare, and law, data sovereignty has become the primary consideration when choosing AI deployment methods. From a cost perspective, frequent cloud API calls can far exceed the amortized cost of locally deploying a server with high-end GPUs, especially in production environments with high inference volumes.

Open-Source LLMs Entering a Flourishing Era

In 2024-2025, open-source models from Chinese teams like DeepSeek, Qwen, Kimi, and GLM have seen their influence continuously rise in the global community. As the bridge connecting models to end users, Ollama naturally benefits from this rising tide.

Behind this trend is a paradigm shift in the open-source AI community: in the past, the most powerful models were almost exclusively closed-source (like GPT-4, Claude), with a clear capability gap between open and closed-source models. But with improved training data quality, training methodology innovations (such as the widespread adoption of alignment techniques like RLHF and DPO), and increased compute investment, open-source models have approached or even surpassed some closed-source models on multiple benchmarks. This means models running locally through Ollama now have sufficient capabilities to cover the vast majority of practical application scenarios.

The Tool Ecosystem Around Ollama Has Matured

Ollama is no longer just a model runner. A complete local AI development ecosystem has formed around it: Open WebUI provides a graphical interaction interface, multi-language SDKs for Python/JS/Go facilitate integration development, and connections with mainstream frameworks like LangChain and LlamaIndex are seamless.

Open WebUI (formerly Ollama WebUI) is a feature-rich web frontend that provides a ChatGPT-like conversation interface, supporting multi-model switching, conversation history management, RAG document uploads, and more, making local models accessible to users unfamiliar with the command line. LangChain is currently the most popular LLM application development framework, providing abstraction layers for Chain calls, Agents, and Memory management. Its OllamaLLM integration allows developers to connect local models to complex AI workflows with just a few lines of code — for example, building an intelligent assistant that can automatically search the web, call tools, and generate reports. LlamaIndex focuses on data indexing and Retrieval-Augmented Generation (RAG), helping users combine private knowledge bases with local LLMs to achieve intelligent Q&A based on enterprise internal data, addressing LLM "hallucination" and knowledge timeliness issues. The maturity of these tools means Ollama has evolved from a simple model runner into a complete local AI development platform.

Who Is Ollama For? Typical Use Cases

• Individual developers: Experience and compare various open-source models at zero cost without relying on cloud resources. A simple ollama run command downloads and launches a model, and you can integrate models into your own applications through a local API interface compatible with the OpenAI format.
• Enterprise intranet deployment: Keep data within your domain while meeting compliance requirements and leveraging LLM capabilities. Enterprises can deploy Ollama on intranet servers, paired with Open WebUI to provide privatized AI assistant services for all employees, with all conversation data retained entirely on the enterprise's own infrastructure.
• AI application prototyping: Rapidly iterate locally, eliminating the wait times and costs of API calls. Developers can locally test different models' performance, adjust prompt strategies, validate RAG pipelines, and then decide whether to migrate to a cloud production environment once the solution matures.
• Education and research: Study architectural characteristics and output differences of various models with a low barrier to entry. Researchers can conveniently compare model performance across different parameter scales and quantization precisions, observing behavioral patterns on specific tasks.

If you're just getting started with local LLMs, Ollama is currently the most recommended entry-level tool. Its clean command-line interface, comprehensive official documentation, and active community discussions help reduce the deployment barrier to a minimum.

Conclusion

With 170K Stars, Ollama has become the de facto standard for local AI infrastructure. The direction it represents is clear — enabling everyone to run powerful large language models on their own devices, free from network limitations and data breach concerns. As high-quality open-source models like DeepSeek and Qwen continue to iterate, Ollama's practical value will only increase.

From a broader perspective, Ollama's success reflects the larger trend of AI democratization: AI capabilities are transforming from exclusive resources of a few cloud providers into tools readily accessible on every developer's desktop. When running a hundred-billion-parameter model becomes as simple as installing an application, the barrier to AI innovation will be fundamentally redefined.