Local AI questions answered clearly.

Terms such as local AI, local model, local LLM, local machine learning model, localized AI, and your local model are often used loosely. The important question is where inference happens. If the model processes the prompt on your laptop, desktop, or private server, it is local.

LocalLlama often means the community around running Llama and other open models locally. It is not one model or one engine.

Local AI is useful for private files, repeated work, unreliable internet, and systems that must keep working offline.

Cloud AI is often easier for the hardest reasoning, live web information, and large image or video jobs. A good system may use local AI for private routine work and cloud AI only when the task needs it.

The engine may be Ollama, llama.cpp, LM Studio, LocalAI, or Foundry Local. The model may be Qwen, Llama, Gemma, DeepSeek, or another supported family.

For business use, also plan user permissions, encrypted storage, backups, logs, updates, and a repeatable test set.

Storage holds the downloaded model. RAM and VRAM hold the model, context, and temporary data while it runs.

A supported GPU can improve speed, but local AI can run on a CPU without dedicated VRAM. Leave memory for Windows, macOS, Linux, and other applications.

A small 1B to 3B quantized model may use about 1 to 3 GB of storage. Many 7B to 8B Q4 files use about 4 to 6 GB. A 32B Q4 file is often around 20 GB.

File size is not the full memory requirement. Context, cache, engine overhead, and other applications also need memory. Check the exact artifact, not only the family name.

Examples worth testing include Qwen3 4B or 8B, Llama 3.2 3B, and other current 7B to 8B GGUF models supported by your engine.

Eight GB VRAM does not mean every 8B model will fit at every context length. Driver support, cache size, engine, and display memory use matter.

On an 8 GB computer, start around 1B to 3B. On a 16 GB computer, start around 3B to 8B. These are conservative starting ranges, not speed guarantees.

Use a short context, close heavy applications, and test a real task before downloading a larger model.

Do not choose from the GPU name alone. Record the exact GPU, dedicated VRAM, operating system, RAM, and engine.

LocalLens gives a conservative shortlist and shows when the information is not enough for a safe recommendation.

Eight GB RAM is an entry point for very small models. Sixteen GB supports a wider small-model range. Thirty-two GB gives more room for 14B and some larger quantized models.

For a purchase, start with the task first. Coding agents, long documents, multiple users, and vision can require much more memory than simple chat.

Use the exact installer page for the current application requirement. After installation, run ollama list to see the models and their stored sizes.

A 3B model may take a few gigabytes. DeepSeek R1 32B is about 20 GB in Ollama. Keep extra storage for updates and more than one model.

There is no useful one-number requirement for every Ollama model. A small model may work on modest hardware while a 32B or 70B model may need tens of gigabytes of memory.

For NVIDIA acceleration, confirm that the GPU and driver are supported by the current Ollama hardware documentation. If GPU support fails, Ollama may use the CPU.

Run ollama show MODEL_NAME to inspect the model and ollama list to see local file sizes.

Do not treat the advertised maximum context as a promise that it will fit on minimum hardware. Longer context uses more memory.

To start it after the download, run ollama run deepseek-r1:32b.

LocalLens uses 32 GB RAM as a cautious minimum planning point and prefers more headroom. That is a planning estimate, not an official speed guarantee.

A GPU is optional. To place most or all of the model on a GPU, you need roughly model-size VRAM plus working headroom. Otherwise the engine can use system RAM and CPU, or split work between CPU and GPU.

This 32B model is a distilled Qwen-based reasoning model. It is much heavier than the current default 8B DeepSeek R1 tag.

For Ollama, a model page provides the exact command. For a GGUF file in llama.cpp or LM Studio, match the model, instruction format, and quantization.

Qwen Coder, Qween Coder, Qwen QCoder, and Qwen coder are often search variations. Always confirm the official model name before downloading.

Qwen3 Coder 30B is a mixture-of-experts model with about 30.5B total parameters and about 3.3B active parameters. Low active parameters reduce compute, but the full model weights still need memory.

The 480B local version is a different class. Ollama states that it requires at least 250 GB of memory or unified memory.

In Ollama, Qwen3 0.6B is about 523 MB, 4B is about 2.5 GB, 8B is about 5.2 GB, 14B is about 9.3 GB, and 32B is about 20 GB.

Start with the exact tag and quantization. A 16 GB computer can reasonably test Qwen3 4B or 8B, while larger models need more memory and may be slow without strong acceleration.

These searches may refer to a mistaken version number, a community model name, or a later release that needs a fresh source check.

Do not download a file only because its name resembles Qwen. Verify the publisher, model card, license, file hash, architecture, and official announcement.

DeepSeek R1 distill models and thinking-capable Qwen models are examples. They can help with math, code, planning, and structured analysis.

Reasoning output can be long and still be wrong. Use a test set with known answers before trusting the model.

The term may refer to an application, project, or community package that can connect to a local engine.

Find the exact product page and identify the actual model and engine underneath it before deciding what hardware it needs.

Examples include Ollama, llama.cpp, LM Studio, LocalAI, and Foundry Local. The engine is not the model.

Choose by hardware support, model format, ease of use, API needs, tuning controls, and the applications you want to connect.

Ollama is usually easier for one computer and a small number of models.

LocalAI can suit a server deployment with broader configuration needs, but it requires more operational work. Compare the exact features and backends you will use.

Foundry Local supports Windows, Apple Silicon macOS, and Linux, selects an execution provider for the device, and provides OpenAI-compatible requests. It does not require an Azure subscription for local use.

The best choice depends on model availability, target devices, programming language, deployment control, and support needs.

Prompts can stay on the device while model downloads, web search, plugins, analytics, cloud storage, or remote APIs still use the internet.

Use trusted model sources, update the engine, encrypt the computer, restrict network access, scan uploaded files, and keep important human review.

Use a local engine, local model, local document search, and local storage. Disable cloud models, web search, remote plugins, telemetry you do not need, and public network exposure.

Use a firewall, user permissions, encryption, retention rules, and network monitoring. Test the system without sensitive information before approving real data.

Do not expose an unauthenticated Ollama, llama.cpp, or other model endpoint directly to the public internet.

Place authentication and authorization in front of the service, encrypt traffic, separate user permissions, patch the host, record important actions, and back up safely.

Internet access is still used for model search, downloads, and software updates. Cloud models, web search, and other cloud features can process data outside the device.

Check the current privacy policy and the features you enable before using confidential information.

EmbeddingGemma is a small multilingual model designed for on-device search and retrieval. Nomic Embed Text is another established option, while Nomic Embed Text v2 MoE targets multilingual retrieval.

An embedding model does not chat. It converts text into vectors for semantic search, clustering, and retrieval-augmented generation. The best choice depends on language, document length, storage, speed, and your test questions.