What is a local LLM?
A large language model (LLM) is a neural network trained on text data that can generate text, answer questions, write code, and perform other language tasks. Popular models include Meta’s Llama, Alibaba’s Qwen, DeepSeek, Mistral, and Google’s Gemma. A local LLM (sometimes written as locallm without a space) is simply one of these models running on your own machine instead of being accessed through a cloud provider’s API. The key differences:Why run an LLM locally?
Sovereignty
Your data never leaves your network. For teams working with sensitive information — medical records, legal documents, proprietary code, financial data — local inference eliminates the risk of data exposure to third-party APIs. There’s no prompt logging on someone else’s server, no training on your conversations, no compliance audit trail to manage.Speed
Local inference eliminates the network round-trip to a cloud API. For latency-sensitive applications — real-time code completion, interactive chat, streaming responses — local models respond in milliseconds instead of the hundreds of milliseconds (or seconds) that cloud APIs can take during peak load.Control
No rate limits imposed by a provider. No model deprecation surprises. No policy changes that restrict what you can generate. No API pricing increases. Your model runs when you want it to, how you want it to, for as long as you want.Customization
Fine-tune models on your own data for your specific domain. Train a model on your company’s codebase, your industry’s terminology, your team’s communication style. Cloud APIs don’t offer this — or they charge a premium for custom fine-tuning endpoints.Cost at scale
For high-volume use cases, local inference can be cheaper than paying per-token to a cloud API. The breakeven point depends on your hardware, electricity costs, and usage volume — but for teams sending thousands of requests per day, a one-time hardware investment pays for itself quickly.What you need to run a local LLM
Hardware requirements
The primary constraint is VRAM (video RAM on your GPU). Larger models need more VRAM:
You can also run models on CPU only (without a GPU), but inference will be significantly slower — usable for testing and light workloads, not for production or interactive use.
For detailed hardware guidance, see Hardware reference and Model fit calculator.
Model selection
Open-weight models are available in a range of sizes and specializations:- General purpose: Llama 3.1 (8B, 70B, 405B), Qwen2.5 (7B, 14B, 32B, 72B), Mistral 7B
- Code: Qwen2.5-Coder (7B, 32B), CodeLlama (7B, 13B, 34B), DeepSeek-Coder
- Reasoning: DeepSeek-R1, QwQ-32B
- Vision: Qwen2.5-VL, Llama 3.2 Vision
- Small and fast: Gemma 2 (2B, 9B), Phi-3 (3.8B, 14B), Qwen2.5-1.5B
Inference engine
You need software to load the model and serve it. The major options:
For detailed comparisons, see Supported backends and the per-runtime guides in the Local LLM Playbook.
Getting started: from zero to local LLM in 30 minutes
The fastest path is Ollama on a machine with a GPU.1
Install Ollama
2
Pull a model
Start with a 7B model if you have 8 GB VRAM, or a 14B model if you have 16 GB:or
3
Verify the model responds
4
Expose it through an API (optional, for team access)
If you want your team or external apps to access the model, install the Tokios connector:Get your claim code
Linux
TKS-XXXX-XXXX from the Tokios dashboard. The connector creates an outbound tunnel so your team can call https://api.tokios.com/v1 with an API key — no port forwarding, no Tailscale, no firewall changes.See Team access to local models for the full walkthrough.Common use cases
Personal AI assistant
Run a model on your laptop or desktop for private, unlimited use. No API costs, no rate limits, no data leaving your machine. Use it for writing, brainstorming, coding, research, or any task where you’d use ChatGPT but want full privacy.Team access to shared models
Run a powerful model on a shared server (homelab GPU box, workstation, DGX Spark) and give your team API access. Each teammate gets their own API key scoped to specific models. See Team access to local models for the full setup.Development and testing
Test your application against a local model before deploying to production. Use the same OpenAI-compatible API interface, but with a local backend that you control. No API costs during development, no rate limits during testing.Fine-tuning and custom models
Fine-tune a model on your own data for your specific domain. Train a model on your company’s codebase, your industry’s terminology, your team’s communication style. Deploy the fine-tuned model locally and access it through the same API.Air-gapped and regulated environments
Run models in environments with no internet access or strict data residency requirements. Government, healthcare, finance, and defense organizations often need local inference because cloud APIs are not permitted.Edge and IoT deployments
Run smaller models on edge devices — Raspberry Pi, Jetson, industrial controllers — for local inference without cloud connectivity. Useful for manufacturing, agriculture, field research, and other remote scenarios.Challenges and solutions
Hardware limitations
Problem: You don’t have enough VRAM for the model you want. Solutions:- Use a smaller model (7B instead of 70B)
- Quantize the model (4-bit instead of full precision) — see Quantization guide
- Use CPU inference with llama.cpp (slower but works)
- Split the model across multiple GPUs — see Model sharding
Networking complexity
Problem: Your team needs remote access, but you can’t open inbound ports or configure Tailscale for everyone. Solution: Use Tokios — the connector dials out through an outbound-only tunnel, so no inbound ports are needed. Works behind CGNAT, corporate firewalls, and NAT. See Team access to local models and Serve a local LLM from behind CGNAT.API compatibility
Problem: Your application uses the OpenAI SDK, but you want to call a local model. Solution: Most local inference engines (Ollama, vLLM, llama.cpp, LM Studio) expose an OpenAI-compatible/v1 API. Point your client at http://127.0.0.1:PORT/v1 and it works. If you’re using Tokios, the endpoint is https://api.tokios.com/v1 — same interface, remote access.
Team access and authentication
Problem: Your local model has no authentication. Anyone on the network can call it. Solution: Use Tokios to add per-user API keys with model scoping. Each teammate gets their own key, and you can revoke access for one person without disrupting everyone else. See Add API keys to local models.Model selection paralysis
Problem: There are hundreds of models. Which one should you use? Solution: Start with Qwen2.5-7B (general purpose, 8 GB VRAM) or Qwen2.5-Coder-7B (code tasks, 8 GB VRAM). Upgrade to larger models as you understand your requirements. See Choose a local model and Models by task.Next steps
- Choose a local model — how to pick the right model for your use case
- Hardware reference — detailed hardware requirements and recommendations
- Team access to local models — give your team API access to your local model
- Local LLM recipes — copy-paste configurations for common setups
- Supported backends — Ollama, vLLM, llama.cpp, LM Studio, and more
FAQ
Is a local LLM as good as ChatGPT?
Is a local LLM as good as ChatGPT?
For many tasks, yes. Open-weight models like Llama 3.1-70B, Qwen2.5-72B, and DeepSeek-R1 match or exceed GPT-4 on many benchmarks. For general chat, code generation, and reasoning, a well-chosen local model delivers comparable quality. The gap is narrowing rapidly — see What are evals? for how models are compared.
Can I use a local LLM without a GPU?
Can I use a local LLM without a GPU?
Yes, but it will be slow. CPU inference works with llama.cpp and Ollama, but expect 5–10× slower performance compared to GPU inference. Usable for testing and light workloads, not for production or interactive use. A GPU with 8 GB VRAM is the practical minimum for a good experience.
How much does it cost to run a local LLM?
How much does it cost to run a local LLM?
The model itself is free (open-weight). Your costs are hardware (GPU, RAM, storage) and electricity. A workstation with an RTX 4090 costs ~$2,000–$3,000. Electricity for 24/7 inference is ~$50–$100/month depending on your local rate. For high-volume use cases, this is cheaper than paying per-token to a cloud API.
Can I fine-tune a local LLM on my own data?
Can I fine-tune a local LLM on my own data?
Yes. Tools like Unsloth, Axolotl, and LLaMA-Factory let you fine-tune open-weight models on your own data. You need a GPU with enough VRAM to load the model plus training data (typically 24 GB for 7B models, 48+ GB for 13B models). See Serve your fine-tune for deployment guidance.
What's the difference between a local LLM and an API like OpenAI?
What's the difference between a local LLM and an API like OpenAI?
A local LLM runs on your hardware — you control the model, the data, and the infrastructure. An API like OpenAI runs on their servers — you send data over the internet, they run inference, and you pay per token. Local gives you sovereignty, speed, and control. Cloud gives you convenience and unlimited scale (at a cost).
Can my team access my local LLM remotely?
Can my team access my local LLM remotely?
Yes. Use Tokios to create an outbound tunnel from your local model to
https://api.tokios.com/v1. Your team calls that endpoint with their API key — no port forwarding, no Tailscale, no firewall changes. See Team access to local models for the full setup.What is locallm? Is it different from local LLM?
What is locallm? Is it different from local LLM?
No difference — “locallm” is just “local LLM” written as a single word. You’ll see both spellings in searches, community discussions (like r/LocalLLaMA), and project names. Some tools and libraries use “locallm” as a package name (e.g., the Python
locallm package), but they all refer to the same concept: running a large language model on your own hardware instead of a cloud API.