Best Local LLM

The best local LLM for most developers in 2026 depends on available memory and the job. A 7B to 14B quantized model runs on a 16 GB laptop and covers code completion and chat. Larger open-weight models need a workstation GPU or Apple silicon with 64 GB or more of unified memory. MCP tools for developers give a local model access to live APIs, so the model can act on real data instead of guessing.

Which local LLM to pick by hardware

Pick the largest model that fits in your memory with room left for context. A 4-bit quantized model uses roughly half a gigabyte of memory per billion parameters, plus overhead for the context window.

• 8 GB of memory: 3B to 4B models for simple completion and summaries.
• 16 GB of memory: 7B to 9B models for everyday coding help and chat.
• 24 GB of GPU memory: 14B to 32B models for multi-file refactors and longer reasoning.
• 64 GB or more of unified memory: 70B-class models for the hardest local work.

Check the model card for the exact quantization and context length before you download it.

Which runtime to run it with

Ollama and LM Studio both load GGUF models and expose a local HTTP endpoint. llama.cpp gives the most control over quantization and offloading. vLLM suits multi-user serving on a dedicated GPU host.

Choose the runtime that your editor or agent framework already supports. Most coding assistants accept a custom OpenAI-compatible base URL, so the local endpoint works as a drop-in.

Give a local model live data through MCP

A local model answers from its training data unless a tool supplies current information. MCP tools connect the model to APIs such as search, code hosting, and databases.

An MCP client sends the tool list to the model. The model picks a tool, the client calls it, and the result returns to the model as context. The model does not need internet access of its own. Tools with clear names and short parameter descriptions work better with smaller models.

• Keep the tool list short. Smaller models lose accuracy when many tools compete for attention.
• Pick tools with typed JSON inputs. Typed inputs reduce malformed calls from 7B-class models.
• Log each tool call during testing so you can see where the model chooses the wrong tool.

How to compare local models for your work

Run the same five or six real tasks against each candidate model. Score the output on correctness, not on fluency. Time the first token and the full response on your own hardware.

Public leaderboards measure general benchmarks. They do not measure how a model handles your codebase, your tool schemas, or your prompt style. Use them only to shortlist candidates.

Where this guide stops

This guide covers local inference and tool access. It does not rank specific model releases. New open-weight models ship often, so rankings change quickly. No public pricing applies to running an open-weight model on your own hardware. Hosted endpoints charge per token. Their rates vary by provider.

Live pricing — developer

Connect via MCP

$ curl -X POST https://apibase.pro/api/v1/tools/account.timeseries/call \
  -H "Content-Type: application/json" -d '{"params": {}}'

FAQ

What is the best local LLM for coding?

For most developers with 16 GB of memory, a 7B to 9B coder-tuned model gives the best balance of speed and accuracy. With 24 GB of GPU memory, a 14B to 32B coder model handles multi-file tasks better. Test two or three candidates on your own code before you commit.

Can a local LLM use MCP tools?

Yes. The MCP client sends tool definitions to the model, the model requests a tool call, and the client runs it and returns the result. The model needs to support function or tool calling in its chat template. Smaller models make more tool-call errors, so keep the tool list short.

How much memory does a local LLM need?

A 4-bit quantized model needs about half a gigabyte of memory per billion parameters, plus room for the context window. A 7B model fits in 8 GB with a short context. Longer contexts need more memory.

Is a local LLM cheaper than a hosted API?

It depends on usage volume and hardware cost. The weights have no public price, because you download and run them on your machine. Hosted API rates vary by provider, so compare your expected monthly token volume against your hardware cost.

Recommended next step

1. STEP 1/2 Connect your MCP client
2. STEP 2/2 Browse the full catalog

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Published: 2026-11-03Source: live apibase.pro catalog snapshot