Question 1

What LLMs can I run locally?

Accepted Answer

You can run most open-source large language models (LLMs) locally. The database currently supports benchmarking for models like Llama 3 (Meta), Qwen 2.5 (Alibaba), Phi 3 (Microsoft), Mistral, and DeepSeek-Coder. Any model that has weights published in formats like GGUF, GPTQ, or safetensors can run locally.

Question 2

How do I run AI models locally to keep my data 100% private?

Accepted Answer

By downloading and running models on your own hard drive using local software like Ollama or LM Studio, your prompts and files never leave your computer. There are no cloud APIs or external servers involved, guaranteeing absolute data privacy and security.

Question 3

Is it free to run AI models locally on my own computer?

Accepted Answer

Yes! Running models locally is entirely free. You do not pay OpenAI API fees, subscription limits, or SaaS subscriptions. The only cost is the electricity your hardware uses during execution.

Question 4

Can I run local LLMs without an internet connection?

Accepted Answer

Absolutely. Once you have downloaded the model weights (e.g. via Ollama or Hug Face), you can disconnect from the internet completely. Local LLMs run offline, making them ideal for secure networks, travel, or remote locations.

Question 5

Why does my local LLM run so slow (1-2 tokens per second)?

Accepted Answer

This happens when your GPU VRAM is full and the model offloads remaining layers to your slower System RAM. Running models on system RAM/CPU is bottlenecked by motherboard memory bandwidth. To speed it up, try running a smaller model (e.g., 8B instead of 14B) or using a tighter quantization format (e.g. Q4 instead of FP16) to fit entirely in VRAM.

Question 6

Why does my local model crash with Out-of-Memory (OOM) errors during long chats?

Accepted Answer

Model memory consumption isn't static. As you chat, the model stores conversation context in the KV Cache. For long chats or large files, this KV Cache expands and pushes your memory past hardware limits. Our calculator factorizes this dynamic KV Cache load to help you prevent these OOM crashes.

Question 7

Is an Apple Silicon Mac (M1/M2/M3/M4) good for running local LLMs?

Accepted Answer

Yes! Apple Silicon Macs use Unified Memory, meaning the graphics engine and main system share the same high-speed memory pool. This allows a Mac with 64GB or 128GB of RAM to fit huge 70B models that would normally require multiple expensive NVidia graphics cards, though raw tokens-per-second processing speeds may be slightly slower than high-end discrete GPUs.

Question 8

Can I run a 70B parameter model on standard consumer hardware?

Accepted Answer

A 70B parameter model running at Q4 precision requires about 40 GB of memory. To run it, you would need dual RTX 3090/4090 GPUs (2x24GB) or a Mac with 64GB+ of Unified Memory. If offloaded to standard System RAM, it will run extremely slow.

Question 9

How much intelligence/quality do I lose by running a quantized local model (Q4 vs Q8)?

Accepted Answer

Quantization reduces the precision of model weights, but the intelligence loss is surprisingly small. A Q8 format is nearly indistinguishable from full precision. A Q4 format drops benchmark scores by only 1-2%, while reducing the model size by over 70%, making it the recommended sweet spot for local hardware.

Question 10

How do I connect local LLMs directly to my IDE (like VS Code or Cursor)?

Accepted Answer

You can run Ollama in the background (which hosts a local API endpoint on port 11434). Then, configure IDE extensions like Continue.dev, Llama Coder, or Cursor settings to use local OpenAI-compatible endpoints pointing to your local Ollama port.

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