Run AI on your own hardware.
Zero cloud inference fees.
BotConnector Local AI matches open models, quantization levels, and runtimes to the exact specifications of your computer. Keep your data on-device with loopback execution while maintaining a unified workflow with cloud models.
Match models to your workstation capacity
Running local AI requires balancing model parameter size, quantization (bit depth), and available RAM/VRAM. Select your machine profile below to see optimal recommendations.
Entry Laptops & Thin Clients
Recommended for laptops with 8 GB unified memory or older workstations.
- ✓ Immediate token generation (40–70 tokens/sec)
- ✓ Zero risk of system memory paging/freezing
- ✓ Ideal for rapid drafting, inline code completion, and quick summaries
Standard Engineering Workstations
Apple Silicon M-Series (16GB) or PC with dedicated 6GB–8GB VRAM / 16GB RAM.
- ✓ Multi-file repository refactoring & logic analysis
- ✓ Comfortable 16k–32k context window budget
- ✓ Deep reasoning and complex tool-use support
High-Performance Workstations & GPUs
Machines with 32 GB+ RAM or NVIDIA RTX 3080/4070/4090 (12GB–24GB VRAM).
- ✓ Frontier-level intelligence running locally offline
- ✓ GPU tensor acceleration via CUDA, Metal, or Vulkan
- ✓ Full multi-agent autonomous engineering sessions
Strict Local Execution Boundary
When you select Local AI, BotConnector interacts with your local runtime over standard loopback network addresses. Your code, documents, and prompts never touch cloud infrastructure.
Zero Data Egress
Inference runs on loopback addresses (127.0.0.1:11434 for Ollama or 127.0.0.1:1234 for LM Studio). Model weights, memory state, and prompt buffers reside exclusively in your system RAM/VRAM.
No Cloud Subscription or Hidden Invoicing
Local inference runs entirely on the electricity and hardware you already own. You can execute millions of tokens offline without paying inference fees or worrying about credit exhaustion.
Seamless Hybrid Switch
Switch between a local Qwen coder model for private proprietary code and a cloud DeepSeek or GLM model for massive context tasks—all inside the same BCCLI terminal session or Web App conversation.
Three steps to running Local AI with BotConnector
Start Your Local Runtime
Install and launch Ollama or LM Studio on your machine.
ollama run qwen2.5-coder:14bDetect in BCCLI
BCCLI automatically detects Ollama running on loopback 127.0.0.1:11434.
bccli modelsExecute Your Workflow
Start an interactive coding session or run a one-off command with your local model.
bccli -m local/qwen2.5-coder:14b