Executing code assistance models locally provides total data privacy and zero monthly API costs. However, getting reliable performance requires balancing model parameter counts against available system memory and memory bandwidth.
System RAM and VRAM Requirements Explained
Quantized seven-billion parameter models require at least eight gigabytes of dedicated video memory for smooth interaction. Moving to larger fourteen-billion parameter variants demands sixteen gigabytes or more to avoid swapping memory to disk, which severely degrades response generation speed.
Measuring Token Speeds Across Modern GPUs
In our benchmarks, dedicated graphics processors achieved generation rates between thirty and fifty tokens per second. System configurations relying purely on shared unified memory produced slower generation rates, though remaining entirely usable for offline code completion tasks.
Optimizing Your Local Workflow
To achieve the best responsiveness, select four-bit quantized models and limit context window allocation based on your hardware ceiling. This setup delivers rapid completion suggestions without locking up background system processes.