I've recently been exploring the world of local language models on a modest 16GB machine, and I want to share some honest, grounded insights from my testing experience. These findings are based on my personal experimentation and are meant to provide a clear picture of what you can expect when running various models on a machine with limited resources.
First, let me clarify that the performance of local language models on a single machine depends significantly on the GPU-CPU spectrum. In my tests, I noticed that smaller models, like the 7B phi3-mini, can run fully on the GPU at approximately 35 tokens per second. However, larger models, such as those with 13B or more parameters, tend to page out to the CPU, resulting in a significant slowdown.
The amount of free VRAM at load time plays a crucial role in determining the GPU-CPU balance for a given model. For instance, I found that a model that was fully GPU-bound during one run could switch to a hybrid GPU/CPU configuration in the next, depending on the available VRAM. This inconsistency can lead to unreliable performance and call-to-response times.
One of the most intriguing findings was how much tool-calling reliability varied between models, even at the same size. Among the 7B-class models I actually ran, mistral-7b-instruct, phi3-mini, and Hermes-2-Pro-Mistral-7B could all call a tool when prompted, but none did it every time. The same model would emit a clean tool call on one run and just narrate what it would do on the next. Some capable code models, like Qwen2.5-Coder, wouldn't call tools through my adapter at all, even though they wrote perfectly good code. Tool-calling on small local models is probabilistic, not a fixed property you can count on.
Lastly, it's essential to acknowledge that the performance of local language models on a single 16GB machine is limited. While it's possible to achieve reasonable results with smaller models, larger models will inevitably struggle, leading to slower processing times and inconsistent performance.
In conclusion, testing local language models on a single 16GB machine reveals a complex interplay between GPU-CPU balance, VRAM availability, and tool-calling reliability. While smaller models can provide decent performance, larger models may struggle, leading to inconsistent results and slower processing times. As machine learning continues to evolve, it's crucial to stay informed about the real-world limitations and challenges of running models on modest hardware.