Can I ask how many tok/s you're getting on that setup? I'm trying to decide whether to invest in a high-end NVIDIA setup or a Mac Studio with llama.cpp for the purposes of running LLMs like this one locally.
Mixtral runs at about 43 tokens/s at q3_K_S with all layers offloaded. I normally avoid going below 4-bit quantization, but Mixtral doesn’t seem phased. I’m not sure if the MoE just makes it more resilient to quantization, or what the deal is. If I run it at q4_0, then it runs at about 24 tokens/s, with 26 out of 33 layers offloaded, which is still perfectly usable, but I don’t usually see the need with Mixtral.
Ollama dynamically adjusts the layers offloaded based on the model and context size, so if I need to run with a larger context window, that reduces the number of layers that will fit on the GPU and that impacts performance, but things generally work well.
There are a few things to keep in mind: no programmer that I know is sitting there typing code for hours at a time without stopping. There’s a lot more to being a developer than just typing, whether it is debugging, thinking, JIRA, Slack, or whatever else. These CoPilot-like tools will only activate after you type something, then stop for a defined timeout period. While you’re typing, they do nothing. After they generate, they do nothing.
I would honestly be surprised if the GPU active time was more than 10% averaged over an hour. When actively working on a large LLM, the RTX 3090 is drawing close to 400W in my desktop. At a 10% duty cycle (active time), that would be 40W on average, which would be 320Wh over the course of a full 8-hour day of crazy productivity. My electric rate is about 15¢/kWh, so that would be about 5¢ per day. It is absolutely not running at a 100% duty cycle, and it’s absurd to even do the math for that, but we can multiply by 10 and say that if you’re somehow a mythical “10x developer” then it would be 50¢/day in electricity here. I think 5¢/day to 10¢/day is closer to reality. Either way, the cost is marginal at the scale of a software developer’s salary.
Ollama caches the last used model in memory for a few minutes, then unloads it if it hasn’t been used in that time to free up VRAM. I think they’re working on making this period configurable.
Latency is very good in my experience, but I haven’t used the local code completion stuff much, just a few quick experiments on personal projects, so my experience with that aspect is limited. If I ever have a job that encourages me to use my own LLM server, I would certainly consider using it more for that.
Note that M1/M2 Ultra is quite a bit faster than M3 Max, mostly due to 800 Gb/s vs 400 Gb/s memory