this post was submitted on 22 Dec 2024
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The environmental cost of training is a bit of a meme. The details are spread around, but basically, Alibaba trained a GPT-4 level-ish model on a relatively small number of GPUs... probably on par with a steel mill running for a long time, a comparative drop in the bucket compared to industrial processes. OpenAI is extremely inefficient, probably because they don't have much pressure to optimize GPU usage.
Inference cost is more of a concern with crazy stuff like o3, but this could dramatically change if (hopefully when) bitnet models come to frutition.
Still, I 100% agree with this. Closed LLM weights should be public domain, as many good models already are.
With current kWh/token it's 100x of a regular google search query. That's where the environmental meme came from. Also, Nvidia plans to manufacture enough chips to require global electricity production to increase by 20-30%.
Doesn't Open AI just have the same efficiency issue as computing in general due to hardware from older nodes?
What are bitnet models and what does that change in a nutshell?
Read the pitch here: https://github.com/ridgerchu/matmulfreellm
Basically, using ternary weights, all inference-time matrix multiplication can be replaced with much simpler matrix addition. This is theoretically more efficient on GPUs, and astronomically more efficient on dedicated hardware (as adders take up a fraction of the space as multipliers in silicon). This would be particularly fantastic for, say, local inference on smartphones or laptop ASICs.
The catch is no one has (publicly) risked a couple of million dollars to test it with a large model, as (so far) training it isn't more efficient than "regular" LLMs.
No one really knows, because they're so closed and opaque!
But it appears that their models perform relatively poorly for thier "size." Qwen is nearly matching GPT-4 in some metrics, yet is probably an order of magnitude smaller, while Google/Claude and some Chinese models are also pulling ahead.