8 ms·

My desktop GPU can run small models at 185 tokens a second. Larger models with speculative decoding: 50t/s. With a small, finetuned model as the draft model, no

• smokel 2y ago
  A simple text parser would probably be 10,000,000 times as fast. So the statement that this won't take much power at all, is a bit of an overstatement.
• Hercuros 2y ago
  It’s not just about the energy usage, but also purchase cost of the GPUs and opportunity cost of not using those GPUs for something more valuable (after you have bought them). Especially if you’re doing this at large scale and not just on a single desktop machine. Of course you were already saying it’s not a good idea, but I think the above definitely plays a role at scale as well.
  • SlightlyLeftPad 2y ago
    You’re right, I could be trying to get Crysis to run at 120 fps.
    • tmtvl 2y ago
      If you have spare GPU time you could donate it to projects like Folding@Home.
• boxed 2y ago
  50 tokens per second. Compared to a quick and dirty parser written in python or even a regex? That's going to be many many orders of magnitude slower+costlier.
  • anthk 2y ago
    awk would run millions times faster, not to mention mawk and awka.
• berkes 2y ago
  In order to make the point that > energy usage for parsing isn't why You'll need to provide actual figures and benchmark these against an actual parser. I've written parsers for larger-scale server stuff. And while I too don't have these benchmarks available, I'll dare to wager quite a lot that a dedicated parser for almost anything will outperform an LLM magnitudes. I won't be suprised if a parser written in rust uses upwards of 10k times less energy than the most efficient LLM setup today. Hell, even a sed/awk/bash monstrosity probably outperforms such an LLM hundreds of times, energy wise.
  • chgs 2y ago
    How many times would you need to parse to get an energy saving on using an lm to parse vs using an llm to write a parser, then using the parser to parse.
    • GTP 2y ago
      > using an llm to write a parser You're assuming OP needs an LLM to write a parser, since they mentions writing many during their career they probably don't need it ;)
      • berkes 2y ago
        I didn't use an LLM back then. But would totally do that today (copilot). Especially since the parser(s) I wrote were rather straightforward finite state machines with stream handling in front, parallel/async tooling around it, and at the core business logic (domain). Streaming, job/thread/mutex management, FSM are all solved and clear. And I'm convinced an LLM like copilot is very good at writing code for things that have been solved. The LLM, however, would get very much in the way in the domain/business layer. Because it hasn't got the statistical body of examples to handle our case. (Parsers I wrote were a.o.: IBAN, gps-trails, user-defined-calculations (simple math formulas), and a DSL to describe hierarchies. I wrote them in Ruby, PHP, rust and perl.)
      • chgs 2y ago
        I was thinking more of when a sufficiently advanced device would be able to “decide” the task would be worth using its own capabilities to write some code to tackle the problem rather than brute force. For small problems it’s not worthwhile, for large problems it is. It’s similar to choosing to manually do something vs automate it.
• anthk 2y ago
  My Atom n270 netbook with mawk and a few lines parsing the files with a simple regex will crush down your GPU+LLM's on both time and power usage.