A random, funny, interesting and telling data point: my MacBook M3 Max while DS4 is generating tokens at full speed peaks 50W of energy usage...
- Consumers of LLM inference (developers and hobbyists) will be more aware of compute cost, leading them to develop more token-efficient uses of LLM inference and be incentivized to pick the right model for the right job (instead of throwing Sonnet at the wall and follow up with Opus if that doesn't stick)
- A larger market for on-device (and therefore open weight) LLM's will probably result in more research concentrated on those inherently more efficient (because compute/memory-constrained) models.
I think that despite the inefficiencies, shifting the market towards local inference would be a net positive in terms of energy use. Remember that 50W might seem like a lot, but is still much less than what, let's say, a PS5 draws.
Also remember how AWS had the same promise and now we're just deploying stack after stack and need 'FinOps' teams to get us to be more resource-efficient?
[1] https://finance.yahoo.com/sectors/technology/articles/nvidia...
They don't usually go into much detail, but the impression I get is that they think data centers are energy monsters full of overheated GPU's that need to be constantly replaced, while your phone is full of mostly unused compute capacity and will barely break a sweat if it's only serving queries for a single user at a time.
They don't seem to give much thought to the energy usage per user (or what this will potentially do to your phone battery), or how different phone-sized vs data center-sized models are in terms of capability.
Plus, a Mac that's not running inference idles down to 1-5W, only drawing power when it needs to. Datacenters must maximize usage, individuals and their devices don't have to.
A Mac is also the rest of the personal computer!
If DS4 Flash peaks at 50W and is 280B parameters, does that mean DS4 Pro at 1.6T parameters would likely be 300W or so? And the latest GPT 5 and Opus which feel maybe comparable-ish around 500W? Is it fair to say that when I'm using Claude Code and it's "autofellating" or whatever I'm burning 500W in a datacenter somewhere during that time?
Data center energy use isn't simple to calculate because servers are configured to process a lot of requests in parallel. You're not getting an entire GPU cluster to yourself while your request is being processed. Your tokens are being processed in parallel with a lot of other people's requests for efficiency.
This is why some providers can offer a fast mode: Your request gets routed to servers that are tuned to process fewer requests in parallel for a moderate speedup. They charge you more for it because they can't fit as many requests into that server.
Claude Sonnet is probably running on a 8 GPU box that consumes 10 kW while Opus might use more like 50 kW but that's shared by a bunch of users thanks to batching.
I could write an engine that only uses 10W on your machine, but it wouldn't be meaningful if it was also 10X slower.
More power consumption is usually an indicator that the hardware is being fully utilized, all things equal (comparing GPU to GPU or CPU to CPU, not apples to oranges)
Washing machine 900W. Hair dryer 1500W. Pizza oven 2000W. So yeah, you say 50W, yeah sure same as video rendering or gaming I guess, yet not really an OMG-level number.
And frankly I'm not quite sure there's anything like economy of scale where it gets more efficient if you serve more users (like some sibling comments seem to imply).
Last thing, and I know many know but also many others don't or have forgotten: Watts is a rate of consumption, not an absolute amount. That is Joule, energy. So you say 50W, but what you pay for (or the planet pays, whatever) generally is the amount of energy, hence you need to say for how long that consumption was sustained. 50W over 2 hours, that's 100 Joules, the actual resource you consumed and paid for.
Power (watts) is like speed (m/s). You say 50 miles an hour, need to say how long was the drive, so we know how far you got.
Also, datacenter scale devices are almost certainly designed to minimize energy use per operation given comparable latency. You can still compete as an on prem consumer by (1) repurposing your existing hardware, which saves on high CapEx costs, (2) increasing latency, getting your answer computed in a longer time, which probably saves at least some power by design if you can leverage e.g. NPUs, or (3) running smaller or more bespoke models that aren't worthwhile for the bigger players to serve at scale.
There's also a likely gain in serving more requests in parallel, but it may have more to do with successfully amortizing memory access for model weights than any inherent increase in efficiency. Anyway, I've argued in sibling comments that you perhaps can also leverage this on consumer hardware for the special case of DeepSeek V4.
Yes of course that was a brain fart of mine. Watt is Joule per second not certainly Joule per hour. I made the point of "lecturing" readers on power v. energy since Antirez (OP) wrote _"50W of energy usage..."_ (instead of power consumption) and it's a mistake people often make. So my side point was: ok 50W but for how long.
The other thing I'm arguing is 50W is nothing to be shocked by. I would like to see an argument for the opposite. I'd like to know what's the power consumption of playing eg. Baldur's Gate for a couple hours on a gaming rig and I wager we surpass that by a margin.
Now, the data center economy of scales. You're saying they almost certainly exists. Okay whatever I don't know. Requests served in parallel. Amortizing memory access for model weights. Likely. I'm writing this with some thinly veiled dismissive attitude because I believe that it would be very useful to have hard data on whether or not serving many users v. just one user makes LLMs more efficient. It's an important point with wide ranging implications.
If there is scale, like you claim, and one day a wealthy patron gifts me a 40k USD rig where I can run a frontier LLM locally, then I'd still be making selfish use of the commons (energy, which belong to the planet, all of us, that kinda stuff) because the efficient/responsible choice is to pool and use a cloud vendor (or pool your rig with neighbors etc).
But saying a machine can be more efficient if it serves many users sounds to me a bit like nine women making a baby in a month.
A big cloud vendor does not face the same opportunity, they cannot leverage the repurposing of your own existing hardware. And they'll definitely want to minimize latency in order to get maximum throughput/utilization from the hardware they did buy, even at an emergy cost. That's why I was careful to note latency as a possible factor before.