One thing about these numbers that's absolutely shocking to me is how low the energy use is:
> That model’s usage was well within our budget ($68, about 4kWh of energy use / 365 grams of carbon emissions).
The energy cost is literally 1% of the total cost. For context, 4kWh of energy would drive you about 15 miles in an EV, about half of the average person's daily driving miles. It's boiling 10 gallons of water.
With the talk of AI data centers' impact on the world, you'd think this would be 10x to 100x the amount of energy in order to get the effects they're using here.
My takeaway: the AI data center buildout is an overbuild probably at least as large as the fiber buildout that left us with so much dark fiber. If not even bigger. The only thing that will save the economy is the inability of NVIDIA and chip fabs to produce enough chips to match the buildout planned.
I have the feeling China is somehow ahead when it comes to energy (and cost) efficiency for AI usage. After all, the two are in a direct competition, and this difference is significant. Or is the "hyper" scaling of energy hungry datacenters in US part of a bubble?
I agree but it does worry me how fast my usage is increasing. Two months ago I was using 10x less tokens and probably not much more than 5kWh on inference. This month about 30kWh on inference. If it becomes more affordable, is there going to be another jump? Not quite sure
At some point the agents will be good enough that you can tell them "here's $100, go make me money" and they will, maybe not a lot and not all the time, but the EV given the cost of inference will be positive.
Your only talking about variable direct energy. Does it take into account the entire lifecycle, building the data centre, running the cooling, building the chips, the % the chips are not utilised.
> Does it take into account the entire lifecycle, building the data centre
My entire point is that 99% of the dollar cost of running these models goes to things other than the GPU power. The capex cost to building cost to GPU cost to storage/networking/chasses/wiring plus the other operation costs dwarf the electricity. Even the other electricity costs, lets say double it for all the supporting compute, plus another 25% for a 1.25 PUE, and you're at 2.5% of all-in cost of running these models is from electricity.
The non-electricity costs are massive and the constraints on fabs, etc. will drive the amount of the AI build far more than energy availability.
> Unfortunately there are still consequences to it. I chose the 'wrong' model for the prototype, and we spent 450M tokens / $150 / 5kWh of energy use almost overnight. The MCP server itself works well and we now have a great demo of the capabilities, so it’s not for nothing:
> Nonetheless, it’s a good reminder to be careful with model selection and with agentic patterns. We could have achieved similar results for most likely 5x less cost with not that much more effort. Lessons learned! We need to budget for this, and be more careful. Could have seen it coming, but now we know.
I don't get it. Why was it wrong? Which one would have been better? What was the lesson and how could you have foreseen it?
Hmm I might need to rephrase. Initial challenge was to use GLM 5.3 Flash and I was on the non-Flash version for the whole vibe coded build. Just wasn’t paying attention and I didn’t realize that one session was a quarter of the month’s spend and 150% of the budget (big price difference between models)
Makes me appreciate my ChatGPT subscription. I’ve had multiple days between 1B-2B tokens (now less so, models have indeed become token efficient) and regularly in the > 100M range. Even then, $150 sounds excessive. I wonder if their cache is getting nuked for some reason, or maybe they decide to use Cerebras that doesn’t subsidize cached tokens.
Yeah, I clicked expecting a review of GLM 5.3 Flash, but the article was more a retrospective about what he learned during his September challenge: "Only use GLM 5.3 Flash for one month"
He said his experiment was a failure because:
1. He accidentally spent 450M tokens vibe coding with the wrong model, instead of GLM 5.3 Flash.
2. When he used GLM 5.3 Flash, it was sometimes slow. So he switched to other models (Deepseek / Qwen) instead. His guess to why it was slow: GLM 5.3 Flash was so good that the providers were congested.
3. He still needed to use other models besides GLM 5.3 Flash, for R&D and benchmarking.
His takeaways from doing the experiment were:
1. Measure local usage more.
2. Experiment with agent orchestration, with bounded goals.
3. Don't count other models that are used for R&D.
4. Play with Jev.
5. Include experiments with flagship models to compare with cheap open models.
His conclusion about GLM 5.3 Flash: Probably viable for day to day work, but he'll have more thoughts next month.
Some vague commentary about performance with what appears to be assumptions about GPU availability, but no clarity about which inference provider is being used. If ZAI is assumed, I believe they aren't subject to the assumptions in the post based on what they've said publicly, but if they were using some other provider, perhaps.
The second reason appeared to be simply "because we chose not to". The post seems to be pretty much content-less in any practical sense. I clicked on it because I do quite like this models average performance and I was hoping to see some kind of review content.
It was a bit of a silly challenge, wasn’t sure how workable, learned a lot in the process about what actually drives usage / costs, and how to keep both under control
Not OP, but I've used both quite a bit and I think GLM 5.3 (non-flash) is a vastly better model. The flash variant is good for general workhorse agents, but it doesn't seem to reason holistically about code and over-engineers solutions to each specific problem it solves. But if you use GLM 5.3 to write a detailed plan with little to no ambiguity, GLM 5.3 Flash executes it just fine for a fraction of the price.
I have a hard time justifying GLM 5.3 these days. It’s slightly better than Flash but rarely enough to justify the much steeper price. We chose to use usage-based billing only so are very sensitive to model price.
When text wuality or for pure but adwansed coding is concerned i always pick glm 5.3. The flash is awesome for everything that dosent really matter though.
Ill tell you from my personal use glm 5.3 flash was better then deepseek 4.1 flash, also deepseek liked to yap in his reasoning traces soo fucking much, the yapping was fast but the task was so slow to complete...
Interesting, I have roughly the opposite experience regarding speed: DeepSeek V4.1 Flash gets stuff done way more quickly for me than GLM 5.3 Flash. (I was getting 300+ tokens/sec with DS vs ~100 with GLM in my testing.) I agree that GLM 5.3 Flash is a slightly better model, but for me at least, it's not a huge difference, and I'd rather have DeepSeek's speed.
Flash is pretty decent coder, but it should be paired with good planner and reviewer. I would pick astra low for planning and sol 6.1 medium for reviews.
It's my favorite model family to interact with, it's prose is the best imo, it makes me laugh from time-to-time (like when it said it would "crib" some code from another project, lul)
I currently have qwen-flash working on an NES emulator harness so qwen-little can play my first RPG (ff1)
(tho I have used all the others I mentioned, happenstance I'm using qwen this iteration/task)
There are some issue point not properly mentioned ...
* Models like DeepSeek V4.1 Flash are much cheaper on DeepSeek their API directly because of the cache handeling is better. Neuralwatt can hit up to 98% but DeepSeek can do 99.x... That may not sound like a big difference but it quickly widens the gap on long tasks to grow 2x a 3x in price. DeepSeek their cache handeling is S-tier (with a ton of features, for instance 24h caching).
* The same issue is also present if you compare GLM 5.3 Flash with z.ai vs Neuralwatt. Its just way more cheaper from the source, then from Neuralwatt.
* The energy numbers from Neuralwatt are ... to be taken with a ton of salt. Past energy numbers had the same models (for instance) GLM 5.2 up to 6x cheaper in energy usage, then after they "fixed" issues with the energy numbers. In reality, those energy numbers are just a different form of billing, but not a actual representation of the energy usage of AI models. Things like profits are inside those energy numbers. So seeing 4kWH used for a model, does not mean that it uses 4Kwh.
Edit: That are some interesting downvotes ...
To answer the questions. It was stated by the CEO himself in one of the video blogs that the energy prices inc their profit margins. Regarding their published numbers ... I like to point out that this is the same company that had up to 6x cheaper energy numbers at the start of the year until they got updated. Again, its in one of those video blogs the CEO did. Its around the same time when they increased the price from $5/1kwh to $10/1kwh.
Yes, DeepSeek API is cheaper then Neuralwatt. I have done way too many comparisons between NW and other providers, regarding their prices. Over long sessions, that gap grows because of the differences in caching. You need to use the NW Flex option to reduce the impact but then your constantly waiting on responses (good for overnight work, not great in prime time).
For what it’s worth the energy numbers I get from Neuralwatt are within the ballpark of what is available elsewhere like https://cleerdash.sustainableaigroup.com/. What’s your source that there is profit / capex in there? Their published methodology seems pretty transparent
I'm skeptical that the DeepSeek official API actually nets out cheaper right now, but I avoid it anyway because they store and train off of your prompts.
I think NW's profits are mostly between what they pay for electricity and what they charge you for electricity. I don't think there's any need to look to conspiracies to explain billing errors.
> That model’s usage was well within our budget ($68, about 4kWh of energy use / 365 grams of carbon emissions).
The energy cost is literally 1% of the total cost. For context, 4kWh of energy would drive you about 15 miles in an EV, about half of the average person's daily driving miles. It's boiling 10 gallons of water.
With the talk of AI data centers' impact on the world, you'd think this would be 10x to 100x the amount of energy in order to get the effects they're using here.
My takeaway: the AI data center buildout is an overbuild probably at least as large as the fiber buildout that left us with so much dark fiber. If not even bigger. The only thing that will save the economy is the inability of NVIDIA and chip fabs to produce enough chips to match the buildout planned.
I have the feeling China is somehow ahead when it comes to energy (and cost) efficiency for AI usage. After all, the two are in a direct competition, and this difference is significant. Or is the "hyper" scaling of energy hungry datacenters in US part of a bubble?
My entire point is that 99% of the dollar cost of running these models goes to things other than the GPU power. The capex cost to building cost to GPU cost to storage/networking/chasses/wiring plus the other operation costs dwarf the electricity. Even the other electricity costs, lets say double it for all the supporting compute, plus another 25% for a 1.25 PUE, and you're at 2.5% of all-in cost of running these models is from electricity.
The non-electricity costs are massive and the constraints on fabs, etc. will drive the amount of the AI build far more than energy availability.
> Nonetheless, it’s a good reminder to be careful with model selection and with agentic patterns. We could have achieved similar results for most likely 5x less cost with not that much more effort. Lessons learned! We need to budget for this, and be more careful. Could have seen it coming, but now we know.
I don't get it. Why was it wrong? Which one would have been better? What was the lesson and how could you have foreseen it?
Makes me appreciate my ChatGPT subscription. I’ve had multiple days between 1B-2B tokens (now less so, models have indeed become token efficient) and regularly in the > 100M range. Even then, $150 sounds excessive. I wonder if their cache is getting nuked for some reason, or maybe they decide to use Cerebras that doesn’t subsidize cached tokens.
It's an excellent workhorse. When I am running out of my GLM quota I switch GLM-5.3-flash to DS-4.1-flash.
He said his experiment was a failure because:
1. He accidentally spent 450M tokens vibe coding with the wrong model, instead of GLM 5.3 Flash.
2. When he used GLM 5.3 Flash, it was sometimes slow. So he switched to other models (Deepseek / Qwen) instead. His guess to why it was slow: GLM 5.3 Flash was so good that the providers were congested.
3. He still needed to use other models besides GLM 5.3 Flash, for R&D and benchmarking.
His takeaways from doing the experiment were:
1. Measure local usage more.
2. Experiment with agent orchestration, with bounded goals.
3. Don't count other models that are used for R&D.
4. Play with Jev.
5. Include experiments with flagship models to compare with cheap open models.
His conclusion about GLM 5.3 Flash: Probably viable for day to day work, but he'll have more thoughts next month.
The second reason appeared to be simply "because we chose not to". The post seems to be pretty much content-less in any practical sense. I clicked on it because I do quite like this models average performance and I was hoping to see some kind of review content.
Edit: I found a linked article that mentions the inference provider who does the measurements.
I'm mainly using flash varients, at least as the default, bump.up to stronger model as needed (less often these days)
It's my favorite model family to interact with, it's prose is the best imo, it makes me laugh from time-to-time (like when it said it would "crib" some code from another project, lul)
I currently have qwen-flash working on an NES emulator harness so qwen-little can play my first RPG (ff1)
(tho I have used all the others I mentioned, happenstance I'm using qwen this iteration/task)
edit: I subscribe to z.ai, I don't host.
What kind of hardware and what particular quant?
* Models like DeepSeek V4.1 Flash are much cheaper on DeepSeek their API directly because of the cache handeling is better. Neuralwatt can hit up to 98% but DeepSeek can do 99.x... That may not sound like a big difference but it quickly widens the gap on long tasks to grow 2x a 3x in price. DeepSeek their cache handeling is S-tier (with a ton of features, for instance 24h caching).
* The same issue is also present if you compare GLM 5.3 Flash with z.ai vs Neuralwatt. Its just way more cheaper from the source, then from Neuralwatt.
* The energy numbers from Neuralwatt are ... to be taken with a ton of salt. Past energy numbers had the same models (for instance) GLM 5.2 up to 6x cheaper in energy usage, then after they "fixed" issues with the energy numbers. In reality, those energy numbers are just a different form of billing, but not a actual representation of the energy usage of AI models. Things like profits are inside those energy numbers. So seeing 4kWH used for a model, does not mean that it uses 4Kwh.
Edit: That are some interesting downvotes ...
To answer the questions. It was stated by the CEO himself in one of the video blogs that the energy prices inc their profit margins. Regarding their published numbers ... I like to point out that this is the same company that had up to 6x cheaper energy numbers at the start of the year until they got updated. Again, its in one of those video blogs the CEO did. Its around the same time when they increased the price from $5/1kwh to $10/1kwh.
Yes, DeepSeek API is cheaper then Neuralwatt. I have done way too many comparisons between NW and other providers, regarding their prices. Over long sessions, that gap grows because of the differences in caching. You need to use the NW Flex option to reduce the impact but then your constantly waiting on responses (good for overnight work, not great in prime time).
I think NW's profits are mostly between what they pay for electricity and what they charge you for electricity. I don't think there's any need to look to conspiracies to explain billing errors.