In math it shares the top spot with o1 and is just a few points behind (well within errors). In creative writing it is basically ex-aequo with the latest ChatGPT 4o and in coding it's actually significantly ahead of everyone else and represents a new SOTA.
Makes me wonder how long the "open"AI engineers are going to want to keep working under Scam Altman.
How would the math change after factoring in that OpenAI isn't even covering entirety of opex with the sub anyway, and/or people finding associating their money and Twitter accounts to be weird, and/or this thing is supposedly running on a bigger cluster than that for OpenAI?
lmarena has also become less and less useful over time for comparing frontier models as all frontier models are able to saturate the performance needed for the kind of casual questions typically asked there. For the harder questions, o1 (not even o1-pro) still appears to be tied for 1st place with several other models... which is yet another indication of just how saturated that benchmark is.
“Grok 3 + Thinking feels somewhere around the state of the art territory of OpenAI's strongest models (o1-pro, $200/month)”.
"[...] though of course we need actual, real evaluations to look at."
His own tests are better than nothing, but hardly definitive.
I don't think anyone is arguing that ChatGPT Pro is a good value unless you absolutely need to bypass the rate limits all the time, and I cannot find a single indication that Premium+ has unlimited access to Grok 3. If Premium+ doesn't have unlimited rate limits, then it's definitely not comparable to ChatGPT Pro, and other than one subjective comment by Karpathy, we have no benchmarks that indicate that Grok 3 might be as good as o1-pro. You already get 99% of the value with just ChatGPT Plus compared to ChatGPT Pro for half the price of Premium+.
numpad0 was effectively making a strawman argument by ignoring ChatGPT Plus here... it is very easy for anyone to beat up a strawman, so I am here to point out a bad argument when I see one.
This thing is produced by musk.
The official source says "Starts at $22/month or $229/year on web", https://help.x.com/en/using-x/x-premium
This is pretty much what I paid a couple of months ago, as a Canadian.
Also visible here: https://help.x.com/en/using-x/x-premium#tbpricing-bycountry
This plan is 75 days old. I didn't know it existed until last week.
OpenAI is starting to try to get a little more realistic revenue in, Grok is acquiring customers.
In the nicest way possible I'm saying this form of preference testing is ultimately useless, primarily due to a base of dilettantes with more free time than knowledge parading around as subject matter experts and secondarily due to presumed malfeasance. The latter is more apparent to more of the masses (that don't blindly believe any leaderboard they see) now that access to the model itself is more widespread and people are seeing the performance doesn't match the "revolution" promised [0]. If you're still confused why selecting a model based on a glorified Hot or Not application is flawed, perhaps ask yourself why other evals exist in the first place (hint: some tests are harder than others.)
[0](One such instance of someone competent testing it and realizing it's not even close to the "best" model out) https://www.youtube.com/watch?v=WVpaBTqm-Zo
Do we have a way to tell if one model is smarter than another at that point?
Here's a real world intelligence test. Take on each AI as a remote intern/new-hire, and try to train it to become a useful team member (solving math puzzles or manufacturing paperclips does not count).
Ask them to design a ranking mechanism for you. They are superhuman, after all.
(I really don't think we're going to have to worry about this).
Of course this is for a personal instance, you'd need a much more expensive setup to handle concurrent users. And that's to run it, not train it.
"hello how are you today?" - 7 tokens.
And this is so much better than I could have imagined in a very short span of time.
This takes advantage of the sparsity of MOE and the efficient KV-cache of MLA.
E-ATX case = ~$300
Power Supply= ~$300
Xeon W-3500 (8 channel memory) = $1339 - $5889
Memory = $300-$500 per 64GB DDR5 RDIMM
Memory will be the major cost. The rest will be around $5,000. A lot less than "$100,000"!
And the original message you were responding to was using a CPU with AMX and mixing it with a GPU like Nvidia 4900/5900. That way the large part of the model sits in the larger slower memory, and the active part in the GPU with the faster memory. Very cost effective and fast. (Something like generating 16 Tokens/s of 671B Deepseek R1 with a total hardware cost of $10-$20k.) They tried both single and dual CPU, with the latter about 30% faster....not necessarily worth it.
https://github.com/kvcache-ai/ktransformers/blob/main/doc/en...
That's the theory. In practice, Sapphire Rapids needs 24-28 cores to hit the 200 GB/s mark and it doesn't go much further than that. Intel CPU design generally has a hard time saturating the memory bandwidth so it remains to be seen if they managed to fix this but I wouldn't hold my breath. 200 GB/s is not much. My dual-socket Skylake system hits ~140 GB/s and it's quite slow for larger LLMs.
> Why does it have to be a dual CPU design?
Because memory bandwidth is one of the most important limiting (compute) factors for larger models inference. With dual-socket design you're essentially doubling the available bandwidth.
> And the original message you were responding to was using a CPU with AMX and mixing it with a GPU like Nvidia 4900/5900.
Dual-socket CPU that costs $10k on a server that costs probably couple of factors more. Now you claimed that it doesn't have to be that expensive but I beg to differ - you still need $20k-$30k of worth equipment to run it. That's a lot and not quite "cost effective".
(Dual Socket Skylake? Do you mean Cascade Lake?)
If you price it out, it's basically the most cost effective set-up with reasonable speed for large (more than 300 GB) models. Dual socket basically doubles the motherboard[2] and CPU cost, so maybe another $3k-$6k for a 30% uplift.
[1] https://www.intel.com/content/www/us/en/products/sku/231733/... $3,157
[2] https://www.serversupply.com/MOTHERBOARD/SYSTEM%20BOARD/LGA-... $1,800
Please price it out for us because I still don't see what's cost effective in a system that costs well over $10k and runs at 8 tok/s vs the dual zen4 system for $6k running at the same tok/s.
I am not sure what your point is? There are some nice dual socket Epyc examples floating around as well, that claim 6-8 tokens/s. (I think some of those are actually distilled versions with very small context sizes...I don't see any as thoroughly documented/benchmarked as the above). This is a dual socket Sapphire Rapids example with similar sized CPUs and a consumer graphics card that gives about 16 tokens/second. Sapphire Rapids CPU and MB are a bit more expensive, and a 4090 was $1500 until recently. So for a few thousand more you can double the speed.
In any case, these are all vastly cheaper approaches than trying to get enough H100s to fit the full R1 model in VRAM! A single H100 80 GB is more than $20k, and you would need many of them + server just to run R1.
The math is clear: single-socket ktransformers performance is 8.73 tok/s and it costs ~$12k to build such a rig. The same performance one gets from a $6k dual-EPYC system. It is a full-blown version of R1 and not a distilled one as you say.
Your claim about 16 tok/s is also misleading. It's a figure for 6 experts while we are comparing R1 with 8 experts against llama with 8 experts. 8 experts on dual-socket system per ktransformer benchmarks runs at 12.2 - 13.4 tok/s and not 16 tok/s.
So, ktransformers can roughly achieve 50% more in dual-socket configuration and 50% more than dual-EPYC system. This is not double as you say. And finally, the cost of such dual-socket system is ~$20k and therefore isn't the "best cost effective" solution since it is 3.5x more expensive for 50% better output.
And tbh llama.cpp is not quite optimized for pure CPU inference workloads. It has this strange "compute graph" framework which I don't understand what is it there for. It appears completely unnecessary to me. I also profiled couple of small-, mid- and large-sized models and the interesting thing was that majority of them turned out to be bottlenecked by the CPU compute on a system with 44 physical cores and 192G of RAM. I think it could do a much better job there.
Cheapest 32 core latest EPYC (9335) x 2 = $3,079.00 x 2
Intel 32 Core CPU used above x 2 = $3,157 x 2 (I would choose the Intel Xeon Gold 6530 which is going for around $2k now, and with with higher clock speeds, and a 100 MB of more cache)
AMD Epyc Dual Socket Motherboard Supermicro H13DSH = $1899
Intel Supermicro X13DEG-QT = $1,800
Memory, PSU, Case = Same
4090 GPU = $1599 - $3,000 (temporary?)
Besides the GPU cost, the rest is about the same price. You only get a deep discount with AMD setups if you use EPYCs a few years old with cheaper (and slower) DDR4.
And again, if you go single CPU, you save over $4,000, but lose around 30% in token generation.
The "$6,000" AMD examples I've seen are pretty vague on exactly what parts were used and exactly what R1 settings including context length they were run at, making true apple to apple comparisons difficult. Plus the Sapphire Rapids + GPU example is about 10x faster in prompt processing. (53 seconds to 6 seconds is no joke!)
Yes, you're blatantly misrepresenting information and moving goalposts. Right now it has become clear that you're doing this because you're obviously affiliated with ktransformers project.
$6k for 8 tok/s or $20k for 12 tok/s. People are not stupid. I rest my case here.
So, say 500W. That's, for me in my expensive electricity city, $40/million tokens, with the pretty severe rate limit of 5600 tokens/hours.
If you're in Texas, that would be closer to $10/million tokens! Now you're at the same price as GPT-4o.
Related, you can get a whole lot of cloud computing for $2k, for those same experiments, on much faster hardware.
But yes, the data stays local. And, it's fun.
This comment chain is pretty funny.
There is no being "on par" in this space. Model providers are still mostly optimising for a handful of benchmarks / goals, like we can already see that Grok 3 is doing incredibly well on human preference (LM Arena) however with Style Control, it's suddenly behind ChatGPT-4o-latest and Gemini 2.0 is out the picture. So even within a single domain, goal, benchmark—it's not as straightforward as to say that one model is "on par" with another.
> shouldn't we expect that anybody with the computer power is capable to compete with o1-pro?
Not necessarily. I know it may be tempting to think that Grok 3 is entirely a result of xAI having lots of "computer power", but you have to recognise that this mindset is coming from a place of ignorance, not wisdom. Moreover, it doesn't even pass off as "cynical" view, because it's common knowledge that model training is really, really complicated. DeepSeek results are note-worthy, and really influential in some respects, but it hasn't magically "solved" training, or made training necessarily easier / less expensive for the interested parties. They never shared the low-level performance improvements, just model weights and lots of insight. For talented researchers, this is valuable, of course, but it's not like "anybody" could easily benefit from it in their training regimes.
Update: RFT (contra SFT) is becoming really popular with service providers, and it's not been "standardised" beyond whatever reproductions to have emerged in the weeks prior, moreover R1 cost is still pretty high[1] at something like $7/Mtok, & bandwidth is really not great. Consider something like Google Vertex AI's batch pricing for Gemini 1.5 Pro and Gemini 2.0 Flash models, which is at 50% discount, and their prompt caching which is at 75% discount. R1 is still got a way to go.
[1]: https://openrouter.ai/deepseek/deepseek-r1/providers?sort=th...
o1-pro is "o1 on steroids" and was the first selling point of the $200/month Pro subscription but they later also added "Deep Research" and Operator to the Pro subscription.
Frankly. Whoever decided on this last gen naming at MS needs to come forward. I would love to know what crazy unacceptable collection of circumstances allowed that to happen.
Not doubting your experience, just thinking how subjective it all is.
Andrej Karpathy: "I was given early access to Grok 3 earlier today" - https://news.ycombinator.com/item?id=43092066 - Feb 2025 (48 comments)
The pull quote is: The impression overall I got here is that this is somewhere around (OpenAI) o1-pro capability