> or instance, maybe you can't afford to take on more customers right now, Anthropic. Maybe if you are severely undermining the customer relationships you already have, you should just admit you can't sell any more 20x plans right now and only accept new customers at lower tiers until you have the necessary capacity.
Or just increase prices for new claude code users? Surely transparent upfront across the board price increases are easier to swallow than hidden context-based pricing changes like this?
Idk about morality, but it’s certainly a way to stop dystopian mass surveillance nightmares if everyone capable of building one refuses.
So if you live in the US and don’t want one government agency in the US to have this power (that is ambiguous under current law), one way you can try to avoid it is by refusing to sell it to them and urging others to do the same.
It’s a long shot sure, but it certainly seems more effective than hoping the legislature wakes up and reigns in the executive these days.
My best guess is that for certain goods, people make purchasing decisions around major life events like getting married or becoming parents. If they have already crossed those thresholds those purchase patterns may be harder to unseat and replace, they may already be solving those new needs and have habits around existing brands.
My second guess is that DINKs have more disposable income.
Disposable income is Gross Income minus Taxes. A 2 parent family with 2 kids making the same as a DINK couple would have a lower tax burden and more disposable income, but the DINK couple almost certainly has more discretionary income, which money left after paying for necessities like housing, food, and medical care.
AMD’s software experience is riddled with bugs rendering out of the box training with AMD is impossible. We were hopeful that AMD could emerge as a strong competitor to NVIDIA in training workloads, but, as of today, this is unfortunately not the case. The CUDA moat has yet to be crossed by AMD due to AMD’s weaker-than-expected software Quality Assurance (QA) culture and its challenging out of the box experience.
[snip]
> The only reason we have been able to get AMD performance within 75% of H100/H200 performance is because we have been supported by multiple teams at AMD in fixing numerous AMD software bugs. To get AMD to a usable state with somewhat reasonable performance, a giant ~60 command Dockerfile that builds dependencies from source, hand crafted by an AMD principal engineer, was specifically provided for us
[snip]
> AMD hipBLASLt/rocBLAS’s heuristic model picks the wrong algorithm for most shapes out of the box, which is why so much time-consuming tuning is required by the end user.
etc etc. The whole thing is worth reading.
I'm sure it has (and will continue to) improved since then. I hear good things about the Lemonade team (although I think that is mostly inference?)
That’s insane. There should be a big team of people at AMD whose whole job is just to dogfood their stuff for training like this. Speaking of which, Amazon is in the same boat, I’m constantly surprised that Amazon is not treating improving Inferentia/Trainium software as an uber-priority. (I work at Amazon)
> “Are we afraid of our competitors? No, we’re completely unafraid of our competitors,” said Taylor. “For the most part, because—in the case of Nvidia—they don’t appear to care that much about VR. And in the case of the dollars spent on R&D, they seem to be very happy doing stuff in the car industry, and long may that continue—good luck to them.
Where's the scope for an L7 promo in "Fixed a bunch of tiny issues that were making it hard to use Tranium/Inferentia with PyTorch"?
Amazon's compensation strategy, in which you primarily get a raise years in the future for tricking your management chain into promoting you is definitely bearing its rotten fruit.
Anecdotal but over several years with an AMD GPU in my desktop I've tried multiple times to do real AI work and given up every time with the AMD stack.
Im running fine on my AMD 7800xt 16gb... Yes memory is a bit limited, but apart from the i have found that it works great using Vulcan in LM studio for example.
ROCm works great too, the only issue i have had is that my machine froze a couple of times as it used 100% of the graphics and the OS had nothing left. Since moving to vulcan i stopped getting these errors apart from a little UI slowdown when i had 4 models loaded at the same time taking turns.
Im also on a i7 6700 with 32gb DDR4 so im sure that is causing more slowdowns then the graphics card.
Yet another reason to doubt claims that ”software is solved”.
Anthropic did retire an interview take-home assignment involving optimising inference on exotic hardware, because Claude could one shot a solution, but that was clearly a whiteboard hypothetical instead of a real system with warts, issues and nuance.
i'm doing inference on a free mi300x instance from AMD right now. not sure if the software stack is just old or what, but here's what i've observed: stuck on an old version of vllm pre-Transformers 5 support. it lacks MoE support for qwen3 models. oss-120b is faaaar slower than it should be.
int8 quantization seems like it's almost supported, but not quite. speeds drop to a fraction of full precision speed and the server seems like it intermittently hangs. int4 quantization not supported. fp8 quantization not supported.
again, maybe AMD is just being lazy with what they've provided, but it's not a great look.
right now the fastest smart model i can run is full precision qwen3-32b. with 120 parallel requests (short context) i'm getting PP @ 4500 tokens/sec and TG @ 1300 tokens/sec
From the papers I've read and the labs that I have worked in personally, I would say that most scientists developing Deep learning solutions use CUDA for GPU acceleration
Yeah, historically it’s been software that’s limited AMD here. Not surprised to hear that may still be the issue. NVidia’s biggest edge was really CUDA.
I don’t know what’s a chicken and what’s an egg here. But ROCm support is often missing or experimental even in very basic foundational libraries. They need someone else to double down on using their chips and just break the software support out of the limbo.
This is what I've heard on the "street". Building a CUDA-compatible stack for AMD's hardware requires highly-paid SWEs. It's a very niche field, and talent is hard to come by.
But AMD does not want to pay these specialized SWEs the market rate. Their existing SWEs would be up in arms saying, basically, "what are we, chopped liver??", or so the thinking goes.
So AMD is stuck with a shitty software stack which cannot compete with CUDA.
If I were making such decisions, I would just cull the number of existing SWEs down by 50%, and double the pay for remaining ones. And then go out and hire some top talent to build a good software stack.
This exactly. I don’t believe the government should be censoring porn, but I have a really hard time arguing that principle against studies that suggest it is normalizing choking and slapping women among the young men exposed to it. Why is this roleplay fetish the beachhead and not something like that?
That's an odd perspective. I have heard of young women demanding to be choked and slapped (with their partners acquiescing) far far more than young men instigating the behaviour.
> Now thought to be the second most common cause of stroke in women under 40, it can also lead to difficulty swallowing, incontinence, seizures, memory problems, depression, anxiety and miscarriage.
remote GPU compute payloads have been around a lot longer than LLMs, they're just few and far between.
folding@home and other such asynchronous "get this packet of work done and get back to me' style of operations rarely care much about latency.
Remote transcoding efforts can usually adjust whatever buffer needed to cover huge latency gaps , a lot of sim and render suites can do remote work regardless of machine to machine latency..
I just sort of figure the industry will trend more async when latency becomes a bigger issue than compute. Won't work in some places, but I think we tend to avoid thinking that way right now due to a lack of real need to do so; but latency is one of those numbers that trends down slowly.
Like gravity, there is some inexorably force drawing the state towards mass surveillance tools as it makes the job easier. Removing friction that fights against that force is real
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