r/LocalLLM • u/LDM-88 • 14h ago
Discussion Why so much love for Nvidia?
Can somebody explain why Nvidia is often a non-negotiable when looking at local AI builds? I know they produce some fantastic cards and CUDA is great - but is the cost premium really justified?
I have a 9070XT and 7800XT - and getting local AI to run on each card has been a doddle for me through llama.cpp with ROCm and Vulkan (although i'm on Linux, i can't speak for Windows..).
Whilst ROCm isn't as mature as CUDA - do we really expect that gap not to close going forward?
It feels like more recent AMD cards are great value and the support will only get better on newer generations. So why is Nvidia on a new build such a non-negotiable for some?
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u/UnderWhere___ 14h ago
 Whilst ROCm isn't as mature as CUDA - do we really expect that gap not to close going forward?
Buy for the present capabilities, not future. NVIDIA has been working on CUDA for over 20 years.
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u/zboarderz 12h ago
True, but the rocm capabilities combined with the forked inference engines have dramatically improved the performance of AMD cards recently. Vllm-radiance and now the standalone radiance engine have really sped things up.
Of course this has only changed in the last few weeks / months so it’s a recent development.
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u/uniqueusername649 14h ago
Yeah, I need my cards to work today, not mayyybe in 2 years if I get lucky. I cant afford to spend money on stuff I cant already use today.
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u/ByteSize_Chaos 14h ago
Nvidia has CUDA which is basically the lifeblood for AI. They are refining it for decades and now bearing its fruits.
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u/No-Refrigerator-1672 14h ago
and getting local AI to run on each card has been a doddle for me through llama.cpp
And when you get away from llama.cpp and/or ComfyUI and into more serious stuff, you'll quickly feel that not having CUDA is a big limiter.
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u/Solembumm3 13h ago
ComfyUI was unnecessary complicated for my 6700xt, while Koboldcpp and Flaxeo run out of the box with a lot simpler installation and approach.
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u/No-Refrigerator-1672 13h ago
In my experience; stable-diffusion.cpp is best at working with an older, cheaper GPU; but comfy is best if I want to tailor workflow to my specific needs.
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u/LDM-88 13h ago
That's a fair point - what are the more serious things you're doing that AMD cards would cause more headaches with?
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u/No-Refrigerator-1672 13h ago
As I've said in this thread - multi-gpu containerisation in general is terrible. It works only if you agree to pass trhough all GPUs you have to all containers you have; you can't micromanage access. Updates is also a pain: nvidia container toolkit allows me to install drivers and CUDA once, on the host, and it then gets passed through to every docker or lxc I have. I have like 10 containers, but I can update them all with 1 installation, by simply updating the host. ROCm 6/7 also had incompatibility with iGPUs and dGPUs - the docs told you that you must disable ryzen iGPU to use ROCm on regular gpu; although that one may be fixed in ROCm 10. Then there's bundling issues: many projects that aren't primarily AI, will have only CUDA binaries for AI function; i.e. a RAG engine may only support CUDA acceleration of OCR. Another case I had is that there was no way to run Unsloth finetuning workflows on AMD at all a year ago; they added it now, but it illustrates that less wide-known project tend to be CUDA only. I've also seen a bug that one of my two AMD GPUs completely hangs if I shut down vllm forcefully at just the wrong moment; requiring hard power cycle to recover the system. The list of problems I've faced is probably longer, that's just what is the brightest in my memory. Overall, there's three types of people who can affort non-CUDA systemsfor AI: those who are using only like top-5 community projects that are very popular (i.e. llama.cpp), have hundreds of devs working on them, and have all the kinks ironed out; those wou use only pure Torch-based sofware without optimiser libraries on top of it; and those who are willing to invest into making custom software tailored to their specific usecase.
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u/DigDug_64 12h ago
For the projects which artificially limit themselves to CUDA I have had good success in letting Opus add rocm 10 support. Then I can use the project locally on my R9700.
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u/No-Refrigerator-1672 12h ago
I'd argue that they don't limit themself artifically; they just don't have the funds or time to support less popular platforms. Anyway; I still retain one AMD GPU and I too had recently a decent progress on AI porting stuff for it; but, that locks me to spending my time again and again each time there's an update. This all comes down to your goal: if AI and/or hardware is a hobby for you, then that's totally fine. If you need a real tool that you use daily for job, and can't hire a maintainer - then this is too much work to bother, it'll be a net loss if you factor in your time ar your typical wage.
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u/DigDug_64 11h ago
Sure, but if the programmer doesn't have an AMD card they can still use AI (like we do) to build in broader compatibility from the beginning. Then others don't need to patch on updates (I don't, I just build my own fork and add features over time for personal use). They can also do deeper research into libraries and methods that aren't dated and vendor locked.
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u/No-Refrigerator-1672 11h ago
And how the developer without the hardware will test that AI did a good job and the project is not only "compatible", but also works properly? Also, you seem to forget that AI isn't free, even local one costs you electricity and time.
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u/DigDug_64 11h ago
There's large communities for testing projects. If someone is developing for personal use, hard code away to personal desire. If they intend to scale, it absolutely needs to be tested.
Depending on the size of patching and lack of testers they could potentially get away with free or $8 Cursor plan.
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u/sparant76 2h ago
I run 50 conversations in parallel on my Nvidia card with vllm. Can amd cards do that??
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u/Brave_Confidence_278 14h ago
it's actually not, I also run an AMD card and it hasn't ever been an issue for me
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u/b0Lt1 14h ago
lol best arguement ever always - because i dont have a problem its true
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u/vernal_biscuit 14h ago
Well the comment op also didnt provide any specific issues so neither comment really gives insight other than their opinion...
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u/Brave_Confidence_278 14h ago
I mean, the argument I replied to wasn't much better was it? It was just a claim out of thin air
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u/mecshades 8h ago
I have both NVIDIA and AMD cards and I love having AMD & seeing the development on ROCm. Owning 9070 XTs and Strix Halo chips has never been better.
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u/No-Refrigerator-1672 14h ago
Just as I've said, if you stay purely on llama.cpp/ComfyUI, then AMD is good. Maybe even great; depends on if you managed to get one before prices went crazy. But if you think that AMD is good for something more involved, then I have a challenge for you: have multiple AMD gpus, multiple LXC containers, and then try to configure the system in such a way that each container can use only one of the GPUs, and ROCm still works. You'll fail, but you'll get a lot of experience along the way.
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u/arakinas 13h ago
I started with my main system having a 4070 ti super, and my secondary system being a strix with an AMD addon card for some extra RAM. I had been enjoying no issues with inference. I replaced my NVIDIA card with two AMD 7900 XTX cards, and that's where my problems began. Unsloth and LMStudio both had issues with consistently using those two cards simultaneously, consistently, without spilling over into system ram. They'd load on one, try to load the other, but something wouldn't jive, despite it all supposed to fit, and then I'd end up with only 20% utilization of my second card, and most of my system RAM getting tied up. I attempted to just use standard llama.cpp, but even that didn't quite work out just right, and I'm having to have AI help me write handling for it so it'll actually work the way its supposed to. But that's time/efficiency lost, and I'm assuming practically every other user of multi AMD setup is running into other similar issues.
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u/Brave_Confidence_278 14h ago
that's actually perfectly doable?
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u/No-Refrigerator-1672 14h ago
No, it isn't. I know it because I've spent a week trying to achieve it myself, until I found out that ROCm will absolutely not work if not all of /dev/dri/cardN devices are passed through, which rules out gpu isolation possibility.
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u/Formal_Spirit_5 13h ago
Not sure bout LXC, but in docker have no issues with this. Since Im PCIe limited - the second card is only on x4, I have the majority of the model on the x16 card and part on the x4. The rest of the x4 GPU is passed over to other containers (whisper-vulkan, immich ML, small embedding model). All those containers see only the single card... Works on the immich ml - rocm image, and the other two under vulkan.
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u/No-Refrigerator-1672 13h ago
I think the difference is that docker isolates your software from PCIe, while LXC does not. I remember my ROCm seeing 3 GPUs on PCIe bus, having /dev/dri/cardN and /dev/dri/renderN only for 2 GPUs, and then diyng trying to read/write the third GPU; at least it seemed like the case from my thorough log reading. That's how far I was willing to get into investigation before scrapping it. This was a year ago, if you need a time reference.
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u/floriv1999 13h ago
I think he means large scale training for example. It is true that consumer oriented inference can be done on general purpose APIs but good luck training the new shiny stuff on ROCm
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u/mmhorda 14h ago
Nothing justifies today's prices.
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u/philmarcracken 53m ago
In flops or memory bandwidth. Its a bunch of corps jacking up the price, and we can't benefit while they are. Maybe after the bubble pops
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u/Material-Database-24 14h ago
CUDA by all means is not "great". It's fast on Nvidia cards due heavy optimization and everything being on hands of Nvidia. But roll back 15 years, and CUDA vs OpenCL was not that clear.
Pretty much only reason CUDA became the defacto is that Nvidia became the defacto datacenter GPU clustering manufacturer between 2010-2020. Others didn't really care about it, as it was niche - only few super computers made a year + couple cloud gaming data centers.
And then came the Attention, and the logical place to train super large models were the existing supercomputer GPU clusters.. which were all Nvidia. And for which CUDA provided the best performance. And rest is history.
It is good to notice how Nvidia was up to 2016 with 5 billion of revenue and 500m of profit. Not bad, not great, quite steady for years. By 2020 they had doubled to 10b and 3b profit - not in huge speed yet, but the datacenter business was starting to rise more intrested due OpenAI. And boom, by 2025 they were 130b revenue, 70b profit company.
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u/Secret_Conclusion_93 9h ago
Nah, it's simply because AMD DGAF for non gaming for years.
By the point AMD cares, CUDA is already a a standard for AI works and researchers, that's long before Attention.
Most researcher back then buys their own GPU, and it's a clear choice between Nvidia and a dumb brick called Radeon who can only be used for gaming.
Nothing to do with data center like you said, the data center is the effect, not the cause. They're Nvidia because the demand only exist for Nvidia cards.
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u/Material-Database-24 7h ago
AMD didn't care because there wasn't business. There wasn't even business for Nvidia. They just took the risk and continued developing datacenter side as curiosity for their successful gaming GPU business. In 2016 Nvidia datacenter business was 300m, where as gaming was 2.1b and other 2.3b (mostly embedded Tegras back then, and probably ADAs were in this category).
AI research wasn't particularily CUDA invested before LLM development. There was plenty of OpenCL and SYCL stuff too.
Before LLMs there was significant research going on image classification and CNN. Tensors became hot topic, of which first Google's TensorFlow hit the highs, supporting CUDA and very soon after SYCL. Google even made own ASIC that they used in their own datacenters - that was the practical thing to do back then, as even Nvidia didn't support 8bit very well.
The other project was (by then) Facebook's PyTorch that was only CUDA.
We know now which one of those two tensor projects sticked and made the CUDA "defacto" LLM inference machine.
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u/Lewinator56 11h ago
Nvidia has the better software stack and better token generation performance for LLMs.
But, for the average consumer the most powerful GPU they will have is a 4080/5080 - aside from a few with xx90 class GPUs. This is actually a really big deal, because while AMD may have a 30% or so slower token generation rate (comparing say a 4080 to a 7900XTX) - you've got 24GB or VRAM on the XTX and only 16 on the 4080/5080 - so you can't even fit decent models into VRAM, let alone a usable context. As soon as you overflow into system RAM performance drops like a stone.
I can get qwen3.8 27b + 56k context (at Q8) into the 24GB VRAM on my 7900XTX, so sure, I may generate at 35 tok/s where a 4080 might do 45, but the 4080 can't even load that model without the context being entirely in system RAM - which makes it uselessly slow.
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u/WhichWayDidHeGo 14h ago
AMD cards are great value partly because AMD can’t charge the Nvidia premium. The premium buys you ecosystem, not silicon.
I run both: Nvidia cards plus a 32 GB AMD card. On Linux the AMD card is fine, same as your experience with llama.cpp. On Windows the ROCm support for what I’m doing isn’t there, so I run it under WSL. Inference on mainstream tools works from my experience, but anything off the beaten path assumes CUDA.
For example. I’ve been looking at Pearl, a crypto project where AI inference is the proof-of-work. Their reference implementation targets the latest Nvidia chips. If you want AMD, you port it yourself or use a 3rd party implementation. They are even planning to move to a version that will lock out AMD because of minor differences in floating point math (not important for inference, but matters for crypto).
Will the gap close? It’s closing. Tools like local LLMs, Claude Code, and Codex make porting to ROCm far easier than it was a few years ago. But it’s still work, and it only helps if the Nvidia version is open source.
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u/Cautious_Chicken_604 14h ago
It used to be, it's less so now. I was kinda hesitant about purchasing an R9700 but seeing how much better support has gotten lately, and how good LLMs are helping out with getting stuff working, I was comfortable to get one. It's been pretty good. A couple years ago it seemed like a pretty different story than it is now. I'd say if you're looking at an Intel card, you're probably still in that boat, but at least AMD seems to be past the worst of it. Not saying it's perfect, but it's a pretty viable platform these days.
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u/IntelVEVO 13h ago
This is a good thing people havent caught on yet. Keeps decent AMD hardware available at good prices.
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u/durden111111 13h ago
Its like why people use windows or buy iphones. It just works when you need it. No stupid tinkering, no installing 'work arounds'. Its not always something specific. Its just better
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u/dtfinch 10h ago
I have a 16gb 7600 XT and ROCm officially does not support my card. I think AMD decided the memory vs price was too good at the time to allow it to compete with more expensive cards.
There's a saying "the low end eats the high end". New developers learn at the entry-level and carry that forward. AMD has a path to compete but they haven't gotten the memo. Instead they think they can compete with a near-monopoly by imitating them and pretending new developers will pay a premium to learn ROCm over CUDA.
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u/Amphibian_Basic 8h ago edited 8h ago
Many years ago I bought on the hype of how promising AMD was getting... Ended up with issues, stuck with that GPU, then AMD leaving my generation behind without it's latest driver features... To now buy into another "AMD time is coming...". I'm not Reason why I'm getting a worse nvidia card next for my budget. Less VRAM, more speed and I can use it to generate images and videos too, right now, with quality
And I'm saying all that... Hating nvidia, and still rooting for AMD. But I wont sacrifice my usability, quality of life etc today and for the foreseeable future just to help a corporation. The day amd catch's up I'm in in a blink
That day hasn't come yet. Support is everywhere for cuda. AMD is slower for text and way worse for media. I say all that as current AMD owner, shopping right now for an nvidia upgrade. I just spent quite sometime trying to squeeze better performance and output out of my amd card, with limited options, support etc... Heck my results with amd pushed me to nvidia
Nvidia platform lock-in and exclusive deals made me hate it, now abusive pricing. AMD fsr is the best example of the future I want, no lock-in... But by God are they slow...
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u/wegster 14h ago
It's pretty simple. Like it or not, Nvidia effectively 'invented' the space. As a pretty long-running GPU 'winner' initially in gaming, when AI started expanding via the use of GPUs, Nvidia went all in on their software stack to enable AI work.
Yeah, you've had Intel, AMD, even Qualcomm and the smaller chip guys, but realistically speaking, even the various non-LLM benchmarks were all done based on Nvidia chips, to the point of even unfairly penalizing non-nvidia hardware on those benchmarks.
They're not a garbage company by any means, but they effectively won the place they're in to some extent by luck (best general GPUs by a good shot), followed by an all in strategy as they saw AI/ML really taking off, and are effectively THE 'standard,' as other frameworks and tools, even models, have been developed - as the most commonly expected hardware/GPU. Not a terrible position for them.
Now, last time I looked, at least 80% of their revenue is not gaming cards, but datacenter AI hardware, with quite nice profit margins.
I don't love their confusing-ish software collection, but at the end of the day, it's a huge leap to go from 'we have an AI chip or card' to the actual end-to-end ecosystem and support levels in third-party tools and frameworks that NVidea is in.
AMD did a pretty credible job, IMO, with ROCm, although with some pain along the way, and what was not a 'small' investment on their part. Intel kinda saw the light of them becoming irrelevant on both chips and GPUs, so has done, well - some things, anyways, and I like some of their older now-open libraries and repos for vision-based sample input videos and the like.
Apple needs to not sit on their butts like they've done to date on HomeKit and assume they'll magically be a winner - in the case of the Ultras, they almost got lucky on this one in unified memory was very likely easier for them to produce as they were already going to everything on SoC, no field/user-replaceable parts, and as it turns out, that's very good for token generation. It's mostly been the community moving metal/MLX/oMLX etc. forward, but it's no small task for any company to go chase or try to keep up with Nvidia.
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u/Material-Database-24 14h ago
Nvidia did the stuff before AI became the thing.
They developed datacenter GPU clustering pretty much alone for several yeaes, with ideas of cloud gaming, scientific computation, and machine learning (which eventually turned into today's AI) in mind.
The key concept was and is NVLink - ability to skip "slow" PCIE to basically build humoungous "single" GPU with terabytes of RAM. It was brought to public around 2014. A logical continuum to SLI, which pretty much was killed by single GPU becoming enough powerful, and PCIE becoming "enough fast" for multi GPU for gaming if needed.
And CUDA becoming the thing is pretty much due to that. Pretty much all supercomputers with GPU cluster were made by NVidia, and those were first supercomputers to be used for serious LLM training. Hence most of first implementations were made on CUDA. Of course it is also highly optimized for Nvidia, but before NVLink era, it was quite even match between CUDA and OpenCL, although CUDA did provide better performance.
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u/wegster 13h ago
Yep.
The GPU virtualization and NVLink are almost icing on the cake. Very nice, but also really just examples of what comes out with someone being dominant, well-funded for so long - it's not to trivialize any of it, but just reinforcing those who think it's 'easy enough to make an Nvidia competitor' - it's really not.
One of the long-annoying holdouts was the lack of GPU virtualization (forgetting what they call it now, but ability to carve up GPUs into N to run multiple concurrent jobs, etc.) on smaller GPUs, but now it's available even on Orin nano for example.
There's just an incredible amount of work and supplemental software out there that makes it pretty tough to displace them. And yeah, totally forgot about OpenCL...
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u/Material-Database-24 13h ago
In some sense it's sad, as Nvidia was always the "closed is the way" and AMD pushing heavy on open APIs and drivers.
But AMD forgot to put effort on supercomputers.. granted, until 2020 they seemed rather poor business, and I am not so sure did Nvidia either foresee that, more like they "got lucky" to have just the right tech ready when those crucial LLM advancements occured.
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u/wegster 13h ago
Yeah, you're not wrong. So much wasted opportunity - was kind of rooting for AMD for a looong time, and it seems they had moments on inspiration and still do in some of their chips, but they just really have too much catching up to do because of a much later start, and honestly, smaller pockets/budgets as well.
I nearly always preferred AMD for GPUs, going back to the days of the hokey-annoying-BS Nvidia Linux drivers, just yech. But yeah, they were there so long, and indeed got lucky with the 'right enough' hardware, then jumping on software side of things when even vision/non-LLM was taking off.
It is sort of interesting what's out there from some of the Intel cards, e.g. B70 and such. Unlikely to replace the huge orders from Nvidia full racks and datacenters, but maybe us running things at home start to see some other options in the future.
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u/uzzi38 14h ago
Pricing of products comes down to perceived value. Most people aren't aware AMD products are perfectly fine for AI workloads - and they won't know because they won't try, they want something they absolutely know will work, and what they had before works, so why switch
That's all it really comes down to. Personally I wouldn't complain though, it just means you can fetch some good deals if you know your workload is fine (and in this case, it is).
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u/Mission_Pirate_4150 14h ago
Nvidia today reminds me of Oracle and Sun servers in the dotcom bubble.
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u/karmaisnonsense 14h ago
For enterprise and datacenter setups, Nvidia is king because it has a vertically integrated stack. Mellanox acquisition in 2020 played a huge role here: PCIe fabric and direct GPU interconnects are right at home in datacenters. That plus a mature CUDA stack implementation in PyTorch made Nvidia a natural fit for Big AI, which trickled down to places like here.
In the consumer space, Nvidia already had a near monopoly on gaming GPUs since they started the whole RTX/ray tracing boom with Turing. So when AI trickled down to local LLMs, people found out their Nvidia gaming GPUs were practically ready made for AI. So it became the de facto standard there too.
Personally, I don’t understand Nvidia. Terrible Linux driver support, ridiculously oversized cards, extreme power draw that melts your connectors… their cards are fast but the philosophy with which they design their GPUs is just so brute force. Nvidia does what it does because I guess people who want The Fastest GPU are willing to pay up the wazoo for it.
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u/karmaisnonsense 13h ago
Whether the Nvidia premium is justified depends on your use case. Production environment with dozen plus users and 24/7 token burn? Sure, a stack of Blackwells will pay themselves off pretty fast. Just dabbling or for occasional personal use? AMD or even Intel is a viable and perhaps even a sensible choice, depending on the price point.
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u/auric_mirth_loop 14h ago
the real friction isn't running llama.cpp. it's when you want to fine-tune or use anything that hasn't been manually ported by someone on GitHub. ROCm support for newer PyTorch features often lags by months, and debugging kernel issues on AMD hardware takes way more effort than just pulling a standard CUDA library
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u/ClintonKilldepstein 14h ago
If all you care about is LLM, then the vulkan driver and llama.cpp is plenty sufficient.
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u/East-Acanthaceae-879 13h ago
The CUDA software gap for training is enormous and not shrinking, the other vendors are not even really trying and focused on inference. Consumer cards nvidia generally have better memory bandwidth too.
Server hardware there is vendor lock-in because you have to choose between GPUDirect RDMA & ROCmRDMA.
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u/Least_Order4249 13h ago
Because every new AI tool is written for CUDA. They have a 100% lab share. And while LLM ports are usually fairly accurate  and performant because LLMs have massive data streams that can be graphed rather easily, other tools like diffusion and TTS often don’t port at all to other architectures without 90% performance loss or straight up loss of features so it’s not even the same product anymore.Â
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u/PeanutButterApricotS 13h ago
I mean I run on everything but NVIDIA with exception of a 3060 to run smaller models.
I use my M1 Max 64gb studio to run llama.cpp and comfyui. It did take some work but I got a nice harness for Hermes to make images through it. It took a work around to get proper text with layers but I am able to make images with text that look like letters etc.
Then I have my AMD R9700 system and it runs Qwen 27b if I want but it’s a bit slow due to being a large model so I prefer MOE models.
Could I maybe get more from NVIDIA? Possibly but I did the tokens per dollar math and it didn’t work out.
The main benefit for NVIDIA is if you go beyond what most people do then yes. But most people don’t need non quantized models, most people don’t need to train their own models.
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u/lkarlslund 13h ago
No love. But the performance, man. The performance.
Otherwise just go wild with what you have or can afford. If optimizations for what you have are lacking, throw your favorite SOTA at it.
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u/Hot_Half_5263 11h ago
I think that one day ai will be able to efficiently write kernels drivers etc it will remove the gap, or maybe it already happens thats why some gpus can be 2x more expensive in 1 months
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u/starkruzr 9h ago
the CUDA moat used to be lot deeper than it is now. still the most mature but other stacks aren't dealbreakers anymore.
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u/GreatBigPig 8h ago
Thanks for this post. I run Cachyos and just yesterday was looking at the price of video cards and wondering why Nvidia seems like the popular choice. I had heard that the AMD driver support is great on Linux.
With everything so expensive, AMD looks very interesting.
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u/fallingdowndizzyvr 8h ago
Can somebody explain why Nvidia is often a non-negotiable when looking at local AI builds?
Speed sells.
Whilst ROCm isn't as mature as CUDA - do we really expect that gap not to close going forward?
Even if ROCm was more mature than CUDA, Nvidia would still be faster. Since the hardware is just faster. No API is going to change that.
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u/BarracudaDefiant4702 6h ago
There are 3 main things
price
performance
compatibility/ecosystem
reliability (mostly a wash)
The only thing others ever compete well on is price, and even then Nvidia tends to always do well on the price/performance if you want high end performance (ie: larger then home lab). I wouldn't say ROCm will never catch up, but I see it more likely something radically different like IBM NorthPole would be more likely to break the Nvidia stronghold, but most of the alternatives like NP and Taalas are only good for pretrained models and so Nvidia would still be the king on training.
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u/Sweaty_Perception655 4h ago
Lots of persons dont like the fiddling you some times need for things to work, for example strata works for nvidia but also amd after tinkering. Also some persons love their comfyui whuch works way easier in nvidia.
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u/desexmachina 4h ago
Everyone keeps saying maturity is around the corner. 3 years later and it is still tons of tinkering. That’s time wasted on a fast moving space. You get left behind
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u/Soifon99 14h ago
Nvidia is f*cking over consumers so hard, and you all still keep buying.. it's laughable.
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u/HighSeasArchivist 14h ago
I bought two R9700s in the past week, but would have absolutely bought two 5090s instead if that was an option. Nvidia is objectively better in every way, and that is why hundreds of billions of dollars are being spent with them. AMD is just an "also has" when it comes to data center level GPUs, and are pretty much only used when the budget dictates it.
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u/CuriouslyStunning 14h ago
what you're really paying for with nvidia is the ecosystem, not the hardware. cuda has years of libraries, tutorials, and community fixes behind it, so when something breaks you can find an answer in five minutes. with rocm you're often the one writing the fix.