I'm sorry that's just not correct. Intel is literally just getting started in the GPU market, and their last several releases have been nearly exactly what people are asking for. Saying "they've lost" when the newest cards have been on the market for less than a month is ridiculous.
If they are even mediocre at marketing, the Arc Pro B50 has a chance to be an absolute game changer for devs who don't have a large budget:
https://www.servethehome.com/intel-arc-pro-b50-review-a-16gb...
I have absolutely no doubt Nvidia sees that list of "coming features" and will do everything they can to kill that roadmap.
To that point, they've been "just getting started" in practically every chip market other than x86/x64 CPUs for over 20 years now, and have failed miserably every time.
If you think Nvidia is doing this because they're afraid of losing market share, you're way off base.
They've been making discrete GPUs on and off since the 80s, and this is at least their 3rd major attempt at it as a company, depending on how you define "major".
They haven't even just started on this iteration, as the Arc line has been out since 2022.
The main thing I learned from this submission is how much people hate Nvidia.
I think there's a lot of frustration with Nvidia as of late. Their monopoly was mostly won on the merits of their technology but now that they are a monopoly they have shifted focus from building the best technology to building the most lucrative technology.
They've demonstrated that they no longer have interested in producing the best gaming GPUs because those might cannibalize their server technology. Instead they seem to focus on crypto and AI while shipping over priced knee capped cards at outrageous prices.
People are upset because they fear this deal will somehow influence Intel's GPU ambitions. Unfortunately I'm not sure these folks want to buy Intel GPUs, they just want Nvidia to be scared into competing again so they can buy a good Nvidia card.
People just need to draw a line in the sand and stop supporting Nvidia.
224 GB/s
128 bit
I love GPU differentiation, but this is one of those areas where Nvidia is justified shipping less VRAM. With less VRAM, you can use fewer memory controllers to push higher speeds on the same memory!
For instance, both the B50 and the RTX 2060 use GDDR6 memory. But the 2060 has a 192-bit memory bus, and enjoys ~336 GB/s bandwidth because of it.
My RTX 5090 is about 10x faster (measured by FP32 TFLOPS) and I still don't find it to be fast enough. I can't imagine using something so slow for AI/ML. Only 2.2 tokens/sec on an 8B parameter Llama model? That's slower than someone typing.
I get that it's a budget card, but budget cards are supposed to at least win on a pure price/performance ratio, even with a lower baseline performance. The 5090 is 10x faster but only 6-8x the price, depending on where in the $2-3,000 price range you can find one at.
Its also orders of magnitudr slower than what I normally see cited by people using 5090s; heck, its even much slower than I see on my own 3080Ti laptop card for 8B models, though usually won’t use more than an 8bpw quant for that size model.
> The 5090 is 10x faster but only 6-8x the price
I don't buy into this argument. A B580 can be bought at MSRP for 250$. A RTX 5090 from my local Microcenter is around 3250$. That puts it at around 1/13th the price.
Power costs can also be a significant factor if you choose to self-host, and I wouldn't want to risk system integrity for 3x the power draw, 13x the price, a melting connector, and Nvidia's terrible driver support.
EDIT: You can get an RTX 5090 for around 2500$. I doubt it will ever reach MSRP though.
the intel card is great for 1080p gaming. especially if you're just playing counterstrike, indie games, etc, you don't need a beast.
very few people are trying to play 4k tombraider on ultra with high refresh rate.
I've been using Mistral 7B, and I can get 45 tokens/sec, which is PLENTY fast, but to save VRAM so I can game while doing inference (I run an IRC bot that allows people to talk to Mistral), I quantize to 8 bits, which then brings my inference speed down to ~8 tokens/sec.
For gaming, I absolutely love this card. I can play Cyberpunk 2077 with all the graphics settings set to the maximum and get 120+ fps. Though when playing a much more graphically intense game like that, I certainly need to kill the bot to free up the VRAM. But I can play something simpler like League of Legends and have inference happening while I play with zero impact on game performance.
I also have 128 GB of system RAM. I've thought about loading the model in both 8-bit and 16-bit into system RAM and just swap which one is in VRAM based on if I'm playing a game so that if I'm not playing something, the bot runs significantly faster.
I don't know exactly how the scaling works here but considering how LLM inference is memory bandwidth limited you should go beyond 100 tokens/sec with the same model and a 8 bit quantization.
Clearly I'm doing something wrong if it's a net loss in performance for me. I might have to look more into this.
If you're using llama.cpp run the benchmark in the link I posted earlier and see what you get; I think there's something like it for vllm as well.
Preempting a (potential) future competitor from entering a market is also an antitrust issue.
Yes.
> Other than the market segmentation over RAM amounts, I don't see very much difference.
The difference between CDNA and RDNA is pretty much how fast it can crunch FP64 and SR-IOV. Prior to RDNA, AMD GPUs were jacks of all trades with compute bias. Which made them bad for gaming unless the game is specifically written around async compute. Vega64 has more FP64 compute than the 4080 for context.
I think if AMD was able to get a solid market share of datacenter GPUs, they wouldn't have unified. This feels like CDNA team couldn't justify its existence.