I'd rather buy two used rtx3090 than a single r9700 AI pro. More VRAM (some wasted due to it being non continuous), more RAM bandwidth, more aggregate compute.

Only if AMD made a card like this with 48G+ I'd consider it.

Also these 20-30t/s jumping to 150-200... Watch out for the massaged numbers coming from vendors.

I believe Intel has claimed something like 1400tok/s (generation! Not prefill) of Qwen3.6-moe on Arc b70.

I was actually very interested in this so I checked the details. Turns out it was 200 simultaneous users running the same 1024 token prompt :D so all the experts got maximum parallelism.

How often are you going to run 200 parallel sessions with a tiny context and same prompt running at 7tok/s.

Based on how much my rtx3090 is getting on a single user (150tok/s) I'm estimating b70 to probably get less than that.

Sadly nvidia is king now.

Also, most of us already have nvidia cards and no inference software supports mixing let's say nvidia, Intel and amd cards in inference of one model.

> ...one of the truly baffling things from either their team or AMDs team, is how much the workstation grade AMD r9700 has been ignored.

It makes a huge amount of sense after considering AMD's approach to graphics cards from around 2010 to 2025. They just didn't see graphics cards as viable compute platform and many who made the mistake of believing that good specs would translate into in-practice performance got badly burned. I'd have been involved in the AI boom but for an expensive AMD graphics card, I'm not going to forget that for a while.

George Hotz was interesting as a public example, but I think his story probably repeated a few times outside the public eye. People tried to make AMD work and ended up the worse for it.

People who had an interest in using AMD cards to get things done are probably by and large waiting for a new generation of hopefuls to prove this time is different. The mutterings out of AMD are promising, but that isn't persuasive enough given the scale of the failures.

Thankfully, there are still people willing to jump on the R9700 bandwagon and get a vLLM fork working. If you have an RDNA4 card check out https://hub.docker.com/r/stilldeadcode/vllm-radiance

I don't see a reason why it should AMD doesn't care about lower end prosumers atm.

They might in the future but future is in the future ofc

Edit: to be clear I think it's ridiculous they don't but from a company's stand point it doesn't make much sense

George Hotz in June 2023:

> I have had direct contact with members of the AMD RTG team and I was disgusted to find that AMD doesn't even provide them with hardware to work on. The developer I was working with had to buy the GPU he was writing drivers for.

You got an article off by one error, I think you meant to post on https://news.ycombinator.com/item?id=49558685 :)

> Naively, I would assume it must perform its normal auto regressive decoding to know what the “correct” token is in order to have something to compare the candidate token with.

Yes, but you can do it in parallel.

Suppose you predicted the tokens "D E F" in the sequence "A B C D E F". To "generate" the last token (F), it must know all preceding tokens (A B C D E). To "generate" the next-to-last token (E), it must know all preceding tokens (A B C D). And so on.

Assuming the prediction is correct, it can then run the "generation" for tokens D, E, and F at the same time. At the end, after all these tokens were "generated", it compares each token with the prediction; if the "generation" result was "D H F" it knows it has to discard the last two predicted tokens (and output "D H"), if the "generation" was "D E H" it knows it has to discard the last predicted token (and output "D E H"), etc.

And the most important part is that you can do it in parallel for each layer of the model. That is, you run "A B C D E F" through the first layer, then through the second layer, and so on; you only have to load the model weights from memory once for each layer. Instead of reading the full weights for all layers once for D, then once for E, then once for F, you only read them once for "D E F", and if the prediction was correct, you output three tokens by the (memory read) price of one (you still had to do the same amount of compute, but AFAIK LLMs tend to be more memory-bound than compute-bound).

You're correct that it needs to run the full model to "verify" a token, but LLM inference benefits from batching - it's much faster to run twice in parallel than sequentially. So the draft model runs ~2-5 tokens ahead, and the full model then runs ~3-6 batches in parallel using those tokens, and can skip ahead by however many results match.

If you have some other source of parallel data (lots of users, many separate tasks) then speculative decoding might not provide any benefit.

The target model is the original LLM that is large and expensive. It can verify candidate tokens in a single forward pass. It means you give all the context + candidate tokens that passes in parallel in the backbone, then you pass the language head (a matmul transformation to produce the token distribution) on all the candidate tokens and you can keep or drop tokens based on how many "quality" you want.

> it must perform it's normal autoregressive decoding to know what is the correct token in order to have something to compare with

Correct except for the word "autoregressive". When you have to verify a sequence of tokens (which were autoregressively generated by the cheap model), you can do each token in parallel. This amortizes the cost of loading the weights from vram to the processors (the primary cost in LLM serving) across those tokens. Cost here is wall clock time, as well as power.

The autoregressive decoding that generates this batch of tokens is delegated to the cheaper model where the cost of loading the weights is lower and so not amortizing it is fine.

Verification means, how close is each token in this sequence to the one I would have output. You keep the longest prefix that is close enough for your liking.