Speculative Decoding in vLLM on AMD GPUs

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.

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.

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

> ...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.

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.