This is way outside my expertise so might be a dumb question, but how does the target model verify candidate tokens? 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. But obviously that would defeat the purpose of speculative decoding so there must be some other way.
Also, what is the difference between “target model” and “target-model,” if any? I feel like half the instances of that phrase included the hyphen and half didn’t.
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 also add that the main overhead of doing anything sequentially on a GPU is fetching memory and launching kernels; synchronizing data too. It quickly adds up. You generally want to combine everything into one large, well optimized pass to avoid those overheads. In my inference engine, I was able to speed up inference by almost 4x just by fusing kernels, batching data as much as I can, loading into fast memory once and reusing it, etc. without changing the algorithm itself. Speculative decoding is based on the similar principle, that if we know several highly probable tokens in advance (from a smaller draft model), the larger model can verify them in a single well-optimized forward pass.
Whilst this is an excellent post from vLLM, one of the truly baffling things from either their team or AMDs team, is how much the workstation grade AMD r9700 has been ignored.
Stock vLLM runs so slowly on these cards compared with vLLM forks like Radiance. Going from say 20-30t/s gen, to 150-200t/s
Most of AMD/vLLM work seems to be around their data centre cards, or the AMD AI Halo/Ryzen and ignores the R9700 AI Pro.
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.
Yeah, as a business AMD should first care about getting their DC grade hardware optimized for inference workloads. It's unfortunate that most of HN discussion has devolved to me-ish.
> 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.
An NVIDIA consumer GPU sells for 50+% or more than an equivalent AMD GPU. Because people are buying NVIDIA GPUs to run local models instead of AMD ones.
I did the same thing, I paid 50% more to get an 5070 Ti instead of the equivalent AMD.
This is probably good for gamers, AMD GPUs are not price inflating to the same degree as NVIDIA, because they are bad at LLMs.
> That was the reason for comparing them in the first place: based on performance, they are direct competitors, or at least they are meant to be. However, as things stand today, there is a massive price divide between the two, with the RTX Ti GPU now commanding a premium of more than 50%.
Also, what is the difference between “target model” and “target-model,” if any? I feel like half the instances of that phrase included the hyphen and half didn’t.
Stock vLLM runs so slowly on these cards compared with vLLM forks like Radiance. Going from say 20-30t/s gen, to 150-200t/s
Most of AMD/vLLM work seems to be around their data centre cards, or the AMD AI Halo/Ryzen and ignores the R9700 AI Pro.
Really wish this would change.
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.
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.
The MXFP4 fork is excellent too. Its my daily driver right now. https://codeberg.org/ggz14/radiance-vllm-mxfp4
Also has PARO quant support there too (early stage)
Also speedups in both repos for 4x R9700s
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
> 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.
An NVIDIA consumer GPU sells for 50+% or more than an equivalent AMD GPU. Because people are buying NVIDIA GPUs to run local models instead of AMD ones.
I did the same thing, I paid 50% more to get an 5070 Ti instead of the equivalent AMD.
This is probably good for gamers, AMD GPUs are not price inflating to the same degree as NVIDIA, because they are bad at LLMs.
> That was the reason for comparing them in the first place: based on performance, they are direct competitors, or at least they are meant to be. However, as things stand today, there is a massive price divide between the two, with the RTX Ti GPU now commanding a premium of more than 50%.
https://www.techspot.com/review/3168-geforce-rtx-5070-vs-rad...
9060 XT 16GB seems to have some great performance with gpt oss 20B and others, and works great with their lemonade-server.
https://lemonade-server.ai/
What exactly do you think is running on a Strix Halo?