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.
> ...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.
You can get those numbers with https://codeberg.org/ggz14/radiance-vllm-mxfp4. I also can get it on a single R9700 but the 75 ~ 80 t/s is only peak acceptance of very predictable tokens like coding or json, and averages lower for prose. It's still much faster than regular llama.cpp.
Lemonade is one of the worst performing options. Run any modern Linux distro and ask your current LLM to setup llama.cpp with dflash2 for you as an unprivileged container running from a systemd user unit.
Obviously only on a system you do not trust at all.
GCN was such a promising compute architecture, AMD even pioneered stuff like async compute and compute shader heavy rendering pipelines, only to never seriously go beyond that on consumer gear.
I agree with your assessment that the story of supporting the competition, only to get burned, has repeated many times with AMD outside the public eye. It's why I don't put much stock in claims that things work great as long as specific flags are used.
Similar here peak ~250 and down to ~120 as it gets close to 128k (which is where I set DSH compaction) though it can readily do 256k.
I just got DeepSeek Harness (DSH) set up with 2x R9700 and it's rather mind blowing that these can do actual work and quickly. Up until now I've always been evaluating and searching for better hardware/model/tweaks. This is much more than I even hoped for and considered getting extra 3090/4090. Now I can stop looking/tweaking and start using it for all the different things I've yet to discover it's good for. I do plan to also try/use Hermes and Pi. DSH is annoying that every plugin install/remove requires a restart--given that "everything's a plugin".
Thanks! Didn't expect to see this here. Exactly what I needed to run Qwen3.8-27B-Quark-AWQ-MXFP4-native.gguf as well as other experiments on one or 2x R9700's (I hope).
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'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.
2x RTX 3090 is not enough for proper use. ideal is 64GB+ so that you get proper cache and 256k context with good enough models. (e.g. Qwen 3.8 27B with MXFP4).
And that is tight already.
You would need 3x RTX 3090 - but then the PCIe bandwidth comes an issue if you really want tensor parallelism with three cards. 3x PCIe5 x16 is not cheap with direct CPU access.
So surprisingly, 2x r9700 starts be a nice deal.
> Also these 20-30t/s jumping to 150-200... Watch out for the massaged numbers coming from vendors.
Well, luckily these are not vendor numbers. Prefill also scales almost linearly with the amount of GPUs.
I don't see how. Even on 32GB you can run Q6_K_XL quant with MTP at 200k context k=q8_0, v=q5_1. So 48GB VRAM is good enough to run Q8 at long context. Also with tings like ninfer and it's various forks I'm seeing people get very good performance out of Qwen3.8 models on all sorts of NVIDIA cards.
You also want prompt/prefix cache. Otherwise there is a lot of duplicate prefill processing if you fork conversation, have a different chat window or anything like that. It therefore also makes subagents much faster.
> 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%.
AMD also deprecates cards and throw in wrenches for cards that are hot in used markets. Latest ROCm kind of works on MI50 but requisite files are taken out of just standard Ubuntu installation. They truly don't understand marketing.
They are actually great at LLMs but you need to invest in keeping up with community tuning efforts. But I am fine with most thinking they are bad at LLMs, because I keep buying more of them!
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.