Just to clarify: this post was not written against Spectral Compute.
Their recent investment news was the trigger for us to finally write it yes, but the idea has been on our minds for a long time.
We actually think solutions like theirs are good for the ecosystem, they make it easier for people to at least try AMD without throwing away their CUDA code.
Our point is simply this: if you want top-end performance (big LLMs, specific floating point support, serious throughput/latency), translation alone is not enough. At that point you have to focus on hardware-specific tuning: CDNA kernel shapes, MFMA GEMMs, ROCm-specific attention/TP, KV-cache, etc.
That’s the layer we work on: we don’t replace people’s engines, we just push the AMD hardware as hard as it can go.
What if that person is not native English and wrote something up and then threw it into chatgpt (or a local chatbot running on 1 MI300x :p) just because he felt that his relatively limited vocabulary would not be enough to express everything?
That person (yeah :p), might just be trying to create as much awareness as possible.
You might get annoyed by the usage of LLM's, some might not.
I get annoyed by people still trying to undermine the testing done while everything is clearly extremely transparant, even the docker image is shared..
That said, the article is about the results, if you'd like to "delve" a bit deeper into those results, let me know, i'd be happy to go over some of the data visualisations ;-)
