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MTS · 2 min · Oct 31, 2026
AMD says its software stack scales from desk to rack
Ian Weinberg says AMD’s same software stack runs from its smallest systems through Helios.
In this episode
Full episode- 9:58Shared memory lets local models exceed 128GB workloadsPro pick 5 min
- 14:43Local agents can turn a prompt into a Blender scenePro pick 3 min
- 18:49Local AI can work offline and route hard tasks to frontier models4 min
- 7:57AMD says its software stack scales from desk to rack2 min
“the same Rockham stack that is installed on the smallest hardware that we have is the same Rockham stack that goes all the way up to Helios.”
Takeaways
- The same stack runs from small systems up to Helios.
- Desk-scale practice can transfer to rack-scale work.
- The platform balances CPU, GPU and NPU workloads.
About this segment
Ian Weinberg says AMD’s same software stack runs from its smallest systems through Helios. Developers can carry skills from desk-scale experiments into rack-scale work.
Ian Weinberg is an AMD representative.
Sophia Dew, Host · Host, MTS
From “Local AI Will Take Over Within One Year | Ian Weinberg”, Oct 31, 2026. Watch the full episode
Thanks. We'll look at and fix what's off.
Transcript
Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.
- 8:01
Great simplification, great report. And that feedback is coming directly from the developer community. So I can stand on other people's backs and say that. But the really neat thing about our stack is the same Rockham stack that is installed on the smallest hardware that we have is the same Rockham stack that goes all the way up to Helios. really fascinating so if someone's coming into the amd ecosystem and you're learning at desk scale going to rack scale it's just it's just a group of skills that you're developing today you're doing local fine tuning laura q laura kind of stuff or you're doing pre-training up in the instinct stack your learning is one yeah curriculum i could see a version where you're a freshman in university and you get one of these and your syllabus and you're early i don't maybe not
- 8:46
Exactly. I don't know. You're going to borrow it with your classroom and you understand how this works. So in theory, that same skill set now when you graduate and you're now operating like rack scale systems, you have that muscle and you can do the same thing there. By the way, that sounds like the coolest path for university. I know that is really cool. That would be awesome. If you're a university and you want to get some of these for your classroom, let us know. 100%. That'd be great. Yeah. So what I can do now is I can actually talk through this. So what we're going to do is a bit of a capability demo and a capacity demo. Cool. Because when I talked about those three things this platform represents a balance that we have built and that balance is one is fit fit means does it fit into the memory signature the second one when you're talking about orchestration
- 9:26
is as we're transitioning into the agentic world and agents are just a whole ton of agents that are moving very quickly the cpu becomes very important because it's doing tool calls it's doing encode decode it's doing a whole bunch of stuff that is traditionally traditionally historically ai would be done gpu but it's being done cpu so that's where you get into the orchestrate stream which is the third component is the gpu and also the npu because small models run really well in npus now so we built this box with that balance in mind all of those capabilities so