Michael Søndergaard retweeted
AMD Instinct™ GPUs are incredible. A single AMD Instinct™ MI355X sports 𝟮𝟴𝟴𝗚𝗕 𝗼𝗳 𝗛𝗕𝗠𝟯𝗘, 𝟴𝗧𝗕/𝘀 𝗼𝗳 𝗽𝗲𝗮𝗸 𝗺𝗲𝗺𝗼𝗿𝘆 𝗯𝗮𝗻𝗱𝘄𝗶𝗱𝘁𝗵 𝗮𝗻𝗱 𝟳𝟴.𝟲 𝗧𝗙𝗟𝗢𝗣𝘀 𝗼𝗳 𝗽𝗲𝗮𝗸 𝗙𝗣𝟲𝟰.
Put eight in a node and you have 2.3TB of HBM3E: enough to hold an entire frontier-scale model in memory on one machine. For accelerated HPC, it means bigger simulations resident on a single GPU, bandwidth-bound kernels with space to breathe, and serious double precision.
SCALE compiles supported CUDA code to run natively on it, cutting the effort required to move existing applications.
Our compiler is built to use what the hardware offers. One example: AMD GPUs can pass data between threads through DPP, hard-wired lane permutations that cost almost nothing. SCALE recognises the patterns in ordinary CUDA shuffles and maps them onto DPP automatically, so your code takes the fast path without a line of AMD-specific code.
The silicon was always capable. What held CUDA workloads back on AMD was getting the code there without leaving performance behind on the way. That is a compiler problem, and compilers can be fixed once for everyone.
We are now listed in @AMD's Instinct AI & HPC Software Ecosystem Program!
amd.com/en/ecosystem/isv/dat…
Michael Søndergaard retweeted
A group of #CUDA experts at Spectral Compute are looking to unhook the language from #Nvidia and allow users to run their CUDA code on other chips. ow.ly/nWPz50ZnBV3
You can't have portability AND peak performance. That's the assumption people make.
They're right if you rely on portability by abstraction at runtime. But that assumes a cross-vendor system must be a "lowest-common-denominator" system.
Generality is free when your abstraction sits at the right level. Dive into Part 4 of my series, "Why hardware-agnostic isn't the same as lowest-common-denominator": scale-lang.com/posts/2026-06…
Have I not managed to convince you yet?
Come hang out in our Discord to keep the debate going. It's where we talk compilers, GPU programming, and other nerdy things with our dev team: scale-lang.com/s/discord?utm…
Michael Søndergaard retweeted
𝗦𝗽𝗲𝗰𝘁𝗿𝗮𝗹 𝗖𝗼𝗺𝗽𝘂𝘁𝗲 𝗶𝘀 𝗻𝗼𝘄 𝗽𝗮𝗿𝘁 𝗼𝗳 𝘁𝗵𝗲 𝗡𝗩𝗜𝗗𝗜𝗔 𝗜𝗻𝗰𝗲𝗽𝘁𝗶𝗼𝗻 𝗽𝗿𝗼𝗴𝗿𝗮𝗺. 🟩
Inception is @nvidia's program for AI startups - a membership that gives access to technical resources, preferred pricing on NVIDIA hardware and software, and exposure to a global network of investors and partners.
CUDA is the de-facto standard for AI developers, and we’re honored to play our part in growing the ecosystem.
Michael Søndergaard retweeted
From CUDA portability to real-world performance on @AMD GPUs, @SpectralMichael, CEO of @SpectralCom shares why open, portable AI infrastructure matters more than ever.
Watch the short interview from Beyond Summit 2026 on our YouTube channel: youtube.com/watch?v=cwmiGYas…
Everyone says CUDA can't target TPUs.
What they mean is nobody has written the compiler that raises CUDA code to something a systolic backend can consume.
Those are very different sentences.
Full post — Part 3 of why @SpectralCom exists: tinyurl.com/mtnxcjsw
Michael Søndergaard retweeted
𝗨𝗽 𝘁𝗼 𝟮𝟱.𝟳× 𝗳𝗮𝘀𝘁𝗲𝗿. Unmodified CUDA. AMD silicon.
Thanks to the @tensorwave team for benchmarking SCALE on MI355X and publishing the numbers.
Port to AMD used to mean a rewrite. Now it means a recompile.
Spectral Compute (@SpectralCom) used TensorWave’s AMD-native infrastructure to benchmark CUDA portability and performance on @AMD Instinct™ MI355X GPUs.
See how they did it - tensorwave.com/blog/spectral…
Obvious take: if agents can write native code for any GPU, who needs a portable CUDA toolchain? Just point the model at each GPU. I think that's exactly backwards — and wrote up why. tinyurl.com/5ac6y5jt
CUDA isn't a standard. No consortium, no ratified spec. It's whatever NVIDIA ships next. That's supposed to be a weakness. It's why CUDA wins. The cross-vendor layer can't be a consortium either. It has to be a company. That's what we're building at @SpectralCom. SCALE compiles unmodified CUDA on AMD and NVIDIA today. No PDF to argue about — a toolchain. Full piece: [bit.ly/3PBsYsU](bit.ly/3PBsYsU)
Michael Søndergaard retweeted
AI compute is bottlenecked. When the hardware mix inevitably diversifies, how will production clusters actually operate?
Our CEO @SpectralMichael is joining @AkashBajwa96's Gradient Descending roundtable this Wednesday to dig into the evolving hardware landscape.
Michael Søndergaard retweeted
What does the AI cluster of the future look like?
Hint: it won't just be a wall of B200s.
Our CEO Michael Søndergaard is joining @AkashBajwa96 for the next Gradient Descending roundtable to discuss ASICs, non-NVIDIA chips, and breaking the software lock-in.
🗓️ Feb 25 | 8:30 AM GMT 🎟️ Request invite: luma.com/f7s69jj2
#AI #HPC #DeepTech #AMD #NVIDIA #ASIC
