How dstack is Cutting GPU Cloud Costs 3–7x with Smarter Orchestration

Spectral Compute CEO: Running CUDA on AMD with SCALE

Learn how Spectral’s Scale toolkit breaks CUDA’s vendor lock-in by letting you run the same code on AMD GPUs with zero rewrites. This episode shows how to future-proof your stack without starting from scratch. #GPUCloud #BeyondCUDA #TensorWave 01:05 - How did you get into GPU development? 03:39 - What is Scale? 05:35 - What are the technical hurdles? 06:34 - What are the opportunities? 07:46 - What is Spectral Focused On? 09:14 - How to break down Scale? 🌐 Connect with us: https://www.linkedin.com/in/jtatarchuk https://dk.linkedin.com/in/michaelsondergaard

Automating GPU Kernels: Mako’s CEO on Crossing the CUDA Moat

Waleed Atallah from Mako joins us to discuss automating GPU kernel generation with AI. 03:07 – Why GPU kernels bottleneck progress 04:46 – Three current ways to get kernels 06:02 – LLMs: can they write fast kernels? 07:48 – Mako’s “kernel agent” in action 09:12 – Early adopters & best-fit users 12:05 – Data scarcity limits fine-tuning 16:18 – AI-written kernels by 2030? 20:27 – Top technical risks ahead #BeyondCUDA #GPUkernels #mako 🌐 Connect with us: https://www.linkedin.com/in/jtatarchuk https://www.linkedin.com/in/waleedatallah * (00:00) - C1750 P1315 Waleed Atallah Audio * (26:24) - OUTRO SEQUENCE

The True Story of ZLUDA: How CUDA Can Run on AMD & Intel GPUs

Listen to the story of ZLUDA directly from Andrzej Janik! From Intel beginnings to AMD, hear the untold story of running CUDA apps on non-Nvidia GPUs. Learn about the journey, challenges, and future of multi-vendor AI compute. #ZLUDA #AMD #CUDA 02:47 - What drew you into GPU software? 06:33 - Why start the ZLUDA project? 08:16 - When did you realize CUDA was viable? 09:56 - What happened at Intel? 11:45 - How did you transition to AMD? 14:58 - What are the challenges? 17:13 - Why did AMD stop funding? 19:59 - Where is ZLUDA today?" 🌐 Connect with us: https://www.linkedin.com/in/jtatarchuk [https://www.linkedin.com/in/jtatarchuk] https://github.com/vosen

Why the World Needs AMD for Sovereign AI with Keith Strier @ AMD

Keith Strier explains the origins of sovereign AI, and why he moved from NVIDIA to AMD. He unpacks the GPU divide, silicon diversity, and why nations are now embracing open source and the ROCm ecosystem. 02:10 – Quitting 17-yr consulting for NVIDIA 04:55 – NVIDIA culture shock: flat & fast 07:32 – EY’s first AI practice (2016) 10:08 – Estonia & Malta AI strategies 12:56 – Launching NVIDIA’s “AI Nations” 14:30 – ChatGPT hits; Jensen pivots 17:05 – GPU land-grab by governments 19:40 – Real bottleneck: data centers 23:55 – Move to AMD to scale AI infra #BeyondCUDA #AIInfrastructure #AMD 🌐 Connect with us: https://www.linkedin.com/in/jtatarchuk https://www.linkedin.com/in/keithstrier/

How dstack is Cutting GPU Cloud Costs 3–7x with Smarter Orchestration

How we cuts cloud GPU costs 3-7x! Andrey Chepstov - CEO @ dstack (https://dstack.ai) chats with Jeff Tatarchuk on open source AI & redefining infrastructure. #AI #OpenSource #GPUs 00:29 - Intro and journey at JetBrains.   09:02 - How was dstack born?   13:14 - What are the challenges with Kubernetes?   18:36 - What problems does dstack solve?   26:21 - dstack roadmap and features.   30:25 - Open source journey and community building.   42:03 - What does Beyond CUDA mean?   🌐 Connect with us: https://www.linkedin.com/in/jtatarchuk/ https://www.linkedin.com/in/andrey-cheptsov * (00:00) - C2004 Andrey Cheptsov - Audio * (00:24) - Guest Introduction: Andre Cheep * (01:12) - Andre's Early Interest in Tech * (02:52) - Journey at JetBrains * (06:31) - Lessons from JetBrains * (09:14) - The Birth of DST Stack * (13:32) - Challenges with Kubernetes * (18:49) - DST Stack's Solutions and Vision * (24:12) - Challenges with Kubernetes and Market Dynamics * (24:53) - The Importance of Kubernetes in Cloud Services * (25:35) - Competing with Kubernetes: A New Approach * (26:54) - DST Stack Roadmap and Exciting Features * (27:20) - GPU Health Checks: A Game Changer * (30:41) - Open Source Journey and Community Building * (34:36) - Technical Challenges and Ecosystem Dynamics * (36:00) - Cost Efficiency and Surprising Use Cases * (39:50) - Future Plans and Community Involvement * (42:48) - Final Thoughts and Beyond CUDA