Intel CEO: 'The entire industry is motivated to eliminate the CUDA market'
Intel’s CEO claims the “entire industry” wants to break Nvidia’s dominance in GPU computing by eliminating reliance on CUDA, but many point out that competitors have had years to build viable alternatives and largely failed to invest at the necessary scale. Commenters argue Nvidia’s real moat is its mature software stack, tools, and libraries—backed by heavy, long-term investment—while Intel, AMD and others still offer fragmented, incomplete ecosystems despite promising technologies like ROCm, SYCL, oneAPI and Vulkan compute. There’s broad agreement that a portable, high‑quality CUDA alternative would be healthy for the market, yet skepticism that incumbent chipmakers will prioritize the deep software work needed to get there.
CUDA’s Moat and Why It’s Hard to Dislodge
- Consensus that CUDA’s true moat is the software ecosystem: mature compilers, libraries (BLAS, RNG, etc.), debugging/profiling tools, and good developer experience across many domains (AI, HPC, graphics, science).
- Many note that “CUDA = x86 of GPUs”: ubiquitous, de‑facto standard, with years of incremental tuning and countless dependent projects.
- Several argue the problem isn’t just the CUDA language but countless hand‑optimized kernels and low‑level hardware features (PTX, tensor cores) that apps rely on.
Intel/AMD Efforts and Shortcomings
- Commenters say Intel/AMD “had years” with OpenCL and underinvested; OpenCL largely failed on ergonomics and support.
- ROCm and HIP are seen as partial attempts at CUDA compatibility. Some report ROCm is improving and open source; others call support fragmented, poorly documented, and limited to select “pro” or recent GPUs.
- Intel’s oneAPI/SYCL stack is viewed as promising by some HPC practitioners, but adoption is still niche and tooling messy across backends.
Hardware Access, Hobbyists, and Old Cards
- Big complaint: AMD/Intel focus on high‑end/datacenter parts and ignore cheap or older GPUs, which blocks students/hobbyists from learning and contributing.
- Debate over whether supporting 5–7‑year‑old cards (e.g., RX 570 series) is essential for community growth or a resource drain.
- Some argue cheap cloud GPU rentals could help; others say cost, credit‑card barriers, and capacity constraints make local GPUs crucial.
Alternative APIs and Architectures
- SYCL, Vulkan compute, DirectX compute shaders, and OpenCL 3.0 are all mentioned; none are seen as matching CUDA’s usability or feature set yet.
- Compute shaders are widely regarded as workable but painful (limited language features, poor tooling, no tensor‑core‑class access in older APIs).
- Past “many‑core CPU” attempts (Xeon Phi, Larrabee) are discussed as instructive failures: not enough bandwidth, awkward to program, and beaten by rapidly improving GPUs.
Open vs Proprietary and Strategy
- Some want an open CUDA‑equivalent (SYCL, Vulkan, OpenCL) to “commoditize the complement”; others note many such consortia historically underperform.
- Strong view that Intel/AMD culture still treats software as secondary; until they fund CUDA‑class work (e.g., first‑class PyTorch backends and full CUDA‑compat layers), talk of “eliminating CUDA” is dismissed as PR.