Mike on X: "$AMD & Forrest declared @AMD AI Hardware is ahead of competition(MI450 2026), now lets talk about Software (ROCm) vs $NVDA CUDA ! 🧵 AMD's ROCm (Radeon Open Compute) and NVIDIA's CUDA (Compute Unified Device Architecture) are the leading GPU computing platforms for AI, https://t.co/RWQmd

& Forrest declared

AI Hardware is ahead of competition(MI450 2026), now lets talk about Software (ROCm) vs

CUDA ! 🧵 AMD's ROCm (Radeon Open Compute) and NVIDIA's CUDA (Compute Unified Device Architecture) are the leading GPU computing platforms for AI, high-performance computing (HPC), and machine learning (ML) workloads. ROCm is an open-source stack designed for AMD GPUs, emphasizing portability and community-driven development, while CUDA is a proprietary(closed source) ecosystem tightly integrated with NVIDIA hardware, benefiting from years of optimization and ecosystem maturity. As of September 2025, ROCm has made significant strides toward parity with CUDA, particularly in AI inference and training, driven by AMD's focus on open-source AI acceleration. 1. AMD ROCm 7+ Progress: AMD ROCm 7+ ProgressROCm 7.0 represents a major leap for AMD, announced in June 2025 at Advancing AI 2025 and released in general availability in Q3 2025 (August 2025). It builds on ROCm 6.x (which introduced multi-GPU support, Flash Attention for transformers, and beta Windows integration via WSL2) by prioritizing AI/HPC scalability, hardware support, and developer accessibility. Key advancements: ~Performance Enhancements: Up to 3.5x uplift in AI inference over ROCm 6.0, with specific gains like 3.2x in Llama 3.1 70B training (TFLOPS on 8x MI300X GPUs) and 3.8x in DeepSeek R1 inference. This is achieved through new kernels (e.g., GEMM autotuning, Mixture-of-Experts (MoE), advanced Attention mechanisms) and support for low-precision datatypes (FP4, FP6, FP8, mixed precision). ROCm 7 also integrates with frameworks like vLLM, SGLang, and llm-d for distributed inference, scaling efficiently across multiple GPUs without interference. ~Hardware Support Expansion: Full compatibility with AMD Instinct MI350/MI355X (CDNA 4 architecture, shipping Q3 2025), MI300X/MI325X, and backward support for MI250X/MI100. It extends to consumer/endpoint hardware, including Radeon RX 9000 series (RDNA 4), Radeon PRO W7000, and Ryzen AI MAX processors (e.g., Strix Halo). Multi-GPU clustering via Infinity Fabric Link is optimized for up to 72 GPUs (260 TB/s bandwidth in Helios rack-scale systems, previewed for 2026). ~Ecosystem and Developer Tools: =>HIP 7.0 API aligns more closely with CUDA semantics (e.g., refined error handling, streamlined headers) to ease porting CUDA codebases, reducing manual adjustments by up to 50% in some cases. =>Day-zero support for PyTorch 2.2+, TensorFlow, ONNX Runtime, and Hugging Face models (e.g., Llama 3.3 70B, Qwen2-72B). =>Windows native support (full, not just WSL2) from 2H 2025, with in-box integration for Ubuntu, OpenSUSE, and Red Hat Linux. =>AMD Developer Cloud: Free/complimentary access (up to 50 hours via ROCm Star Developer program) to MI300X/MI350 hardware for testing, fine-tuning, and benchmarking. =>Profiling tools: rocprofiler-SDK (beta to production-ready) with FP8 metrics, rocSOLVER improvements (e.g., faster eigensolvers/SVD), and upcoming rocpd for trace outputs (CSV/OTF2/Perfetto). Future Outlook (ROCm 7+): ROCm 8.0 (expected 2026) will support MI400X/MI500X (CDNA 5/6 architectures) and Helios unified racks (EPYC CPU + Instinct GPU + Pensando NIC). AMD emphasizes open-source momentum, with frequent bi-weekly updates, bundled dependencies via TheRock build system, and partnerships (e.g., Hugging Face, Meta, OpenAI) for model optimization. 2.

CUDA Progress CUDA remains the industry standard, with the latest major release being CUDA Toolkit 13.0 (August 2025), following iterative updates in the 12.x series (e.g., 12.9.1 in June 2025, 12.8 in January 2025 for Blackwell support). NVIDIA's focus is on ecosystem depth, with enhancements for emerging architectures like Blackwell (B200) and future Rubin (2026). Key features ~Performance Enhancements: CUDA 13.0 introduces tile programming (bringing Hopper/Blackwell tensor cores to CUDA), JIT LTO (link-time optimization) via nvJitLink, and C++20 support (minimal versions: GCC 11, Clang 14, MSVC 19.29). It optimizes for FP8/INT8 in AI, with up to 2x gains in tensor memory traffic visualization via Nsight Compute 2025.3. Libraries like cuDNN, cuBLAS, and TensorRT-LLM see ongoing updates for MoE, Flash Attention, and distributed training. ~Hardware Support: Full compatibility with Blackwell (GB200/B200, compute capability 9.0+), Hopper (H100/H200), Ada Lovelace (RTX 40-series), and legacy (Ampere+). Deprecates offline compilation for pre-Turing architectures (Maxwell/Pascal/Volta; use CUDA 12.9 or earlier). Supports up to 8x GPU scaling in NVLink clusters. ~Ecosystem and Developer Tools: =>Minor version compatibility (e.g., apps built on 12.x run on 13.x drivers). =>Nsight Systems/Compute 2025.x: Range profiling improvements, instruction mix/scoreboard dependency views, and Blackwell-specific metrics (e.g., tensor memory charts). =>Integrations: PyTorch 2.4+, TensorFlow 2.16+, and cuPy (Blackwell-patched in 2025). Deprecates PowerPC and older PTX ISAs (pre-7.5). =>Driver branch R580 is the last for pre-7.5 architectures. Future Outlook: CUDA 14.0 (expected late 2025/early 2026) will enhance Rubin architecture support and agentic AI (e.g., memory/planning optimizations). NVIDIA's closed ecosystem ensures tight hardware-software co-design but locks users into its stack 3. What Large Hyperscalers say about

ROCm Hyperscalers (e.g.,

AWS,

,

,

Cloud,

Azure,

Cloud, Saudi Humain) have increasingly adopted ROCm for AMD Instinct GPUs, driven by cost savings (MI300X ~30-50% cheaper than H100 equivalents) and open-source flexibility. However, Hypersaclers praised for inference performance and scalability, but criticisms on ecosystem maturity, installation complexity, and training support compared to CUDA. Adoption is strongest among neo-hyperscalers (e.g., TensorWave, HUMAIN, Oracle,

) and AI-focused firms (e.g., xAI, Meta, OpenAI), with traditional hyperscalers using it selectively for cost-sensitive workloads. Positive feedbacks from Capability to Performance: A.

: CEO

(June 2025 keynote) called MI300X/ROCm "extremely exciting" for training/inference, confirming production use of MI300X and collaboration on MI450 (2026). ROCm's open stack enables faster iteration on models like GPT variants, with 3x inference uplifts in previews. B. Meta &

: Meta uses ROCm for Llama fine-tuning on MI300X clusters; xAI (

) integrates it for Grok models, citing 30% higher throughput in DeepSeek R1 vs. NVIDIA B200 (FP8, ROCm 7 on MI355X). Both highlight ROCm's distributed inference (vLLM scaling) as "seamless" for hyperscale.

CEO also confirmed $600B investment through 2028 with #AI infrastructure diversification from day one. Today,

is AMD's largest customer. C.

Azure &

: Azure deploys MI300X with ROCm 6.1+ for HPC/AI, reporting "leadership inference" (e.g., 3.4x Qwen2-72B speedup) and easy Kubernetes/SLURM integration. Oracle praises ROCm's open-source primitives for custom optimizations, reducing vendor lock-in. D.

Cloud & AWS: Limited but growing; AWS uses ROCm for cost-optimized inference (e.g., Stable Diffusion), with feedback on "mature multi-GPU" via Infinity Fabric. Google notes FP4/FP6 support as a capability edge for next-gen models. Overall, hyperscalers report 2-3x efficiency gains in ROCm 7 vs. 6 for transformer models (BERT/GPT), with low power draw (MI355X at 750W vs. B200's 1kW+). E.

&

(Neo-Hyperscalers): AstraZeneca switched to MI300X/ROCm via TensorWave, achieving "breakthrough efficiency" in MLPerf Inference v5.1 (FP4 pruning, 10x scaling). HUMAIN reports ROCm's Flash Attention as "power-efficient" for LLMs, with near-parity to CUDA in tokens/second. Overall, hyperscalers view ROCm as "viable for prime time" in 2025, especially for inference-heavy AI (e.g., LLMs like Llama/Qwen), with 70-80% of feedback positive on cost/performance. Adoption is ~20-30% of AMD's AI sales from hyperscalers, up from 10% in 2024. 4. The question that analysts/investors are asking: Will ROCm 7+ Outperform or Be on Par with CUDA in Future Benchmarks? ROCm 7+ is poised to reach parity with CUDA in many AI/HPC benchmarks by 2026, with potential outperformance in specific areas like inference efficiency and cost-per-token.

Forrest Norrod declared "MI450 AI GPU will be faster than ANYTHING Nvidia has, yes, that includes even Rubin Ultra" Current benchmarks ROCm 7 vs CUDA 13: ~Inference: ROCm 7 on MI355X shows 30% higher throughput in DeepSeek R1 (FP8) vs. B200/CUDA, and 3x vs. ROCm 6 in Llama 3.1 70B (tokens/second via vLLM). MLPerf Inference v5.1: MI300X/ROCm leads in FP4 efficiency and scaling (up to 8x GPUs). ~Training: Up to 3.2x TFLOPS uplift in Megatron-LM (Llama 2-70B on 8x MI300X), but CUDA edges out by 10-20% in PyTorch benchmarks due to cuBLAS optimizations. ~General: In Stable Diffusion/LLM tasks, RX 7900 XTX/ROCm matches RTX 4080/CUDA in tokens/images per second, per Phoronix tests. ROCm lags in non-AMD hardware portability. Conclusion: While the ROCm vs CUDA debate can go on for months and years, but ROCm 8.0 (2026), HIP's CUDA alignment will minimize porting friction, enabling 90-95% compatibility. Benchmarks predict parity in PyTorch/TensorFlow for inference/training on MI400X (10x Helios performance vs. MI300). Open-source contributions (e.g., FlashAttention ports) will match CUDA's kernel depth. In cost-sensitive hyperscale inference, ROCm could lead by 20-40% (e.g., MI500X vs. Rubin in FP4 MoE, per AMD projections). Open ecosystem avoids CUDA's lock-in, accelerating innovations like GRPO-RLHF (2x faster alignment on MI300X). SemiAnalysis forecasts ROCm "catching up" in 18-24 months for LLMs. Not Financial Advice!