AMD ROCm 7.0 Software: Supercharging AI and HPC Infrastructure with AMD Instinct Series GPUs and Open Innovation

In today’s era of exponential compute demand, AMD ROCm™ 7.0 software represents a foundational leap forward in software infrastructure for AI and high-performance computing (HPC). Built from the ground up for AMD Instinct™ GPUs – especially the next-generation MI350 Series – ROCm 7.0 software introduces transformative capabilities in GPU compute, precision optimization, orchestration, and datacenter scalability. With full-stack performance enhancements, expanded datatype support (including FP4 and FP6), and robust support for modern AI and scientific workloads, ROCm 7.0 enables datacenter architects, HPC system managers, and AI infrastructure leads to deploying large-scale training and inference with maximum efficiency, throughput, and flexibility.

Datacenter-Grade Feature Highlights

ROCm 7.0 introduces a slate of upgrades across the AMD AI software stack, each aimed at improving usability, performance, and cross-platform compatibility:

• Unified Triton 3.3 Kernels for Cross-Vendor HPC/AIML Portability: Integrates Triton v3.3, enabling a unified GPU kernel development experience across AMD Instinct hardware. Triton automatically targets HIP for AMD hardware, with built-in support for vendor-specific instructions—ideal for scientific computing teams seeking performance portability. Out-of-the-box Flash Attention kernels in Triton offer fast transformer attention operations compared to unoptimized baselines, accelerating core LLM operations.
• DeepEP Inference Engine for Multi-GPU Efficiency: Debuts DeepEP, introducing intelligent pipelining for overlapping compute and data transfer across GPU nodes, ensuring high utilization for latency-sensitive AI workflows—particularly valuable in tightly coupled multi-node configurations common in supercomputing.
• Full Stack Day-0 Framework & Model Stack Readiness: ROCm 7.0 supports most major AI frameworks (PyTorch, TensorFlow, ONNX, JAX/XLA) from day-zero through the 1.8M Hugging Face Models on its Hub and scientific workloads, helping ensure that system integrators and infrastructure managers can count on production-ready software for datacenter deployment.
• Vendor-Agnostic Orchestration for Heterogeneous GPU Datacenters: With integrated support for vLLM , ROCm 7.0 offers highly efficient orchestration of transformer workloads across mixed-GPU clusters. SGLang intelligently balances compute loads in real time, supporting complex topologies and reducing idle time in multi-node installations. Crucially, these orchestration layers work across heterogeneous clusters – the ROCm communication layer abstracts AMD RCCL seamlessly, so multi-GPU collectives and pipeline transfers operate transparently on any mix of hardware.

Scalable Performance for AI & HPC Infrastructure

Training & Inference Throughput

ROCm 7.0 software boasts significant performance gains for AI workloads. Key results include:

• ~3× Training Throughput with AMD Instinct MI355X GPU and ROCm 7.0: A preview version of ROCm 7.0 delivered an average of three times higher training performance (measured in TFLOPs) with an MI355X GPU compared to ROCm 6 software identically configured with an MI300X GPU node¹. Large-scale model training (e.g. GPT pre-training via Megatron-LM) benefits from new FP8 precision and optimized kernels, enabling fast time-to-train for transformer models in ROCm 
• Up to 4.6× Inference Throughput Uplift for Generative AI Infrastructure: A preview version of ROCm 7.0 achieves up to 4.6× more tokens/sec as a combined average on Llama 3.1 70B, Qwen 72B, and DeepSeekR1 models on the MI355X platform, compared to the MI300X platform². High-throughput quantized formats (FP4, INT8, etc.) combined with better pipeline parallelism make ROCm software ideal for scalable AI serving deployments.

Competitive Benchmarking vs. NVIDIA GPUs

AMD has demonstrated a pre-build version of ROCm 7.0 software on AMD Instinct hardware can meet or exceed NVIDIA’s higher end accelerators in throughput. For example, an AMD Instinct MI355X platform (8x GPU) (with FP4 precision) delivered up to 1.3× better inference throughput than an NVIDIA B200 8x GPU platform on a cutting-edge LLM (the DeepSeek R1 model)³. This shows that the AMD Instinct Series MI355X Series offers higher generative AI throughput per node, directly challenging the NVIDIA B200 GPU  on large-model inference. (Note: AMD Instinct MI355X Series GPU is a next gen GPU optimized for inference, with 288GB HBM3e per GPU, versus 192GB on NVIDIA B200 per GPU).

Overall, ROCm 7.0 software is designed for performance gains which can translate into more productivity and low costs for AI infrastructure. In short, technical leaders can achieve favorable AI outcomes by adopting the optimized AMD ROCm 7.0 software stack.

AMD Instinct™ MI350 Series GPU Enablement for Advanced AI and Scientific Computing

ROCm 7.0 software introduces production-grade software enablement for the AMD Instinct MI350 Series, purpose-built for large-scale inference and training workloads. The MI350 GPU leverages advanced FP4 and FP6 datatype support, offering outstanding compute density and memory efficiency for transformer workloads. Combined with the AI Tensor Engine for ROCm (AITER) software, these GPUs are designed to deliver faster performance on decoder execution and significant acceleration across GEMM, attention, and MoE layers—transforming datacenter throughput for both AI and HPC workloads.

Ecosystem & Community

ROCm 7.0 software is not just a standalone release – it’s backed by a robust ecosystem strategy and community collaboration:

• Open-Source Collaboration: AMD continues to upstream key optimizations from ROCm 7.0 back to the broader open-source community. Improvements to kernel algorithms (e.g. fused attention, optimized GEMMs) have contributed to projects like ONNX-MLIR and MLIR compilers. Also, advances in distributed training flow into DeepSpeed, scheduling and runtime innovations fed into OpenXLA and even the Triton OSS compiler community sees contributions. This cooperative approach ensures that ROCm software stays aligned with the industry’s best practices and that the wider AI ecosystem.
• Cross-Vendor Flexibility: A core tenet of ROCm 7.0 is vendor neutrality. The software stack is designed to be fully compatible with various vendors, out-of-the-box. Abstraction layers like NIXL automatically select the appropriate communication backend RCCL at runtime, and kernel compilers like Triton generate device-specific code so that developers don’t need to write vendor-specific variants. There are no proprietary lock-ins or “works on X only” caveats – ROCm 7.0 software’s broad support enables true portability and protects investments as new hardware emerges in the future.

Deployment Scenarios in Datacenter Environments

ROCm 7.0 software’s features come together to enable a wide range of real-world AI workflows. Below are a few examples of what becomes possible with this release:

• Distributed Inference Scheduling at Scale: With vLLM, SGLang, and ROCm 7.0, datacenter operators can serve large generative models across heterogeneous clusters while meeting strict latency targets. ROCm integrates both frameworks to optimize scheduling, balance workloads, and streamline backend communications for efficient large-scale AI services.
• Fine-Tuning Large Models on Minimal Hardware: Using ZeRO-3, quantization, and FP8/FP4 datatype support, a preview version of ROCm 7.0 enables datacenter teams to train and fit up to 520B parameters using only MI355X GPUs ⁴—dramatically improving compute efficiency without infrastructure sprawl.
• Hybrid Cloud Bursting: For organizations running on-premises AI deployments, ROCm 7.0 offers flexibility to burst workloads to the cloud when needed. Workflows can be configured to “burst” from a small on-prem cluster (e.g. 4 GPUs) to a larger cloud cluster (say 32 GPUs) during peak demand. ROCm software’s orchestration layer handles the transition seamlessly – spinning up containers on cloud instances, redistributing model shards with llm-d on the fly, and later scaling back down. This elasticity means you pay for extra compute only when required, without having to maintain a massive cluster.

Enterprise Ready With ROCm 7.0

ROCm 7.0 offers an open-source, modular AI software stack built on a multi-layered architecture that spans from core algorithms to infrastructure, delivering a cohesive platform for enterprise AI. Its’ private , vendor-neutral design allows deployment across various platforms without vendor lock-in, making ROCm 7.0 ideal for heterogeneous, hybrid cloud and on-premises data center environments. ROCm 7.0 integrates seamlessly with Kubernetes orchestration and MLOps pipelines, enabling enterprise infrastructure teams to run and scale AI workloads in containerized environments and oversee the entire AI lifecycle from model ingestion to inference within a unified platform. Furthermore, the platform offers robust multi-tenant capabilities via Kubernetes – including fine-grained resource quotas, role-based access control (RBAC), and built-in observability with monitoring and auto-scaling hooks – so infrastructure teams can securely share and govern AI resources across projects with confidence.

Conclusion

ROCm 7.0 marks a significant milestone in AMD’s commitment to open, high-performance computing for AI and HPC. By delivering a production-grade platform with uncompromising performance and broad compatibility, ROCm 7.0 enables organizations to deploy advanced AI solutions at scale on their terms – whether that’s fine-tuning the latest LLM on an on-prem cluster or serving millions of inference requests across a heterogeneous fleet. This release solidifies ROCm software as a truly vendor-agnostic AI stack, marrying software flexibility with GPU power to drive breakthroughs in machine learning. With ongoing optimizations and a thriving open-source community, ROCm software continues to redefine what’s possible in AI, paving the way for researchers, engineers, and enterprises to innovate without barriers. We invite you to explore ROCm 7.0 and join the growing community pushing the frontiers of AI on an open platform.

Deployment & Documentation Resources

To explore ROCm 7.0 or deploy it in your environment, the following resources will be helpful:

• AMD ROCm Official Documentation – Comprehensive guides, release notes, and tutorials for installing and using ROCm 7.
• AMD ROCm 7.0 Technical Blog – Comprehensive overview of the stack and new features
• AMD Infinity Hub – A catalog of ready-to-run containerized applications and frameworks (for both HPC and AI) optimized for ROCm, along with deployment guides.
• ROCm Developer Hub – Portal for developers featuring training materials, webinars, community forums, and best practices for ROCm.

For more information, visit amd.com/ROCm and join the open AI/HPC community driving ROCm forward.