Alex Medick

Discover what’s new in ROCm™ 7.0 - breakthrough AMD Instinct™ MI350 GPU performance, distributed inference, and enterprise AI tools.

ROCm 7: What’s New With The AMD Software

AI Is Moving Fast And So Are the Challenges

AI innovation isn’t just accelerating. It’s racing ahead. Models that once felt massive at tens of billions of parameters now stretch into the hundreds of billions. Inference workloads keep ballooning, and enterprises are being asked to deliver state-of-the-art performance while keeping infrastructure costs from spiraling out of control.

For developers, the pressure is equally intense: build faster, scale smarter, and keep systems flexible and portab

AI Is Moving Fast And So Are the Challenges

AI innovation isn’t just accelerating. It’s racing ahead. Models that once felt massive at tens of billions of parameters now stretch into the hundreds of billions. Inference workloads keep ballooning, and enterprises are being asked to deliver state-of-the-art performance while keeping infrastructure costs from spiraling out of control.

For developers, the pressure is equally intense: build faster, scale smarter, and keep systems flexible and portable in a world where both algorithms and hardware evolve monthly. It’s not enough to optimize for today. You have to be ready for tomorrow.

This is the environment AMD designed ROCm™ 7.0 for.









ROCm 7.0: A Milestone for the AI Era

ROCm 7.0 isn’t a minor release. It’s a major milestone designed to address the realities of modern AI. With this release, AMD brings together performance improvements, expanded model support, and enterprise-ready management tools into one unified software stack.

At a glance, ROCm 7 delivers:

 * Breakthrough training and inference performance on AMD Instinct™ MI350 series GPUs.
 * Seamless distributed inference across clusters with popular frameworks.
 * HIP 7.0 improvements for code portability and easier migration across hardware ecosystems.
 * Enterprise-focused tools for cluster management and AI workflow orchestration.
 * Production-ready quantized models (MXFP4, FP8) built and optimized with AMD Quark.

The bottom line: ROCm 7.0 is built to help developers move faster, and enterprises deploy AI with confidence.


Breakthrough Performance with the AMD Instinct MI350 Series

At the heart of ROCm 7.0 is full enablement of the AMD Instinct MI350 series GPUs, powered by the AMD CDNA™ 4 architecture. These GPUs bring massive memory bandwidth and refined compute engines, making them ideal for large-scale AI training and inference workloads.

But performance on silicon alone doesn’t solve the problem. AMD paired the hardware with an ecosystem of prebuilt ROCm 7.0 Docker images, including vLLM and SGLang, optimized for AMD Instinct MI355, MI350, MI325, and MI300 GPUs.

These images are tuned for MXFP4 and FP8 precision and ship with production-ready large-scale models such as:

 * DeepSeek R1
 * Llama 3.3 70B
 * Llama 3.1-405B
 * Additional open-source models like gpt-oss-120B and gpt-oss-20B

All of these models are quantized by AMD Quark, an open-source toolkit for optimization that supports quantization, pruning, and other efficiency techniques.

For enterprises, this means less time configuring environments and more time delivering results. For developers, it means the ability to quickly benchmark, fine-tune, and deploy cutting-edge models at scale, without starting from scratch.


Distributed Inference: Scaling Beyond a Single Node

Today’s AI deployments rarely live on a single box. Whether you’re powering enterprise-wide copilots, serving multimodal models, or running a high-throughput inference service, distributed performance is what matters.

ROCm 7.0 makes distributed inference seamless. With native support in open frameworks like SGLang, enterprises can run models across multiple GPUs and multiple nodes with minimal friction.

Key upgrades include:

 * Expanded algorithm support for attention and reasoning workloads.
 * Native Mixture of Experts (MoE) support for more efficient scaling.
 * Low-precision formats (FP4, FP6, FP8) that balance accuracy and throughput.
 * Prefill–Decode disaggregation, enabling frameworks like SGLang to boost throughput and slash latency compared to single-node inference.

This makes ROCm 7 a powerful foundation for organizations building enterprise-grade inference platforms. Instead of wrestling with scale, teams can focus on delivering AI applications that matter to end users.


Enterprise AI: Open and Scalable

As AI adoption shifts from experiments to production, infrastructure management becomes the bottleneck. ROCm 7.0 addresses this with new enterprise-focused tools designed for scale:

 * AMD Resource Manager – Simplifies cluster orchestration and workload scheduling across environments like Kubernetes and Slurm.
 * AMD AI Workbench – A flexible environment for deploying, adapting, and scaling AI models with built-in support for inference, fine-tuning, and integration into enterprise workflows.

These tools align with AMD’s commitment to open-source principles. By staying open, AMD ensures that enterprises have transparency, flexibility, and ecosystem collaboration at their disposal, not vendor lock-in.

The result: enterprises gain the operational efficiency they need to manage AI at scale while retaining the agility to adopt new models, frameworks, and algorithms as they emerge.


Getting Started: From Prototyping to Production

One of the standout aspects of ROCm 7 is accessibility. AMD isn’t just shipping software; it’s providing an ecosystem developers can plug into immediately.

You can start today by exploring the ROCm AI Developer Hub, where tutorials, guides, and performance best practices are published. Prebuilt Docker images are available for:

 * SGLang
 * vLLM
 * Megatron-LM
 * JAX

These give developers a ready-to-run environment for benchmarking and deploying models on AMD Instinct GPUs.

For enterprises, the ROCm documentation dives deep into deployment guidance, best practices, and strategies for managing large-scale AI infrastructure with AMD hardware.

Whether you’re a researcher experimenting with MXFP4-optimized LLMs or an enterprise scaling AI copilots across thousands of users, ROCm 7 is ready to support your workflow.


Conclusion: The Future of AI Is Open, Scalable, and Accelerated

AI is moving too quickly for organizations to stand still. Developers need performance that scales, portability that ensures future-readiness, and tools that make enterprise management simpler.

That’s exactly what ROCm 7.0 delivers:

 * Breakthrough GPU performance with the MI350 series.
 * Seamless distributed inference for large-scale deployments.
 * Enterprise tools that simplify orchestration and workflows.
 * Open-source, production-ready models optimized for real workloads.

This release isn’t about keeping up with the AI era, it’s about pushing it forward.

Ready to get started? Explore ROCm 7 on AMD Instinct GPUs today, and see how open, accelerated AI can reshape what’s possible.


Links and Resources

 * ROCm 7 Launch Page (link)
 * ROCm Software 7.0.0 Documentation (link)
 * ROCm 7 GitHub (link)


About TensorWave

TensorWave is the AI cloud purpose-built for performance. Powered exclusively by AMD Instinct™ Series GPUs, we deliver high-bandwidth, memory-optimized infrastructure that scales with your most demanding models—training or inference.

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ROCm 7: What’s New With The AMD Software

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