Alex Medick

ROCm + AMD MI325X is ready for prime time. See benchmarks vs CUDA and why more teams are switching to ROCm for AI performance and cost.

ROCm vs CUDA: A Performance Showdown for Modern AI Workloads

The Battle for AI Acceleration

For years, CUDA has been the default for AI teams, mainly because there were no serious alternatives. But the hardware landscape has changed. So has the software.

Enter ROCm, AMD’s open-source compute platform.

Combined with the latest MI325X GPUs, ROCm is no longer just “an alternative” but now it’s a real performance contender.

Here’s how ROCm stacks up against CUDA in real-world AI workloads.


Benchmarks: ROCm on MI325X vs CUDA on H100

📌 Source: TensorWav

The Battle for AI Acceleration

For years, CUDA has been the default for AI teams, mainly because there were no serious alternatives. But the hardware landscape has changed. So has the software.

Enter ROCm, AMD’s open-source compute platform.

Combined with the latest MI325X GPUs, ROCm is no longer just “an alternative” but now it’s a real performance contender.

Here’s how ROCm stacks up against CUDA in real-world AI workloads.


Benchmarks: ROCm on MI325X vs CUDA on H100

📌 Source: TensorWave internal tests, using Hugging Face Transformers + DeepSpeed across identical model configs.


Why ROCm Performs This Well Now


1. Open kernel libraries tuned for transformer workloads

ROCm 6.x includes major updates to MIOpen and math libraries, optimized specifically for attention-heavy models.


2. Huge VRAM advantage on MI325X

With 256GB of HBM3e per GPU, ROCm avoids model splitting and pipeline complexity that CUDA often needs.


3. DeepSpeed + Hugging Face native support

Frameworks like DeepSpeed and Transformers now run cleanly on ROCm with minimal patching and no hacks required.


4. Compiler improvements via hipRTC + MIGraphX

AMD’s compiler stack is catching up fast, with performance parity across common transformer layers, fused kernels, and matmul ops.


When ROCm Beats CUDA

 * Memory-bound workloads: Think long-context LLMs or training with large batch sizes
 * Inference at scale: ROCm’s larger per-GPU memory = fewer nodes, lower latency
 * Cost-sensitive training: MI325X instances are more cost-efficient per TFLOP than H100s
 * Open ecosystem needs: ROCm doesn’t lock you into NVIDIA’s tooling


What’s Still Catching Up

Let’s be real, ROCm isn’t perfect.

 * Some niche libraries still require manual tuning
 * CUDA still leads in ecosystem maturity for legacy tools
 * TensorRT equivalent (MIGraphX) is improving but less widely adopted

That said, TensorWave is helping bridge the gap by offering ROCm-optimized clusters, dedicated support, and direct collaboration with AMD engineering teams.


Final Word: CUDA Isn’t Dead, But ROCm Is Real

If you’re evaluating infrastructure for your next model, ROCm + AMD isn’t a compromise, it’s an advantage for many workloads.

And with open tooling, higher memory headroom, and comparable performance, ROCm is finally ready for prime time.

→ See it for yourself: Run your next model on TensorWave’s ROCm-optimized cloud


About TensorWave

TensorWave is the AMD GPU cloud purpose-built for performance. Powered exclusively by 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 vs CUDA: A Performance Showdown for Modern AI Workloads

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