AMD ROCm 7.0 Benchmarks: 3.2x Faster AI

Key findings

Short version: AMD reports major gains with ROCm 7.0. Training throughput improved up to about 3x versus ROCm 6 and some inference tests show ~3.2x on Llama 3.1 70B and higher on other models. Read on for how those numbers were produced, how you can reproduce tests, and what to watch for.

Why ROCm 7.0 matters

ROCm 7.0 is an open-source software stack for AMD GPUs. It fixes many AI performance gaps that held AMD back. The update adds support for new low-precision datatypes (FP4, FP6, FP8), better GEMM kernels, and library improvements.

That matters because software often limits hardware. Better software can make the same GPU much faster.

What this means for you

• If you already run AMD Instinct or Radeon AI hardware, a software update can give a large speed boost.
• If you choose new hardware, ROCm 7 lowers the cost-per-performance gap with competitors.
• If you run CUDA today, ROCm tools like HIPIFY aim to help migration.

What the public benchmarks show

AMD published preview benchmarks showing big gains. See their announcement at AMD ROCm 7 announcement and the detailed performance tables at AMD performance results. Press coverage that summarizes those claims includes VideoCardz, Tom's Hardware, and Phoronix. These sources report similar uplift ranges: about 3x for training and 3x+ for inference on select models.

How AMD measured (summary)

• Hardware: multi-GPU systems with AMD Instinct MI300X family and preview MI350/MI355 support.
• Workloads: LLM training and inference. Examples include Llama 2/3 (70B), Qwen 72B, and DeepSeek R1.
• Software: ROCm 6.x vs ROCm 7.0 preview, frameworks like PyTorch and vLLM.
• Metrics: training TFLOPS and inference tokens-per-second (TPS), averaged across tested models.

How to reproduce these benchmarks yourself

Below is a clear, step-by-step plan you can follow. Keep steps simple and repeatable. If you want to compare ROCm 6 vs ROCm 7 on your hardware, do this.

1) Prepare systems

• Use the same machine for both runs. Do not change BIOS, kernel, or network settings between tests.
• Install the OS and drivers clean. For ROCm 7 preview notes see the ROCm 7.0 RC1 release notes.

2) Software versions

• Frameworks: use the same PyTorch, TensorFlow, and vLLM versions across runs. ROCm 7 RC1 supports PyTorch 2.7 and Triton 3.3.0.
• Containers: prefer containerized runs to keep environments identical.

3) Models and config

• Pick the same model checkpoints and batch sizes. AMD used Llama 3.1-70B, Qwen 72B, and DeepSeek R1 in their tests.
• Report sequence lengths and batch sizes. Inference TPS changes with both.

4) Run the tests

1. Run ROCm 6 baseline tests, note TFLOPS and TPS.
2. Upgrade to ROCm 7 and repeat tests without other changes.
3. Repeat runs 3 times and use median values to reduce noise.

5) Report everything

• List hardware, OS, kernel, driver versions, framework versions, container images, and exact commands.
• Share logs and scripts on a public repo so others can reproduce.

Typical results and the "3.2x" number

Public reports show different uplift numbers by model. For example, VideoCardz notes ~3.2x uplift for Llama 3.1 70B, while AMD's own summary shows ~3x training and up to ~4.6x for some inference workloads. Expect results to vary by model, batch size, quantization, and how well a model uses the new low-precision kernels.

Why performance improved

Key technical reasons behind the speed gains:

• New kernels and GEMM autotuning: Better matrix-multiply code paths speed up core operations.
• Low-precision datatype support: FP8, FP6, and FP4 allow faster compute and lower memory use for inference.
• Library optimizations: hipBLASLt and Composable Kernel improvements boost throughput for many data types.

See the ROCm 7.0 release notes for specifics: ROCm 7.0 RC1 release notes.

Common caveats

• These are preview results. ROCm 7.0 is rolling out; final numbers may change.
• Not all models benefit the same way. Smaller models or poorly optimized code paths may see smaller gains.
• Framework and model support matters. Use tested framework versions to avoid regressions.

Practical tips for teams

• Start with a single-node test. If you see big gains, move to multi-node tests.
• Use container images to share reproducible setups across teams.
• If you depend on CUDA-only libraries, try HIPIFY to port code. Monitor correctness, not just speed.

How to get ROCm 7

Download and install information is on AMD's developer pages and release notes. Start at the announcement: AMD ROCm 7 announcement and the docs at ROCm 7.0 RC1 release notes. The AMD performance pages host the raw measurement tables: ROCm performance results.

FAQ

Will updating to ROCm 7 always make my models 3x faster?

No. Gains depend on model, quantization, batch size, and how your code uses kernels. Expect big wins for large LLMs that can use low-precision math.

Can I run ROCm 7 on consumer Radeon GPUs?

ROCm 7 broadens support. Coverage is expanding. Check the release notes for specific GPU support and distro compatibility.

How do I migrate CUDA code?

Use HIPIFY to convert CUDA to HIP. Then test thoroughly. Some CUDA libraries may not have exact ROCm equivalents yet.

Next steps and reproducible kit

If you want repeatable results, follow the steps above and publish your scripts and logs. Share a tiny benchmark first: run a single inference workload on a short sequence and post the command and output. That helps others validate your numbers quickly.

References: AMD announcement and benchmarks at AMD ROCm 7 announcement, ROCm 7 release notes at ROCm 7.0 RC1, raw tables at AMD performance results, and coverage at VideoCardz, Phoronix, and Tom's Hardware.

If you want, try the steps above on one model this week. I'll help you interpret the numbers.