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Choose CUDA when your project already depends on NVIDIA hardware, CUDA libraries, or CUDA-specific code. Choose ROCm when the exact AMD GPU, Linux environment, ROCm release, framework build, and required libraries are all supported together. HIP and HIPIFY can reduce the work of moving CUDA code to AMD, but they do not turn every application into a drop-in port. The only reliable performance decision comes from benchmarking your own model and deployment conditions on the GPUs you plan to use.
This guide explains how to make that choice without treating “NVIDIA versus AMD” as a sufficient technical specification. Compatibility is release-specific, migration has edge cases, and a platform that looks attractive on paper can fail when one driver, operating-system, framework, or library requirement does not line up.
CUDA or ROCm for AI: the short decision
Start with the GPU, not the brand name. NVIDIA publishes a toolkit, driver, and architecture matrix that connects GPU generations to CUDA toolkit versions and minimum drivers. AMD publishes a ROCm compatibility matrix that lists supported compute GPUs and operating-system requirements for particular ROCm releases. Check the exact model and release you intend to run; a different card from the same vendor can have a different support boundary.
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- Existing NVIDIA system or CUDA-dependent project: CUDA is normally the lower-risk path because the hardware and software assumptions already match.
- Existing AMD system: ROCm is sensible only after the exact GPU, Linux distribution and kernel requirements, ROCm release, framework build, and libraries have been verified.
- New hardware purchase: select a GPU whose architecture appears in the toolkit or ROCm matrix for the software versions you need, then confirm framework installation guidance.
- CUDA code that must run on AMD: evaluate HIP and HIPIFY as migration tools, budget for manual changes, and test correctness and performance on the target GPU.
- Performance or cost question: run an equivalent end-to-end benchmark. The official compatibility and porting documents do not establish a universal winner.
For CUDA version and architecture details, use NVIDIA’s CUDA Toolkit, Driver, and Architecture Matrix. For AMD hardware and Linux requirements, use the ROCm Compatibility Matrix.
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What CUDA and ROCm actually provide
CUDA is NVIDIA’s GPU computing stack
CUDA combines a programming model, runtime and driver interfaces, compiler and toolkit components, and an ecosystem of GPU-accelerated libraries. An application compiled with an older CUDA toolkit can use a newer NVIDIA driver because NVIDIA documents backward compatibility in the driver API. That does not remove version constraints: each CUDA toolkit still has a minimum driver requirement, and NVIDIA notes that toolkit and driver releases can deprecate support for older GPU architectures.
In practice, “the project uses CUDA” can mean several different things: source code calls CUDA runtime functions, kernels are written for CUDA, a framework build expects CUDA libraries, or a prebuilt extension was compiled for a particular toolkit and compute capability. Identify which of these applies before estimating migration effort.
ROCm is AMD’s compute software platform
ROCm supplies the runtime, compiler, libraries and development tools used to run GPU compute workloads on supported AMD hardware. Its support is expressed as a release-specific compatibility list rather than a blanket promise for every AMD product. The cited AMD matrix for ROCm 7.2.2/7.2.1 is a Linux matrix for compute GPUs and includes operating-system and kernel requirements. It explicitly separates compute support from graphics support, so a card that works for display or gaming should not automatically be assumed to be supported for ROCm AI workloads.
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ROCm compatibility also depends on the framework and libraries layered above it. The matrix does not establish universal support for every framework, model, consumer card, or extension. Follow the framework’s official installation instructions for the exact ROCm release you selected.
Hardware and version compatibility
NVIDIA architecture examples
NVIDIA’s current matrix gives these examples of the first or relevant CUDA toolkit support points:
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| GPU architecture | CUDA support shown in the matrix | What to verify |
|---|---|---|
| Blackwell | CUDA 12.8 is listed as the first toolkit support | Use a toolkit at or above the listed support point and its required driver. |
| Hopper | CUDA 11.8 and CUDA 12.0 variants are listed | Match the selected toolkit and minimum driver to the exact GPU. |
| Ada | CUDA 11.8 is listed | Confirm the framework build and any compiled extensions support the chosen toolkit. |
| Ampere | CUDA 11.0 is listed | Check the toolkit’s driver floor and the project’s library versions. |
| Volta, Pascal and Maxwell | Listed as supported through CUDA 12.x | The matrix associates their last driver support with R580; verify the precise release before upgrading. |
These are version-specific entries, not a guarantee that every CUDA 12.x feature, framework package, or third-party extension works on every architecture in the row. Consult the live matrix when selecting a toolkit or planning a driver change.
AMD GPU and operating-system checks
The ROCm 7.2.2/7.2.1 compatibility page dated April 23, 2026 lists supported compute GPUs alongside Linux distribution and kernel requirements. Treat that page as a release-specific contract: look up the exact SKU, then confirm that the host operating system and kernel fall within the listed combinations. Do not generalize from “AMD GPU” to an unsupported consumer card, and do not use graphics compatibility as evidence of compute compatibility.
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If your target is a different ROCm release, use that release’s matrix rather than copying a 7.2.2/7.2.1 result. A framework package may also pin a particular ROCm version, so the hardware check and the framework check must be performed together.
How to audit the complete software stack
A GPU is usable for an AI project only when all layers agree. Record the versions before installing anything:
- GPU model and architecture: write down the exact board or accelerator identifier, not merely “NVIDIA” or “AMD.” Include available VRAM because it determines whether the model and batch size can fit, although the compatibility documents do not prescribe a universal capacity threshold.
- Operating system and kernel: note the distribution, release and kernel. This is especially important for ROCm, whose cited matrix specifies Linux operating-system and kernel combinations.
- Driver: compare the installed NVIDIA driver with the CUDA toolkit’s minimum driver requirement, or install the driver required by the selected ROCm release.
- Toolkit and runtime: distinguish a system-wide toolkit from the runtime bundled with a framework environment. Record the CUDA or ROCm version that the application will actually load.
- Framework build: confirm that the framework’s official installation method supports the chosen toolkit and GPU. Do not infer support merely because the framework has an AMD or NVIDIA backend.
- Libraries and extensions: list attention kernels, distributed-training components, inference engines, custom operators and other binary packages. A single CUDA-only extension can block an otherwise workable ROCm migration.
- Deployment target: repeat the audit on the production host, container image or managed service. A development machine that passes the checks does not prove that the deployment image has the same driver access and libraries.
Keep this inventory in version control. When a package upgrade changes a toolkit, driver or framework build, rerun the compatibility checks instead of assuming backward compatibility at every layer.
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Can you port CUDA code to HIP?
What HIP and HIPIFY do
AMD describes HIP as “a C++ runtime API and kernel language for AMD GPUs” in its HIP Porting Guide. HIP provides an API and kernel-language model intended to make CUDA-style GPU code portable to AMD hardware. HIPIFY can translate much CUDA runtime code into HIP-oriented source, reducing repetitive edits in a large codebase.
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A practical migration sequence
- Inventory CUDA dependencies. Search the project for runtime calls, kernel launches, device-property queries, CUDA-specific libraries, inline assembly, custom operators and build scripts that assume NVIDIA compiler tools.
- Freeze a known-good baseline. Record model outputs, tolerances, training loss or evaluation metrics, throughput, latency, batch size, precision and memory use on the existing CUDA setup. Without a baseline, a port can appear successful while silently changing results or performance.
- Run HIPIFY on a branch. Use HIPIFY to translate the portions it recognizes. Preserve the original source and inspect every generated change rather than accepting a bulk replacement blindly.
- Resolve API and build differences. Replace unsupported calls, adjust architecture queries, update compiler and linker settings, and adapt libraries or extensions that have no HIP equivalent. The amount of manual work depends on how deeply the project relies on CUDA-specific behavior.
- Compile for the exact AMD target. Use the ROCm release and GPU listed in AMD’s compatibility matrix. Fix compile-time errors before interpreting runtime behavior.
- Validate correctness. Compare deterministic tests where possible, then compare representative training or inference outputs within an explicitly chosen tolerance. Exercise error paths, multi-GPU paths and checkpoint loading if the project uses them.
- Benchmark the complete workload. Measure data loading, preprocessing, transfers, kernel execution, synchronization and output handling, not just one kernel. Record the framework, precision, batch size, model version and deployment configuration.
- Document unsupported features. Keep a porting note that identifies any remaining CUDA-only component, workaround or performance-sensitive change. This prevents a later dependency upgrade from quietly reintroducing an NVIDIA requirement.
The HIP FAQ is available from AMD at HIP FAQ. Its guidance reinforces that HIPIFY does not eliminate all manual porting, particularly where CUDA has no direct HIP equivalent.
Is CUDA faster than ROCm?
There is no supported universal answer. The cited NVIDIA and AMD documents describe compatibility and porting, not a controlled cross-vendor benchmark. Performance depends on the exact GPU, model, framework build, kernels, precision, batch size, input pipeline, number of devices, interconnect, driver and deployment configuration.
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| Control | Keep identical | Why it matters |
|---|---|---|
| Workload | Same model revision, dataset or request mix, sequence lengths and evaluation procedure | Different inputs can change kernel selection and memory pressure. |
| Software | Equivalent framework release, application commit and dependency versions where each platform permits | A library change can look like a hardware advantage. |
| Numerics | Same precision, accumulation mode, quantization and deterministic settings | Lower precision can improve speed while changing accuracy. |
| Shape | Same batch size, input dimensions and concurrency | GPU utilization often changes sharply with workload shape. |
| Measurement | Warm-up period, timed iterations, synchronization rules and repeated trials | Asynchronous execution and startup costs otherwise distort results. |
| System | Comparable CPU, storage, data pipeline, power and thermal conditions | End-to-end latency includes more than GPU kernels. |
Report throughput, tail latency, peak memory, energy or cloud cost only when you measured them under these controlled conditions. If one platform needs a different framework version or unsupported optimization, state that limitation instead of presenting the numbers as a general CUDA-versus-ROCm verdict.
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Project scenarios and the safer choice
An existing CUDA training codebase
Stay with CUDA unless there is a specific reason to move. The existing GPU, driver, toolkit and binary extensions already form a tested path. A ROCm move introduces source translation, manual API work and a new validation burden. Consider migration only after identifying the business or infrastructure benefit and confirming that every critical dependency has a HIP or alternative implementation.
A new project with no vendor commitment
Shortlist hardware by the model’s memory requirement and the framework’s official installation path, then apply the architecture and operating-system matrices. Build a minimal proof of concept that imports the framework, allocates a tensor, runs a representative kernel and executes one end-to-end inference or training step. This catches unsupported combinations before the full application is built.
An AMD workstation or server already purchased
Check whether the exact GPU appears in the ROCm release matrix and whether the host meets its Linux requirements. If it does, install the framework build recommended for that ROCm release and test the project’s required libraries. If it does not, do not assume that a generic AMD package or a graphics driver will make the AI workload supported; select a supported software and hardware combination or reconsider the deployment target.
A portable product shipped to customers
Define supported configurations explicitly. A “CUDA or ROCm compatible” label is too broad unless it names GPU families, operating systems, toolkit or runtime versions, framework builds and excluded features. Provide a startup diagnostic that records the detected GPU and runtime, and fail with an actionable message when a required capability is absent.
Troubleshooting compatibility failures
The GPU is detected but the framework cannot use it
- Compare the detected GPU architecture with the toolkit or ROCm matrix, not just the vendor name.
- Check that the runtime loaded by the framework matches the version used to build its extensions.
- Verify the driver meets the toolkit’s minimum requirement or the ROCm release’s host requirements.
- Reinstall using the framework’s official instructions for the exact release; avoid mixing packages intended for different toolkit versions.
Installation succeeds but a custom operator fails
- Identify whether the operator contains CUDA-only headers, libraries, compiler flags or architecture-specific code.
- Look for a HIP implementation or port it deliberately; HIPIFY may translate routine API calls but cannot invent an equivalent for every CUDA capability.
- Rebuild the operator for the target GPU and run numerical tests before enabling it in production.
Results differ after a HIP migration
- Compare precision, accumulation and deterministic settings first.
- Reduce the test to one operation or kernel and compare intermediate tensors, not only final accuracy.
- Check synchronization and memory-order assumptions in translated code.
- Keep the CUDA baseline available so a change in model code can be distinguished from a backend change.
Performance is unexpectedly low
- Confirm that the application is using the intended GPU and not silently falling back to the CPU.
- Profile the full pipeline for data-transfer, input and synchronization stalls.
- Check that the framework selected kernels appropriate for the exact architecture and that the chosen batch size provides enough parallel work.
- Compare equivalent software versions and repeat warm runs before drawing a platform conclusion.
Questions to answer before committing
Use this checklist in a design review:
- What exact GPU model and architecture will run the workload?
- Which CUDA or ROCm release does the framework build require?
- Does the NVIDIA matrix or AMD matrix list that GPU for the selected release?
- Which operating system and kernel will be used in development and production?
- What minimum driver version is required?
- Do all custom operators and third-party libraries have a supported implementation?
- If migrating, which CUDA APIs or architecture queries need manual changes?
- What correctness tests define an acceptable port?
- Which benchmark conditions will be held constant?
- What is the rollback plan if a driver, toolkit or framework upgrade removes support?
CUDA is the direct fit for NVIDIA-first projects and established CUDA code. ROCm can be the right fit for an AMD-based project when the exact release matrix and framework guidance confirm support. Neither platform should be selected from a brand-level assumption or a generic claim that one is faster.
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