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Do not begin by swapping GPUs. Begin by mapping every CUDA dependency, then verify a complete target configuration—GPU, operating system, kernel and driver, ROCm or other platform version, framework, and serving stack. Port a representative workload, test numerical correctness and production behavior, and keep the NVIDIA path available until predefined acceptance criteria are met. A framework may hide much of the hardware layer, but custom kernels, extensions, libraries, and runtime assumptions often determine the real migration effort.
Why a platform migration is more than a hardware change
An AI application is a stack, not a single model file. It includes model code, data preprocessing, framework operators, custom kernels, third-party extensions, compilers, container images, drivers, monitoring, and deployment automation. NVIDIA CUDA can appear in any of those layers. Replacing the accelerator without identifying those connections can produce a system that installs successfully but fails at runtime, gives different results, or misses its latency and throughput targets.
NVIDIA’s CUDA Compatibility documentation covers specific driver and toolkit relationships within supported NVIDIA configurations. Minor Version Compatibility and Forward Compatibility can reduce upgrade coordination in those configurations; they are not a cross-vendor binary-compatibility layer. A CUDA binary should therefore be treated as NVIDIA-specific until each component has been rebuilt or replaced for the target platform.
AMD describes ROCm as its software stack for supported GPUs and HIP as a route for moving CUDA source. Its ROCm Software overview states: “The tools and APIs are not directly interchangeable, so migration effort depends on the application and its dependencies.” That is the practical starting point for any business case: estimate work from the workload’s dependencies, not from the number of model files.
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Inventory the workload’s actual CUDA coupling
Create an inventory before selecting replacement hardware. Record the version and purpose of every item, where it runs, and whether a maintained alternative exists on the target platform.
Runtime, libraries, and containers
- Capture the CUDA runtime, toolkit, compiler, driver, and base container image versions.
- List calls into cuDNN, cuBLAS, cuFFT, NCCL, TensorRT, and any other NVIDIA library. Include transitive dependencies pulled in by framework wheels or containers.
- Record environment variables, device-selection logic, compute-capability checks, launch scripts, and health probes that assume NVIDIA device names or utilities.
- Identify CUDA-specific build flags, architecture lists, PTX or binary artifacts, and precompiled wheels. These often fail even when high-level Python code appears portable.
Framework and extension boundaries
- Separate ordinary framework operations from custom operators, fused kernels, attention implementations, quantization code, and third-party extensions.
- Check whether preprocessing, tokenization, augmentation, compression, or post-processing uses GPU code rather than the CPU.
- List serving components and plugins, including batching, scheduler, communication, and telemetry modules. A framework may support the target GPU while a serving extension does not.
- Locate tests that compare device types, query architecture properties, or skip code paths unless CUDA is present.
Architecture assumptions
Search source and build files for warp-size assumptions, shared-memory limits, tensor-core instructions, atomic operations, alignment requirements, and hard-coded architecture names. HIP can translate many common CUDA API patterns, but architecture queries and unsupported CUDA capabilities require manual work. Treat every custom kernel as code that needs review, not as a file that can be mechanically renamed.
Choose a supported target configuration, not just a GPU model
After identifying dependencies, select a candidate platform and validate the entire combination in the vendor’s release-specific support matrix. AMD publishes matrices for ROCm releases, including separate references for releases such as ROCm 10.0.0 and ROCm 6.4.1, and a Linux matrix for Radeon hardware. A component listed in one release does not prove support for another release or operating-system combination.
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| Compatibility item | What to verify | Why it can block migration |
|---|---|---|
| GPU | Exact model, memory capacity, supported interconnect, and architecture | A supported family does not guarantee that every model or feature is enabled. |
| Operating system | Distribution and release, kernel version, and installation method | Driver modules and packaged runtimes are often OS- and kernel-specific. |
| Driver and runtime | ROCm release, kernel driver, user-space libraries, and compiler versions | Framework packages are built and tested against particular runtime combinations. |
| Framework | Exact PyTorch, TensorFlow, or JAX build and supported operators | A framework name alone does not guarantee that your version, operator set, or extension works. |
| Serving stack | Exact vLLM, SGLang, or other server version, plugins, and launch options | Inference servers can expose narrower support than the underlying framework. |
| Distributed layer | Collectives, topology, process launcher, and multi-GPU configuration | Single-device success does not establish multi-device scaling or stability. |
AMD identifies PyTorch, TensorFlow, and JAX for AI development and vLLM and SGLang for serving within ROCm. The support statement is conditional on the ROCm release and system configuration. Check the matrix again whenever you change a pinned component; compatibility is a property of the complete version tuple, not a permanent property of a product name.
Example: evaluating an AMD candidate
An AMD Radeon PRO W7900 appears in AMD’s Radeon ROCm compatibility documentation. That establishes a compatibility listing, not equivalence to a particular NVIDIA accelerator, suitability for every model, current availability, or a guaranteed price. Before procurement, match that model to the exact ROCm release, Linux distribution, framework build, and serving configuration you intend to operate.
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Select the least risky porting route
Framework-first migration
If your application uses supported framework APIs and has few custom extensions, install the vendor-supported framework build for the target runtime and run the existing test suite. Replace CUDA-only packages with target-supported builds, then address failures one operator or extension at a time. This route can preserve model code, but it still requires validation of numerics, memory behavior, and performance.
HIP and HIPIFY for direct CUDA source
For C++ or CUDA kernels, HIP provides a source-level migration path and HIPIFY can convert supported API patterns. Conversion is a starting point, not a completed port. Inspect every converted file, compile it on the target, resolve unsupported calls, and review architecture-specific logic. AMD’s HIP FAQ specifically calls out architecture queries and unsupported CUDA capabilities as cases needing additional porting.
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitches- Freeze a baseline. Record the current compiler, driver, framework, model revision, data set, precision, batch or concurrency, throughput, p50/p95/p99 latency, memory use, utilization, and error rate.
- Convert a small slice. Port one kernel or extension and compile it with the target toolchain. Keep the original implementation available behind a build or runtime switch.
- Run correctness tests. Compare deterministic outputs where possible and use tolerances appropriate to the chosen precision. Check loss curves, generated text or images, top-k decisions, and post-processing—not just whether the process exits.
- Expand to the full graph. Port remaining operators, rebuild containers, and test checkpoint loading, serialization, distributed initialization, and recovery.
- Tune after correctness. Revisit launch geometry, memory movement, fusion, precision, communication, and batching on the target architecture. A mechanically translated kernel can be correct yet slow.
Measure the workload that matters
Peak theoretical compute is not a migration result. Use the same model revision, data, precision, batch or concurrency mix, and service-level target on both platforms. Warm up each system, document software versions, and repeat runs sufficiently to expose variance.
| Measure | Questions to answer |
|---|---|
| Correctness | Are outputs, quality metrics, loss, and numerical tolerances acceptable? |
| Throughput | How many samples, tokens, or training steps are completed per second? |
| Latency | What are p50, p95, and p99 latency under the intended concurrency? |
| Memory | What is peak allocation, fragmentation, and remaining headroom at production batch size? |
| Utilization | Are the GPU and host saturated, or is the workload waiting on input, transfers, or synchronization? |
| Scaling | How does time-to-result or throughput change from one GPU to several, and what is the communication overhead? |
| Reliability | Do long runs, restarts, checkpoint recovery, and upgrades remain stable? |
| Operating cost | What is cost per useful inference, training step, or completed job after host, storage, power, and operations are included? |
Set acceptance thresholds before benchmarking. For example, specify the maximum permitted quality drift, minimum throughput, p99 latency ceiling, memory headroom, multi-GPU efficiency, error rate, and cost per useful result. If a target misses one threshold, record whether software work, a different configuration, or a different platform is the remedy; do not hide the miss behind an average score.
Compare total migration risk and cost
Build a decision sheet with separate rows for software coverage, engineering effort, hardware capacity and bandwidth, precision-specific behavior, interconnect and scaling, availability, reliability, and operations. Mark each row as verified, needs a proof of concept, or unsupported. This prevents a high specification in one category from masking a blocking dependency in another.
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Include one-time and recurring costs: porting labor, parallel infrastructure during validation, new monitoring and deployment work, capacity reservations, support contracts, power and cooling, and the cost of maintaining two code paths. A cheaper accelerator can have a higher cost per useful result if it requires extensive kernel work or delivers lower throughput at the required latency.
Do not import generalized migration-cost, compatibility-rate, or speedup percentages from unrelated workloads. The available technical guidance does not establish a universal performance or cost winner among AMD, Intel, cloud accelerators, and NVIDIA. Only a reproducible benchmark using your model and service target can answer that question.
Run a reversible proof of concept
- Choose a representative slice. Include the largest model, critical custom operators, realistic input lengths, production batch or concurrency, and the deployment path you actually plan to use.
- Keep the NVIDIA implementation deployable. Use separate images, environments, or feature flags so the experiment cannot interrupt the working service.
- Automate parity checks. Run the same test data and compare outputs, quality, latency distributions, memory, and errors on every candidate build.
- Exercise failure paths. Test out-of-memory behavior, worker restarts, node replacement, checkpoint recovery, and rolling upgrades.
- Review evidence at a gate. Proceed only when all acceptance criteria pass and the remaining risks have named owners and rollback steps.
This staged approach is operational advice rather than a universal schedule. The appropriate duration depends on model complexity, custom code, deployment scale, and how much parallel infrastructure you can afford.
Common failure modes and fixes
The framework installs but an operator is missing
Confirm the exact framework and ROCm versions in the support matrix, then identify the first unsupported operator. Replace it with a supported implementation, move that operation to the CPU when practical, or port the custom kernel. Do not infer support from a successful package installation.
HIPIFY produces code that does not compile
Inspect the conversion output for unsupported APIs, architecture queries, inline assembly, and assumptions about memory or synchronization. Apply manual changes, compile with target architecture settings, and add focused tests before integrating the kernel into the full model.
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Correctness passes but performance is poor
Profile host-to-device transfers, kernel occupancy, memory access, synchronization, communication, and input pipelines. Reconsider fusion, precision, launch parameters, and batching for the target architecture. Performance tuning belongs after correctness because optimizing an incorrect result is wasted work.
Single-GPU tests pass but distributed training fails
Validate the collective communication library, process launcher, topology, peer access, and failure recovery in the exact multi-GPU configuration. Measure scaling efficiency instead of assuming that single-device throughput multiplies linearly.
Container or deployment checks still expect NVIDIA
Replace CUDA base images, device plugins, health checks, monitoring exporters, and environment variables as a coordinated change. Search infrastructure-as-code and CI pipelines, not only application repositories.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Questions to answer before approval
- Can every production-critical operator and extension run on the candidate configuration?
- Which components remain vendor-specific, and who will maintain each path?
- Does the target meet quality, p99 latency, throughput, memory, stability, scaling, and cost thresholds on representative jobs?
- Can the team roll back without rebuilding the model or losing checkpoints?
- Have the exact GPU, OS, kernel, runtime, framework, and serving versions been recorded and tested together?
Bottom line
Migrating AI workloads away from NVIDIA is feasible when the workload is treated as a dependency graph and the target as a versioned system. ROCm and HIP provide a documented path for supported AMD hardware, but neither makes CUDA code a drop-in portable binary. Inventory CUDA coupling, verify the release-specific matrix, port and inspect custom code, benchmark production-like jobs, and cut over only after a reversible proof of concept meets workload-specific acceptance criteria.
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Frequently Asked Questions
Can a CUDA workload run on an AMD GPU without code changes?
Only in limited cases where the framework, operators, extensions, and deployment stack already have supported ROCm builds. CUDA binaries and CUDA-only libraries are not made portable by NVIDIA’s compatibility mechanisms, so the exact workload must be checked component by component.
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How much code must be changed to move from CUDA to ROCm?
There is no reliable percentage. Framework-only applications may need mainly environment and dependency changes, while custom kernels, architecture queries, unsupported APIs, and CUDA-specific extensions can require substantial manual porting and tuning.
Does HIPIFY fully convert a CUDA application?
No. HIPIFY converts supported source patterns. The converted code still needs inspection, target compilation, handling for unsupported capabilities, correctness tests, and performance optimization.
Which AI frameworks are available on ROCm?
AMD identifies PyTorch, TensorFlow, and JAX for AI development, and vLLM and SGLang for serving. Availability and operator support depend on the exact ROCm release and system configuration.
What is the safest way to decide whether to switch platforms?
Run a representative proof of concept against predefined thresholds for output quality, throughput, p50/p95/p99 latency, memory headroom, scaling, stability, and cost per useful result while keeping the NVIDIA deployment ready for rollback.
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