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You can run many AI models without NVIDIA CUDA. PyTorch provides a CPU path, AMD GPUs can use ROCm, Apple-silicon Macs use the Metal Performance Shaders (MPS) backend, and supported Intel GPUs use the XPU/SYCL stack. The reliable process is to identify your hardware and operating system, install the matching framework build, select the correct device in code, and then test operators, memory, numerical output, and speed on your actual model.
CUDA-dependent custom kernels and compiled extensions are the main complication. Basic tensor and neural-network operations may work after changing the device, while projects that call CUDA libraries directly require a supported alternative, a port, or removal of the extension.
What changes when CUDA is unavailable?
CUDA is both a programming platform and a software ecosystem used by many machine-learning libraries. A project may depend on it in several ways:
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- Framework tensor operations may select a CUDA device explicitly.
- Custom C++ or CUDA kernels may be compiled as part of the project.
- Third-party packages may assume CUDA libraries, CUDA-specific fused operators, or NVIDIA-only quantization support.
- Scripts may hard-code memory-management calls, device names, or performance settings that exist only on CUDA.
Alternative backends replace different portions of that stack. A CPU build offers the broadest portability but no GPU acceleration. ROCm supplies AMD-specific compilers, runtimes, libraries, and the HIP programming layer. MPS maps PyTorch operations to Apple’s Metal Performance Shaders on Apple silicon. XPU uses Intel’s SYCL software stack for supported Intel GPUs.
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Consequently, “install PyTorch without CUDA” is not one universal procedure. The correct package, driver, operating-system version, device name, supported data types, and available operators depend on the hardware.
Choose the execution path for your hardware
| Backend | Supported hardware and software path | Availability check | Best fit | Main limitation to plan for |
|---|---|---|---|---|
| CPU | Any system supported by the CPU PyTorch build | torch.device("cpu") |
Portable inference, small models, development, and fallback execution | Usually lower throughput and higher latency than a suitable accelerator |
| AMD ROCm | Supported AMD GPUs with a compatible ROCm, driver, operating-system, and framework combination | Use the ROCm/PyTorch installation and device check documented for the selected build; many PyTorch ROCm builds expose the accelerator through the CUDA-compatible API | Local AMD GPU training and inference, including major AI frameworks and serving tools | Exact GPU and operating-system support must be verified; CUDA-only extensions may still need porting |
| Apple MPS | Apple-silicon Mac, macOS 14 or later, Python 3.10 or later, and Xcode command-line tools for the stable release described by Apple’s current guide | torch.backends.mps.is_available() |
Local development and inference on Apple-silicon Macs | Operator coverage, memory behavior, and precision support vary by model |
| Intel XPU | Supported Intel client or data-center GPU using Intel’s SYCL software stack | torch.xpu.is_available() |
Intel GPU workloads using the official PyTorch XPU path | Some third-party extensions remain CUDA-oriented and require alternatives or source changes |
Do not choose a backend from the chip brand alone. Check the exact GPU model, operating-system release, driver, framework version, and supported memory and precision modes before deploying.
CPU: the compatibility-first fallback
PyTorch’s installation selector includes an explicit CPU option for systems without a CUDA-capable or ROCm-capable GPU. Select that build when portability matters more than acceleration, when the model is small, or when you need a dependable fallback for unsupported operators.
CPU execution is also useful as a reference implementation. Run a small batch on the CPU, save its output, and compare accelerator results against it within an appropriate numerical tolerance. That comparison helps distinguish a backend bug from an error in preprocessing or model conversion.
AMD ROCm and HIP
ROCm is an open software stack containing programming models, tools, compilers, libraries, and runtimes for AI and high-performance computing on AMD GPUs. AMD documents support for PyTorch, TensorFlow, JAX, vLLM, and SGLang.
For framework installation, AMD recommends official prebuilt container images because they package the framework and ROCm dependencies together. Containers reduce dependency drift, but they do not remove the need to verify that your exact GPU, host driver, operating system, and model are supported.
Radeon users should consult the Radeon and Ryzen support matrix before installing. A graphics card that physically fits a workstation may still lack support for the particular ROCm release or operating-system combination you intend to use.
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In PyTorch, ROCm builds commonly use the cuda device namespace for compatibility, even though the underlying runtime is ROCm. Do not infer backend support solely from the spelling of that namespace; identify the installed build and test it on the target GPU.
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Apple Metal and MPS
Apple’s documentation states that “PyTorch uses the Metal Performance Shaders (MPS) backend for GPU acceleration.” The current stable-release guide describes Apple-silicon Macs running macOS 14 or later, Python 3.10 or later, and Xcode command-line tools.
MPS uses unified memory rather than a separately installed graphics-card VRAM pool. A model that fits in one machine may fail on another with less available unified memory, especially when the operating system and other applications are active. Test the complete workload, not only the parameter count.
MPS support is model-dependent. An operation can be implemented on the CPU while the rest of the model runs on MPS, or it can fail if no fallback exists. Precision behavior also differs from CUDA, so validate output quality instead of assuming identical results.
Intel XPU and SYCL
PyTorch’s Intel GPU guide places Intel GPU support in the official PyTorch stack and uses Intel’s SYCL software stack. The documented readiness test is torch.xpu.is_available().
Install the Intel-specific framework components for the intended client or data-center GPU, then run representative operators. CUDA-oriented extensions, fused attention packages, and quantization libraries may not have XPU implementations. Treat each extension as a separate compatibility item.
Install the matching framework and runtime
- Record the target. Write down the exact chip or GPU model, available VRAM or unified memory, operating-system version, driver version, Python version, and the framework version required by the project.
- Use the framework’s installation selector or vendor instructions. Choose CPU, ROCm, MPS, or XPU rather than selecting a CUDA wheel by habit. For ROCm, prefer the official prebuilt container image when one matches your framework and operating system.
- Keep the environment isolated. Use a new virtual environment or a container for each backend experiment. Mixing packages from CPU, CUDA, and vendor-specific builds can leave an environment that imports successfully but fails at runtime.
- Install vendor prerequisites. ROCm requires a compatible host driver and ROCm release. MPS requires the Apple operating-system and command-line-tool prerequisites documented for the PyTorch release. XPU requires the Intel runtime and driver components described by its installation guide.
- Confirm the imported build. Print the framework version and backend availability before loading a large model. This catches an accidentally installed CPU package early.
import torch
print("PyTorch:", torch.__version__)
print("CUDA-compatible device API:", torch.cuda.is_available())
print("MPS built:", hasattr(torch.backends, "mps") and torch.backends.mps.is_built())
print("MPS available:", hasattr(torch.backends, "mps") and torch.backends.mps.is_available())
print("XPU available:", hasattr(torch, "xpu") and torch.xpu.is_available())The CUDA-compatible check in this diagnostic is not a universal test for every backend. Use the backend-specific checks as well: MPS availability for Apple silicon and torch.xpu.is_available() for Intel GPUs. ROCm support should be verified with the ROCm installation and the device behavior of the installed PyTorch build.
Select a device without hard-coding CUDA
Replace strings such as "cuda:0" scattered through a project with one selected device. The following function prefers an available accelerator and otherwise returns the CPU. Adjust the priority to match your deployment policy.
import torch
def choose_device():
# ROCm PyTorch builds commonly expose their GPU through torch.cuda.
if torch.cuda.is_available():
return torch.device("cuda")
if hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
return torch.device("mps")
if hasattr(torch, "xpu") and torch.xpu.is_available():
return torch.device("xpu")
return torch.device("cpu")
device = choose_device()
print("Using", device)
model = model.to(device)
inputs = inputs.to(device)
with torch.inference_mode():
outputs = model(inputs)Some applications should prefer a specific backend rather than the first available one. For example, a workstation may have an Intel GPU and a CPU, while a test runner should intentionally force CPU execution for reproducibility. Make the choice configurable through a command-line option or environment variable, then fail with a clear message if the requested backend is unavailable.
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When a model returns several tensors or a nested structure, move every input tensor to the selected device. Non-tensor metadata such as token lists and lengths may remain on the host unless the model expects them as tensors.
Run a small test before loading the full model
- Test allocation. Create a small tensor on the selected device and perform a basic operation.
- Test the model graph. Run one realistic batch with the same preprocessing, sequence length, image size, and generation settings used in production.
- Check memory growth. Repeat the batch enough times to expose leaks or caching behavior. A model that succeeds once can still fail during a long-running service.
- Compare outputs. Save CPU or known-good reference output and compare values with a tolerance appropriate to the model and data type.
- Measure the real workload. Record warm-up time, steady-state latency, throughput, and peak memory. There is no universal speed ranking across CPU, ROCm, MPS, and XPU; results depend on the model, batch size, precision, driver, and preprocessing.
import time
import torch
x = torch.randn(8, 3, device=device)
y = x @ x.T
print("Smoke-test shape:", y.shape)
model.eval()
for _ in range(3):
with torch.inference_mode():
_ = model(inputs)
if device.type == "cuda":
torch.cuda.synchronize()
start = time.perf_counter()
with torch.inference_mode():
outputs = model(inputs)
if device.type == "cuda":
torch.cuda.synchronize()
elapsed = time.perf_counter() - start
print("Batch seconds:", elapsed)Synchronization matters for asynchronous GPU execution. Without it, a timer may stop before the device has finished. The example uses CUDA synchronization for devices exposed through that API, including many ROCm builds. Use the synchronization and profiling method documented for the specific XPU or MPS release when collecting production measurements.
Port projects that contain CUDA-only code
1. Replace hard-coded device assumptions
Search for cuda strings, calls to CUDA memory-management functions, device-count queries, and code that assumes a particular GPU index. Route those operations through the selected-device abstraction. Avoid calling a CUDA-only API merely to discover whether an accelerator exists.
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Look for custom .cu files, build scripts that invoke NVIDIA’s compiler, packages that ship precompiled CUDA binaries, and imports of fused attention or quantization modules. A model can appear portable while one optional import prevents startup.
3. Select an implementation for each extension
- Use a ROCm/HIP implementation when the project provides one.
- Use an MPS or XPU implementation when the project documents it.
- Use a Vulkan or CPU implementation if the application supports that route.
- Port the kernel and its build process when no maintained alternative exists.
- Remove the optimization and use a standard framework operator when correctness is more important than peak speed.
4. Review precision and quantization
Do not assume that a CUDA-focused float16, bfloat16, or quantization path has equivalent support elsewhere. Test the exact data type on the target backend, check for silent promotion to another type, and compare model quality against the reference path.
5. Isolate optional features
Load optional accelerators only after the backend has been selected. A project should be able to start in CPU mode even when an unavailable CUDA extension is not installed. This design also provides a recovery path for unsupported operators.
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Hardware and software record
- Exact chip or GPU model
- VRAM or unified-memory capacity and the amount available during the run
- Operating-system version
- Driver and vendor-runtime version
- Python and framework versions
- Backend version, such as ROCm, the Apple MPS-capable macOS release, or Intel’s XPU runtime
Operator coverage
Run every model branch, not only the first forward pass. Test attention, normalization, convolutions, embedding operations, sampling, post-processing, and any custom layers. Unsupported operations may produce an explicit error, fall back to the CPU, or behave differently depending on the backend and framework release.
Numerical agreement
Compare logits, scores, generated tokens, or final predictions with a known-good reference. Use tolerances that reflect the selected precision. Investigate large discrepancies before measuring speed; a faster result that changes predictions is not a successful port.
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Memory behavior
Check peak allocation during the largest expected request, not just model loading. Include input buffers, activation memory, key-value caches for generation, and concurrent requests. On Apple silicon, account for other applications sharing unified memory.
Serving readiness
Confirm that the serving framework supports the backend you selected. ROCm documentation covers major frameworks and inference tools, but support still depends on the particular release and model. XPU and MPS may require framework-native serving or a different deployment design when a third-party server assumes CUDA.
Troubleshoot common failures
“No accelerator is available”
Verify that the installed framework build matches the backend, that the driver is loaded, and that the process sees the device. Print framework and runtime versions, then run the vendor’s basic device diagnostic. On MPS, check both that the backend was built and that it is available at runtime.
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Find the first failing operator in the traceback. Replace it with a standard framework operation, move only that operation to the CPU if the data transfer cost is acceptable, install a backend-supported extension, or use the CPU path for the entire model. Re-test output quality after each change.
Out-of-memory errors
Reduce batch size, sequence length, image resolution, or generation length; disable retained activations during inference; and use a supported lower-precision mode only after validating accuracy. On unified-memory systems, close competing applications and monitor system memory rather than looking only for a separate VRAM meter.
Unexpectedly slow execution
Measure warm-up separately from steady-state batches, confirm that inputs and the model are on the same device, and check for hidden CPU fallbacks or repeated host-device transfers. A small model may run faster on the CPU because launch and transfer overhead dominates.
A CUDA extension blocks startup
Make the import conditional on the selected backend. Install the extension’s ROCm, MPS, or XPU variant if one exists; otherwise disable the feature and use a standard operator. If the extension is mandatory, the project requires a source port before it can run on that hardware.
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- Choose CPU when maximum portability, simple deployment, or reliable operator coverage outweighs acceleration.
- Choose ROCm when you have a supported AMD GPU and want a full GPU software stack with major framework and inference-tool support.
- Choose MPS for local development and inference on an Apple-silicon Mac, provided the model’s operators and memory needs fit the backend.
- Choose XPU when the target is a supported Intel GPU and the project’s dependencies have Intel-compatible implementations.
For production, select the backend only after measuring the actual workload and confirming maintenance requirements. A theoretical accelerator advantage can disappear if an unsupported extension forces CPU fallback or if the deployment toolchain lacks a stable serving path.
Quick Recap
Practical deployment checklist
- Identify the exact hardware, operating system, driver, framework, and backend versions.
- Install the matching CPU, ROCm, MPS, or XPU build in an isolated environment.
- Run backend-specific availability checks.
- Use one device-selection variable instead of hard-coded CUDA strings.
- Run a tensor smoke test and one representative model batch.
- Exercise every important operator, precision mode, and model branch.
- Compare outputs with a known-good reference.
- Measure warm-up, latency, throughput, and peak memory on the real workload.
- Document fallbacks and recovery steps for unsupported operators or memory failures.
- Recheck the hardware and software support matrix before upgrading the driver, framework, or model.
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