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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallTo check CUDA support, identify the exact NVIDIA GPU model, look it up in NVIDIA’s CUDA GPU Compute Capability table, and then compare the result with the driver, operating-system, toolkit, and application requirements. If NVIDIA lists your model in the current or legacy GPU table, the hardware is CUDA-capable. That does not automatically mean every CUDA program will run on it.
The decisive detail is the model and its compute capability—not simply the fact that the card is branded GeForce, Quadro, Tesla, or another NVIDIA product family.
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What CUDA support actually means
CUDA is NVIDIA’s platform for running general-purpose computing workloads on NVIDIA GPUs. NVIDIA describes CUDA as a standard feature in GeForce, Quadro, and Tesla GPUs and in NVIDIA GRID solutions in its CUDA FAQ. In practical terms, a CUDA-capable card contains the hardware features and instructions needed by CUDA software.
NVIDIA calls the hardware capability level compute capability (CC). The company defines it as the specification that determines a GPU’s general capabilities and available features. Compute capability is written as a major and minor number, such as 6.1, 8.6, or 12.0.
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That number answers a hardware question: which CUDA instructions and architectural features does this GPU provide? It is not the same thing as the CUDA Toolkit version installed on your computer, and it is not a guarantee that a particular application supports the card.
Check your exact NVIDIA GPU model first
A broad label such as “GeForce GTX” or “RTX” is insufficient. Several generations and dozens of models can share a family name while having different compute capabilities and software support. Record the complete model name, including its series and number, before doing the compatibility lookup.
Use nvidia-smi when the NVIDIA driver is installed
On Linux, Windows, and other systems where the NVIDIA driver exposes the utility, open a terminal or command prompt and run:
nvidia-smiThe command reports information about the installed NVIDIA GPU and driver. Read the full product name in the GPU listing; do not stop at a generic family label. If the computer has more than one NVIDIA GPU, note every model because applications can select a particular device.
The command may fail if the NVIDIA driver is not installed, the driver service is unavailable, the system is using a different graphics device, or the utility is not on the executable search path. A failure of nvidia-smi does not prove that the physical GPU lacks CUDA support; it means this identification method did not work.
Save the output when troubleshooting. It gives you a point-in-time record of the model and driver that an application can see. If a machine has several cards, repeat the check after selecting the intended device in the application, because a CUDA-capable secondary card does not help if the program is bound to another adapter.
Use your operating system’s hardware information
Every major operating system provides a hardware or device-information view that displays the adapter name. The exact labels and menu paths vary by operating-system version, so use the system’s device-information or graphics-adapter panel and copy the model exactly. If the operating system shows only “Microsoft Basic Display Adapter,” an abbreviated name, or an error state, install or repair the correct NVIDIA driver before treating that text as the GPU model.
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For laptops and workstations with integrated and discrete graphics, check that the reported adapter is the NVIDIA device rather than the integrated GPU. A program can also be configured to use one adapter while the operating system lists another, so identify the device that the intended CUDA workload will actually access.
Confirm unusual or older hardware carefully
Some products are sold under regional names, workstation branding, or OEM-specific names. Search NVIDIA’s tables using the complete model designation and, if necessary, the underlying GPU series. Do not infer support from a product’s age, price, or branding alone.
Mobile, OEM, and workstation variants can have names that look almost identical to desktop cards. Keep suffixes and qualifiers in your notes, then verify that the table row describes the same model or GPU variant. If the name on a box differs from the name reported by the driver, use the driver-reported device as the starting point and resolve the discrepancy before installing software.
Look up the model in NVIDIA’s official tables
Start with the current CUDA GPU table
- Open NVIDIA’s CUDA GPU Compute Capability page.
- Use the page’s model list or browser find function to search for the exact GPU name you recorded.
- Read the compute-capability value in the same row.
- Record the value in major.minor form, preserving both digits.
NVIDIA states on that page that compute capability defines the hardware features and supported instructions for each NVIDIA GPU architecture. A matching entry is the official evidence that the model is CUDA-capable.
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Older GPUs may not appear in the main list. Check NVIDIA’s separate legacy GPU table before concluding that the card is unsupported. Search by the exact model and verify that the row corresponds to your device rather than a similarly named model.
If the model is absent from both tables, treat that as unresolved rather than guessing. Recheck the model string, look for an OEM or mobile variant, and consult the GPU’s original NVIDIA documentation. A near match is not sufficient evidence because small model differences can correspond to different architectures.
A 60-second yes-or-no check
- Copy the complete model from
nvidia-smior the operating system’s adapter information. - Find that exact name in NVIDIA’s current table.
- If it is not there, search the legacy table.
- If either table provides a compute-capability value, answer “yes, the hardware is CUDA-capable.”
- For an application-specific answer, continue through the software checks below.
This separates the hardware verdict from the more demanding question of whether a particular program can run successfully.
Understand the compute-capability number
Compute capability is an architecture identifier. The major number generally distinguishes a broad generation of hardware features, while the minor number identifies a variant within that generation. The number is used by CUDA software to select the instructions and code path appropriate for a GPU.
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For example, NVIDIA lists these models and values:
| GPU model | Compute capability | How to interpret it |
|---|---|---|
| GeForce RTX 5060 | 12.0 | The model is listed as CUDA-capable with compute capability 12.0. |
| GeForce GTX 1080 | 6.1 | The model is listed in NVIDIA’s legacy table with compute capability 6.1. |
These are lookup examples, not universal recommendations. Suitability still depends on the CUDA application, driver, operating system, memory, and performance requirements.
Why software writes values such as sm_86
CUDA compiler architecture names remove the decimal point from the compute-capability number. A GPU with compute capability 8.6 is represented as sm_86. Thus, 6.1 corresponds to sm_61, and 12.0 corresponds to sm_120 when a tool uses that architecture notation. NVIDIA’s device-properties explanation documents this notation and the relationship between compute capability and supported instructions.
Do not read sm_86 as a toolkit version, driver version, or product number. It is an architecture target used when CUDA code is compiled or packaged. If a build log says that an sm_ target is missing, compare that target with the compute capability reported for your card and with the application’s supported architecture list.
What the number does and does not tell you
Compute capability tells you about architectural features and instruction support. It does not tell you the card’s graphics memory capacity, bandwidth, clock speed, power draw, or expected performance. It also does not identify the installed driver or toolkit. Two cards can both be CUDA-capable while differing substantially in the workload they can hold or the time they need to complete it.
Do not confuse hardware capability with the CUDA Toolkit version
The GPU table answers whether the hardware has CUDA capability. The CUDA Toolkit is the software development package containing compilers, libraries, headers, and related tools. The two numbers describe different things:
- Compute capability: the GPU architecture’s hardware features and instructions.
- Toolkit version: the version of NVIDIA’s software development environment installed on the system.
- Driver version: the installed driver’s software interface and supported features.
- Application requirement: the minimum architecture, driver, toolkit, operating system, and libraries required by one particular program.
NVIDIA’s CUDA Toolkit Archive listed Toolkit 13.4.2 as the latest release in September 2026. That is a dated release listing, not a statement that every GPU supports that toolkit or that installing it makes an unsupported GPU compatible. Check the archive and the release-specific installation notes before choosing a toolkit.
Installing a newer toolkit cannot add missing hardware instructions. Conversely, an older CUDA-capable card may work with a program that uses an older supported toolkit even when it is not supported by the newest release. Always match the versions documented for the application rather than choosing solely by the largest version number.
Verify that the complete CUDA setup can run your application
A listed model establishes hardware capability, but a working CUDA environment requires several compatible layers. NVIDIA’s getting-started guidance identifies a CUDA-capable GPU, a supported operating system with an appropriate compiler or toolchain, and the CUDA Toolkit as core requirements.
Check the application’s minimum compute capability
Find the target program’s own requirements and compare its minimum compute capability with the number from NVIDIA’s table. An application may drop support for older architectures even though those GPUs remain CUDA-capable in NVIDIA’s hardware list. Conversely, a program may require a newer architecture for a specific feature.
Look for wording such as “minimum CC,” “supported architectures,” or an architecture list containing values like sm_86. A card that meets the minimum number can still be excluded if the package was built only for a narrower set of targets, so check the application’s release notes and installation instructions as well as its headline requirements.
Check driver and operating-system support
Confirm that the NVIDIA driver branch supported by the application and toolkit is available for your operating system. Driver support can differ by operating-system release and GPU generation. A current driver is not automatically compatible with every legacy card, and an old driver may not provide the interface expected by a newer toolkit.
Record the driver version shown by nvidia-smi and compare it with the application’s documented minimum. On managed workstations, a system administrator may control driver updates; in that case, choose an application version that fits the installed driver instead of replacing software blindly.
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Check toolkit, compiler, and library requirements
Match the toolkit version, compiler, runtime libraries, and any framework-specific requirements to the application’s documentation. Some applications bundle a CUDA runtime; others require a separately installed toolkit. Follow the application’s versioned installation instructions rather than assuming that the newest toolkit is the correct one.
Do not assume that a successful toolkit installation means every library is available. Frameworks can require particular runtime components, and a package compiled for one architecture may omit another. Keep the toolkit and application versions together in your notes so a later update can be traced when behavior changes.
Check memory and other non-CUDA requirements
CUDA capability does not specify how much GPU memory the workload needs, how fast it will run, or whether the card supports every optional library. Compare the application’s memory, operating-system, CPU, storage, and display requirements separately. A card can pass the CUDA lookup and still be unsuitable because it lacks memory or performance for the workload.
For multi-GPU systems, also check how much memory is available on the specific device selected by the application. A program may not combine memory across cards automatically, and selecting a lower-memory adapter can cause an out-of-memory failure even when another installed card has enough capacity.
Hardware support versus a working CUDA setup
It helps to classify the result into three separate answers:
- Hardware answer: NVIDIA lists the exact model and assigns it a compute capability.
- Environment answer: the operating system, driver, toolkit or runtime, compiler, and libraries can expose that device correctly.
- Application answer: the particular program supports the architecture and has enough memory and other system resources.
Only the first answer comes directly from the GPU table. A “yes” at that stage is necessary, but it is not a promise that every CUDA application will install, launch, or perform well.
A practical decision procedure
- Identify the device: run
nvidia-smior use the operating system’s hardware information, then record the exact model. - Search NVIDIA’s current table: use the exact model on the CUDA GPU Compute Capability page.
- Search the legacy table: if no match appears, check NVIDIA’s legacy list.
- Record compute capability: write down the major.minor value and convert it to the application’s notation only when required, such as 8.6 to
sm_86. - Read the target application’s requirements: check its minimum compute capability, supported driver, operating system, toolkit, and libraries.
- Validate the installed environment: confirm that the driver sees the GPU, the required toolkit or runtime is installed, and the application is configured to use the NVIDIA device.
- Run the application’s own diagnostic: use its documented test or startup check. A successful hardware lookup alone is not a successful application installation.
Troubleshoot common results
“nvidia-smi is not recognized”
The utility may be missing from the path, the NVIDIA driver may not be installed, or the system may be using a different adapter. Locate the NVIDIA driver installation or use the operating system’s hardware-information view to identify the model. Do not use the command’s absence as a compatibility verdict.
“No devices were found”
Check whether the GPU is physically present and enabled, whether the driver is loaded, and whether the system is exposing the device to the environment where the command runs. Virtual machines, containers, remote sessions, and hybrid-graphics laptops can require additional device-passthrough or configuration steps. Resolve device visibility first, then repeat the model lookup.
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In a container or virtual machine, the host may see the card while the guest does not. That is a visibility and configuration problem, not evidence that the underlying model lacks CUDA support. Test from the same environment in which the application will run.
The model appears only in the legacy table
The GPU is still identified by NVIDIA as CUDA-capable, but the application or current toolkit may impose newer minimum requirements. Use the model’s compute capability as the starting point and verify the specific software’s supported architecture and driver range.
The application says the GPU is unsupported
Compare the application’s stated minimum compute capability with the value in NVIDIA’s table. If the value is high enough, check for a driver mismatch, unsupported operating-system release, missing runtime library, incompatible toolkit, or a package built without your architecture. The application’s error message and installation documentation should determine the next step.
The application detects the GPU but fails during execution
Detection proves only that the device is visible. Check the application’s memory requirement, required CUDA libraries, architecture targets, and driver compatibility. A program can recognize a CUDA-capable device yet fail when it requests an unsupported instruction, an unavailable library, or more memory than the card provides.
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Do not substitute a similar model or assume that a family-level match is enough. Check spelling, mobile or OEM suffixes, and the legacy list. If the exact device remains unresolved, use NVIDIA’s original product documentation; report the result as “not confirmed” rather than claiming that it is unsupported.
When an upgrade is the right answer
Consider replacing the GPU only after establishing which requirement is failing. Compare candidate cards on the exact axes that matter to the workload:
- Minimum compute capability required by the application.
- Driver and toolkit versions supported by the application.
- Operating-system and compiler compatibility.
- GPU memory required by the workload.
- Physical fit, power delivery, cooling, and system firmware.
- Whether the application supports the candidate architecture rather than merely listing it in NVIDIA’s general GPU table.
NVIDIA’s table lists the GeForce RTX 5060 at compute capability 12.0, making it a concrete model to verify when comparing hardware. The table alone does not establish that this card is suitable for every workload or compatible with every computer. Confirm the target program’s current requirements and the rest of the system before purchasing.
If the failing requirement is only an old driver or toolkit, a software change may be less expensive than a GPU replacement. If the application requires a higher compute capability or more memory than the current card provides, compare cards against that precise requirement rather than buying on brand or series name alone.
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Keep the result reproducible
Record the exact GPU model, compute capability, driver version, operating-system version, toolkit version, and application version when you perform the check. Software support changes over time, while the hardware’s compute capability does not. Keeping those values together makes it easier to diagnose a future update that drops an architecture or changes a minimum driver requirement.
Recheck the official NVIDIA tables and the application’s versioned requirements whenever you change the GPU, driver, toolkit, operating system, or application release. A CUDA-capable GPU is the necessary hardware foundation; it is only one part of a compatible CUDA stack.
Frequently Asked Questions
How can I tell quickly whether my NVIDIA GPU supports CUDA?
Run nvidia-smi or read the exact adapter name in your operating system, then search that complete model in NVIDIA’s current CUDA GPU table. If it is not listed, check the legacy table. A matching row with a compute-capability value confirms CUDA-capable hardware.
Is every GeForce or RTX card CUDA-compatible?
Do not rely on the family name alone. NVIDIA’s FAQ describes CUDA as a standard feature across GeForce and other NVIDIA product families, but the exact model and architecture determine the documented capability and the software versions that can use it.
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It is the GPU’s compute capability, an architecture identifier for hardware features and supported instructions. CUDA compiler targets remove the decimal point, so compute capability 8.6 is written as sm_86.
Does a CUDA-capable GPU run every CUDA application?
No. The application may require a newer minimum compute capability, a particular driver or toolkit, a supported operating system, specific libraries, or more GPU memory than the card has.
What if my GPU appears only in NVIDIA’s legacy table?
The model is still identified as CUDA-capable, but current applications and toolkits may no longer support its architecture. Compare its compute capability with the target program’s minimum and verify the supported driver and toolkit versions.
Does installing the newest CUDA Toolkit add CUDA support to an old card?
No. A toolkit supplies software tools and libraries; it cannot add hardware instructions that the GPU does not provide. Choose a toolkit and application version that explicitly support the card’s compute capability.
Why does nvidia-smi show no devices?
The driver may be missing or inactive, the GPU may be disabled, or a virtual machine, container, remote session, or hybrid-graphics configuration may not expose the device. Fix visibility and driver issues first; the command failure is not a hardware compatibility verdict.
What should I compare before buying a replacement GPU for CUDA?
Compare the application’s required compute capability, supported driver and toolkit versions, operating-system and compiler support, GPU memory, physical fit, power, cooling, and whether the application supports the candidate architecture. A general NVIDIA table entry alone is not enough.
The Bottom Line
Bottom line: identify the exact NVIDIA GPU, find it in NVIDIA’s current or legacy CUDA table, and note its compute capability. Then verify the target application’s architecture, driver, operating-system, toolkit, library, and memory requirements. The table confirms CUDA-capable hardware; only the complete software and system check confirms that your application can run.
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