Why CUDA Code Works on Some GPUs but Fails on Others

Introduction: The “Write Once, Run Anywhere” Illusion in GPU Computing

CUDA is often treated as portable by default. Developers write kernels, compile them, and assume they’ll run anywhere an NVIDIA GPU is present. In practice, that assumption breaks quickly.

A training job runs perfectly on a developer’s workstation with an RTX card, but crashes in production on a data center GPU. A scientific workload compiled two years ago suddenly fails after a driver upgrade. A cloud deployment scales to a different GPU class and starts throwing obscure launch errors.

This is not rare. It’s structural.

CUDA is portable within defined boundaries. Those boundaries are shaped by GPU architecture, compute capability, compilation targets, driver versions, hardware limits, and environment constraints. When those variables shift, code that previously worked can fail  sometimes loudly, sometimes silently.

If you build or scale CUDA workloads, especially in cloud or multi-GPU environments, understanding these differences is not optional. It’s operational hygiene.

CUDA Compatibility Basics

What CUDA Actually Guarantees  and What It Doesn’t

CUDA guarantees compatibility at the software ecosystem level, not universal binary portability across all GPUs forever.

NVIDIA maintains:

• A CUDA Toolkit (compiler, libraries, headers)
• A driver stack
• A set of GPU architectures with defined compute capabilities

The guarantee is scoped:

• Newer drivers can generally run applications built with older toolkits (forward driver compatibility).
• Newer GPUs can run binaries compiled correctly with PTX included.

What CUDA does not guarantee:

• That code compiled for a specific architecture will run on all GPUs.
• That hardware resources required by your kernel exist on all devices.
• That deprecated APIs will continue to function indefinitely.

There’s a difference between runtime compatibility (driver + toolkit alignment) and hardware compatibility (architecture + compute capability support). Most failures occur in the second category.

GPU Architecture Differences

Streaming Multiprocessor (SM) Evolution

Every NVIDIA GPU generation changes the internal design of its Streaming Multiprocessors (SMs). Differences include:

• Warp scheduling
• Shared memory structure
• Register file size
• Cache hierarchy
• Tensor Core design
• Instruction throughput

Consider major architecture shifts:

• Volta
• Turing
• Ampere
• Ada
• Hopper

Each introduced structural changes  not just performance improvements.

For example:

• Volta introduced independent thread scheduling.
• Ampere expanded Tensor Core precision modes.
• Hopper introduced new memory hierarchy and scheduling features.

If your kernel implicitly relies on scheduling behavior, synchronization assumptions, or specific hardware throughput characteristics, those assumptions can break when the architecture changes.

Newer architectures don’t just run old code faster. They execute it differently.

GPU Architecture Evolution Comparison

Compute Capability Mismatch

What “Compute Capability” Means

Compute Capability (e.g., 7.0, 8.6, 9.0) defines the feature set supported by a GPU. It determines:

• Available instructions
• Tensor Core support
• Atomic operations
• Memory model features
• Warp-level primitives

When compiling CUDA code, you target specific compute capabilities.

If:

• Your binary doesn’t include support for the target GPU’s compute capability
• Or your code requires features unavailable on that GPU

You’ll get compile-time or runtime failures.

Common scenario:

• Code compiled only for sm_75 (Turing)
• Deployed to sm_90 (Hopper)
• Fails because no compatible binary or PTX fallback was included

Or:

• Kernel uses a feature introduced in compute capability 8.0
• Deployed to a 7.0 GPU
• Compilation fails

Compute capability is not a minor detail. It’s the contract between your kernel and the hardware.

CUDA Version and Driver Issues

Toolkit vs Driver Version

Two versions matter:

• CUDA Toolkit version (nvcc, libraries)
• NVIDIA driver version

Forward compatibility works within limits:

• New drivers can usually run older CUDA applications.
• Old drivers cannot run applications compiled with newer toolkits.

Common failure patterns:

• Application built with CUDA 12.x
• Deployed on a system with older drivers
• Runtime error: unsupported PTX version

Cloud environments often expose this quickly. Developers assume local and production driver versions match. They often don’t.

Always check:

• nvidia-smi

• nvcc –version

Version drift is one of the most common production failures.

The following table demonstrates the toolkit vs driver matrix for better understanding.

Toolkit vs Driver Matrix

Compilation Targets and Fat Binaries

Single-Architecture Compilation Is a Trap

When you compile CUDA code, you can target multiple architectures using -gencode.

If you compile for only one architecture:

• The binary contains only that GPU’s machine code.
• It will fail on GPUs without matching architecture support.

Correct practice:

• Include multiple -gencode targets.
• Include PTX for forward compatibility.

Without this, your binary becomes tightly coupled to one GPU class.

That’s why code runs locally but fails in cloud deployment.

The issue isn’t CUDA. It’s compilation strategy.

Deprecated or Unsupported Features

CUDA evolves. APIs get deprecated. Some eventually disappear.

Examples include:

• Legacy texture APIs
• Certain synchronization primitives
• Older memory management methods

Older GPUs may still support these features, but newer toolkits remove them. Or newer GPUs optimize away legacy pathways.

Code that hasn’t been maintained may fail when:

• Recompiled with modern toolchains
• Deployed on new architectures

CUDA backward compatibility is strong  but not indefinite.

Memory Model and Resource Limits

Hardware Resource Variations Matter

Each GPU model has limits:

• Shared memory per SM
• Registers per thread
• Maximum threads per block
• L2 cache size

A kernel that uses heavy shared memory may run fine on a high-end data center GPU but fail on a smaller consumer GPU.

Failure message:
“too many resources requested for launch”

The code is correct. The hardware simply cannot accommodate it.

This is especially common when:

• Scaling down from A100-class GPUs
• Running on smaller RTX-class GPUs
• Using cloud GPUs with constrained partitions

CUDA doesn’t abstract resource limits. You must design within them.

Tensor Cores and Specialized Hardware

Tensor Cores are not universal.

They differ by generation:

• FP16 support
• BF16 support
• TF32 support
• FP8 (Hopper)

If your kernel or framework assumes Tensor Core acceleration and deploys to a GPU without compatible hardware, performance drops or runtime errors may occur.

Similarly:

• Mixed precision assumptions may fail.
• Certain math intrinsics may not exist.

Hardware specialization improves performance but reduces universal portability.

Precision and Instruction-Level Differences

Not all GPUs support:

• High-performance FP64
• Specific atomic operations
• Cooperative groups extensions

Compute-heavy scientific code relying on FP64 throughput may run technically but perform drastically differently on consumer GPUs.

Instruction-level changes can also affect:

• Numerical stability
• Reduction behavior
• Synchronization correctness

Subtle differences across architectures can produce incorrect outputs without crashing.

Silent failure is worse than a hard failure.

Runtime vs Compile-Time Failures

Compile-time failures:

• Missing compute capability
• Unsupported intrinsics
• Deprecated API usage

Runtime failures:

• Kernel launch errors
• Illegal memory access
• Driver incompatibility

Worst case:

• Code runs
• Produces incorrect output
• No crash

Cross-GPU debugging is difficult because behavior may differ by hardware class.

Testing on one GPU is insufficient validation.

The table below summarizes common CUDA failure modes and when they are most likely to occur.

Consumer vs Data Center GPUs

Differences include:

• ECC memory availability
• FP64 performance
• Reliability guarantees
• Feature gating

Consumer GPUs may lack:

• Full double-precision throughput
• Certain virtualization capabilities

Cloud GPUs often run data center-class hardware, which behaves differently from local development GPUs.

Assumptions built on consumer cards often break at scale.

Multi-GPU and Virtualized Environments

In modern deployments, GPUs often run inside:

• Containers
• VMs
• MIG partitions
• Pass-through environments

Virtualization layers introduce constraints:

• Memory partitioning
• Limited SM exposure
• Driver abstraction layers

Code that works on bare metal may fail in containerized environments due to mismatched driver libraries.

MIG (Multi-Instance GPU) changes available resources. Kernels designed for full-GPU memory may fail inside partitions.

Environment matters as much as hardware.

Diagnosing CUDA Compatibility Issues

Start with fundamentals:

1. Check GPU model and compute capability.
2. Run nvidia-smi to verify driver version.
3. Run nvcc –version.
4. Confirm compilation targets.
5. Review kernel resource usage.
6. Read error messages carefully.

CUDA errors are often explicit. The mistake is ignoring them.

Compatibility debugging is systematic, not mysterious.

Best Practices to Avoid CUDA Failures

1. Compile for multiple architectures using proper -gencode flags.
2. Include PTX for forward compatibility.
3. Avoid hard-coded resource assumptions.
4. Query device properties at runtime.
5. Test across at least two GPU classes early.
6. Keep drivers and toolkits aligned intentionally.
7. Avoid deprecated APIs.

Portability requires discipline. It doesn’t happen automatically.

Cloud GPU Considerations

Cloud environments amplify compatibility issues.

Why?

• Rapid hardware switching
• Mixed GPU classes
• Managed driver stacks
• Virtualization layers

Before scaling workloads:

• Verify compute capability compatibility.
• Confirm driver versions.
• Test kernels on the target GPU class.
• Understand hardware constraints of MIG or shared environments.

Scaling first and debugging later is expensive.

Validate first. Scale second.

Conclusion

CUDA code fails across GPUs for concrete reasons:

• Compute capability mismatch
• Compilation targeting errors
• Driver-toolkit misalignment
• Resource constraints
• Architectural differences
• Hardware specialization assumptions

CUDA is powerful and portable  within defined boundaries.

If you treat GPU hardware as interchangeable, you’ll encounter failures. If you understand architectural differences, resource constraints, and compilation strategy, you gain control.

The teams that scale CUDA successfully don’t rely on the myth of “write once, run anywhere.”

They design with hardware awareness, test across architectures early, and treat compatibility as part of the engineering process  not an afterthought.