1. Symptoms
The clw-gpu-failure error manifests when OpenClaw attempts to initialize, access, or utilize GPU resources for accelerated computation and encounters an unrecoverable state. This error typically surfaces during application startup, kernel compilation, or when submitting compute workloads to GPU devices.
Common indicators of this failure include terminal output containing the error signature followed by a descriptive sub-code:
[OpenClaw Error] clw-gpu-failure: DEVICE_INIT_FAILED
[OpenClaw Error] clw-gpu-failure: KERNEL_COMPILE_ERROR
[OpenClaw Error] clw-gpu-failure: RUNTIME_CRASH
[OpenClaw Error] clw-gpu-failure: MEMORY_ALLOCATION_FAILED
Additional symptoms may accompany the primary error message. Users frequently report incomplete GPU enumeration where the system identifies fewer devices than physically present, or zero devices reported despite dedicated graphics hardware being installed. Applications may hang indefinitely during GPU-bound operations, or crash with segmentation faults when attempting to access GPU memory addresses. Performance profiling tools may show GPU utilization remaining at zero percent despite active compute submissions, indicating that work batches are failing before reaching device execution queues.
In severe cases, the GPU itself may become unresponsive, requiring a system reboot to restore normal function. The nvidia-smi or equivalent monitoring utilities may report the GPU in a faulted or reset state.
2. Root Cause
The clw-gpu-failure error encompasses several underlying failure modes, each with distinct technical origins. Understanding these root causes is essential for applying the correct remediation strategy.
Driver Incompatibility represents the most prevalent cause. OpenClaw requires specific minimum driver versions for CUDA or OpenCL backends, and version mismatches between the installed driver, the OpenClaw runtime, and the application binaries create initialization failures. When the driver API surface differs from what OpenClaw expects, device query operations return malformed data or crash outright during context creation.
GPU Memory Exhaustion occurs when previously executed workloads fail to properly release device memory, or when a single operation requests more memory than the device possesses. The GPU memory allocator returns NULL pointers, which OpenClaw’s error handling may not gracefully manage, resulting in downstream null-pointer dereferences or memory access violations.
Kernel Compilation Failures happen when OpenClaw generates PTX or OpenCL kernel code that exceeds the GPU’s architectural capabilities or violates compiler constraints. Modern GPUs with sm_90 architecture (Hopper) or RDNA3 architectures may reject kernels written for older instruction sets, or vice versa. Syntax errors in generated kernel code, register pressure exceeding hardware limits, and shared memory oversubscription all trigger compilation failures that surface as clw-gpu-failure.
PCIe Connectivity Issues can cause intermittent GPU failures. Insufficient PCIe slot power delivery, physical connection degradation, or bandwidth constraints from shared lanes prevent reliable data transfer between host and device. The GPU may enumerate correctly but fail during memory copy operations or kernel launches due to transaction timeouts.
CUDA Context Corruption occurs when concurrent CPU threads interfere with GPU operations, or when application code improperly manages multiple GPU contexts. Context state corruption leads to undefined behavior when subsequent operations execute against the damaged state.
3. Step-by-Step Fix
Diagnosing the Specific Failure Mode
First, identify the specific sub-error by examining full error output and enabling verbose logging:
Before:
./openclaw-app --compute workload.clw
After:
OPENCLAW_LOG_LEVEL=debug ./openclaw-app --compute workload.clw 2>&1 | grep -E "(clw-gpu-failure|GPU|device)"
This reveals the specific failure code and underlying reason for the GPU error.
Fixing Driver Incompatibility
Uninstall existing GPU drivers completely before installing a compatible version:
Before:
# Check current driver version
nvidia-smi
# Output may show outdated or conflicting versions
After:
# Remove existing NVIDIA packages
sudo apt-get purge nvidia-* libnvidia-*
sudo apt-get autoremove
# Install specific compatible driver version (example for CUDA 12.x compatible)
sudo apt-get install nvidia-driver-535-server
# Reboot and verify
sudo reboot
nvidia-smi
# Verify driver version matches OpenClaw requirements
Consult the OpenClaw compatibility matrix for your specific version’s driver requirements.
Resolving Memory Allocation Failures
Add explicit memory management and reduce batch sizes:
Before:
import openclaw
context = openclaw.Context()
kernel = context.load_kernel("compute.ops")
# Unbounded allocation causing OOM
data = context.allocate(size=16_000_000_000) # 16GB on limited device
kernel.execute(data)
After:
import openclaw
context = openclaw.Context()
# Query actual device memory capacity
device_props = context.get_device_properties()
max_alloc = device_props['total_memory'] * 0.9 # Reserve 10% headroom
# Chunk large operations
chunk_size = int(max_alloc * 0.25) # Use 25% per operation
for offset in range(0, len(data), chunk_size):
chunk = data[offset:offset + chunk_size]
buffer = context.allocate(size=chunk.nbytes)
buffer.upload(chunk)
kernel.execute(buffer)
result = buffer.download()
buffer.release() # Explicit cleanup
Addressing Kernel Compilation Errors
Update kernel code to target the correct GPU architecture:
Before:
context = openclaw.Context(arch="sm_80") # Ampere only
After:
# Query available architectures
context = openclaw.Context()
target_arch = context.detect_architecture()
# Or specify compatible multi-architecture target
context = openclaw.Context(arch="sm_70,sm_80,sm_90")
Fixing PCIe Connectivity Issues
Verify PCIe configuration and power delivery:
Before:
lspci | grep -i nvidia
# May show warnings or degraded operation
After:
# Verify PCIe slot power configuration
cat /sys/bus/pci/devices/0000:01:00.0/current_link_speed
cat /sys/bus/pci/devices/0000:01:00.0/current_link_width
# Ensure sufficient power (requires root)
# Check power draw
nvidia-smi -q | grep -i power
# If using external power, ensure PCIe power limit is adequate
# Consider reseating the card in a different slot with dedicated power
4. Verification
After applying fixes, verify GPU functionality through systematic testing:
Step 1: Confirm GPU Visibility
nvidia-smi
# Should list GPU with name, driver version, and memory usage
Step 2: Test OpenClaw Device Enumeration
openclaw-cli devices
# Expected output showing available compute devices with properties
Step 3: Run Diagnostic Workload
openclaw-cli bench --device all --workload basic
# Should complete without clw-gpu-failure errors
Step 4: Execute Target Application
./openclaw-app --compute workload.clw
# Should process successfully with GPU utilization visible in nvidia-smi
If the original workload still fails, re-enable debug logging and compare the error sequence against the previously captured output to determine if the failure mode has changed.
5. Common Pitfalls
Avoid these frequent mistakes when resolving clw-gpu-failure errors:
Neglecting Driver Reboots: Installing a new GPU driver without rebooting leaves the kernel module in an inconsistent state. Always reboot after driver installation or updates, even if the installer claims a live update succeeded.
Mixing CUDA Versions: Having multiple CUDA Toolkit installations creates library conflicts where OpenClaw loads an incompatible CUDA version at runtime. Use environment variables or LD_LIBRARY_PATH explicitly to select the correct version, or remove unused installations entirely.
Ignoring Memory Overhead: GPU memory calculations must account for driver overhead, context allocation, and temporary buffers. Allocating exactly the device’s total memory always fails. Always reserve 5-10% minimum headroom.
Skipping Architecture Compatibility Checks: Deploying kernels compiled for a newer architecture on older hardware produces cryptic failures. Always compile kernels for the minimum architecture requirement of your deployment environment.
Assuming Single-User Operation: On shared systems, other users’ processes may hold GPU memory or have corrupted the context. Check for orphaned processes with fuser -v /dev/nvidia* and terminate stale references before retrying.
Overlooking Thermal Throttling: GPUs under sustained heavy load may thermal throttle, causing intermittent failures that appear as hardware faults. Monitor temperatures with nvidia-smi -l 1 during workloads and ensure adequate cooling.
6. Related Errors
clw-device-not-found: This error occurs when OpenClaw cannot enumerate any compatible GPU devices, often preceding clw-gpu-failure if a partially functioning GPU attempts to initialize. The distinction lies in enumeration failure versus initialization failure.
clw-memory-exceeded: Indicates that GPU memory allocations have exceeded device capacity, which frequently triggers clw-gpu-failure during subsequent memory operations. This error specifically signals the allocation boundary was crossed, while clw-gpu-failure represents the resulting crash state.
clw-kernel-timeout: Occurs when individual GPU operations exceed the operating system’s watchdog timeout threshold, causing the driver to terminate the kernel. This can escalate to clw-gpu-failure if the timeout handler itself encounters an error during cleanup or recovery.
These related errors share common remediation strategies around memory management, driver configuration, and workload partitioning, making them frequently encountered in combination during GPU compute troubleshooting sessions.