GPU on ErrorVault — Developer Error Code Dictionary

Fix clw-gpu-limit-exceeded: GPU Resource Quota Exceeded in OpenClaw

Fri, 07 Aug 2026 22:20:04 +0800

1. Symptoms

The clw-gpu-limit-exceeded error manifests when a workload attempts to utilize more GPU compute resources than are available within your OpenClaw account quota. This error typically surfaces during job submission, container deployment, or when scaling GPU-accelerated workloads.

Shell Output Indicators

When attempting to launch a GPU-accelerated workload, you may encounter output similar to the following:

$ clw job submit --gpus 4 --image tensorflow:latest --command "python train.py"
Error: clw-gpu-limit-exceeded
Message: Requested 4 GPUs but only 2 available in quota. Current usage: 2/2 GPUs allocated.

Quota Details:
  - Total GPU quota: 2
  - Currently in use: 2
  - Requested: 4
  - Available: 0

Suggestion: Reduce GPU request, wait for running jobs to complete, or request a quota increase via the OpenClaw console.

Additional Symptoms

Beyond the command-line output, you may observe the following indicators that suggest a GPU limit issue:

Fix clw-gpu-denied: GPU Device Access Denied

Sat, 01 Aug 2026 17:32:04 +0800

1. Symptoms

When encountering the clw-gpu-denied error, you will observe the following indicators and shell outputs that signal GPU device access has been blocked or denied by the OpenClaw runtime environment.

Primary Error Indicators

The error typically manifests as a diagnostic message printed directly to the console or logged to stderr during computation initialization. You may encounter output resembling the following pattern:

[OpenClaw] ERROR: GPU device access denied
[OpenClaw] Code: clw-gpu-denied
[OpenClaw] Device: /dev/nvidia0 or CUDA:0
[OpenClaw] Reason: Insufficient permissions or device not accessible

Additional symptoms that frequently accompany this error include job failures that terminate immediately upon reaching GPU-intensive code sections, applications that fall back to CPU-only execution despite GPU hardware being present on the system, and runtime exceptions that reference DeviceAccessDenied or similar permission-related terminology. In containerized environments, you may also notice the container failing to start or crashing during initialization of GPU-accelerated workloads.

Fix clw-gpu-failure: OpenClaw GPU Initialization and Runtime Failure

Fri, 24 Jul 2026 15:08:04 +0800

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.

Fix clw-gpu-exhausted: GPU Compute Resources Fully Allocated

Mon, 20 Jul 2026 00:44:04 +0800

1. Symptoms

The clw-gpu-exhausted error occurs when attempting to launch new GPU-accelerated compute instances or allocate additional GPU resources through the OpenClaw platform. Users encounter this error during critical deployment workflows, particularly when scaling machine learning workloads, running distributed training jobs, or provisioning inference endpoints.

Shell Output Examples:

When using the OpenClaw CLI (clw), the error manifests in several distinguishable ways depending on the operation:

$ clw compute launch --instance-type gpu-standard --count 2
Error: clw-gpu-exhausted
GPU compute resources exhausted in region us-west-2.
Current allocation: 8/8 units. Retry after 2024-01-15T18:00:00Z
Request ID: req_7f3a9c2d8e1b

$ clw training submit --config ./ml_config.yaml
[ERROR] Failed to provision GPU workers: clw-gpu-exhausted
The requested 4x NVIDIA A100 GPUs exceed available capacity (0/2 available)

$ clw inference deploy --model deeplearning-model-v3 --replicas 3
CRITICAL: clw-gpu-exhausted
Region: eu-central-1
Available GPU memory: 0 GB / 80 GB
Recommended action: Scale down existing workloads or select alternate region

Additional observable symptoms include failed autoscaling events where GPU-enabled node pools cannot scale beyond current limits, pending reservation requests that remain unsatisfied for extended periods, and dashboard indicators showing GPU utilization at 100% across all available compute nodes in the target region.

Fix clw-gpu-crash: GPU Memory Segmentation Fault in OpenClaw Compute Workloads

Fri, 05 Jun 2026 03:08:04 +0800

1. Symptoms

The clw-gpu-crash error occurs when an OpenClaw compute workload encounters a critical failure at the GPU level. This manifests as an abrupt termination of the GPU computation process, often leaving the device in an undefined state.

Observable Symptoms

The most common symptoms reported by developers include:

Sudden process termination: The OpenClaw worker process exits with a non-zero exit code immediately after launching GPU kernels.
Device becomes unresponsive: After the crash, subsequent GPU operations return CL_DEVICE_NOT_AVAILABLE or similar errors until the device is reset.
dmesg kernel errors: On Linux systems, the kernel ring buffer may contain entries indicating GPU memory access violations:

[  123.456789] NVRM: Xid (PCI:0000:01:00): GPU Crash, reason: GF100
[  123.456890] NVRM: Xid (PCI:0000:01:00): GPU memory access violation at address 0x12345678
[  123.456891] NVRM: Xid (PCI:0000:01:00):   - GPU 0000:01:00.0: GPU has fallen off the bus

Error log output: The OpenClaw runtime emits the following error message:

[ERROR] OpenClaw Worker: clw-gpu-crash detected
[ERROR]   Device: NVIDIA Tesla T4 (ID: 0)
[ERROR]   Workload: matrix_multiply_v2.clw
[ERROR]   Crash type: GPU_MEMORY_SEGFAULT
[ERROR]   Context dump saved to: /var/log/openclaw/crash_20241230_143255.dmp

Partial results: In some cases, the GPU may have completed a portion of the workload before crashing, leaving partial output in device memory.
Timeout behavior: If Watchdog timers are enabled, the system may report a kernel execution timeout before the crash is officially detected.

Secondary Symptoms

After a clw-gpu-crash, you may observe:

Fix clw-gpu-timeout: Resolving GPU Device Communication Timeouts in OpenClaw Clusters

Thu, 30 Apr 2026 05:32:04 +0800

1. Symptoms

The clw-gpu-timeout error manifests when OpenClaw fails to receive a response from a GPU device within the expected time window. This error typically surfaces during compute-intensive operations, workload scheduling, or device enumeration phases.

Primary symptoms include:

Error message clw-gpu-timeout displayed in terminal output or application logs
Jobs stuck in PENDING or SCHEDULED state indefinitely
Partial cluster initialization where some GPUs are accessible but others are not
Timeout errors occurring during CUDA kernel execution or memory transfers
Inconsistent behavior where the same workload may succeed or fail depending on cluster load

Typical error output:

Fix clw-gpu-unreachable: GPU Device Cannot Be Reached by OpenClaw Runtime

Thu, 30 Apr 2026 03:08:04 +0800

1. Symptoms

The clw-gpu-unreachable error manifests when the OpenClaw runtime establishes an initial connection to the host system but cannot communicate with or access a configured GPU device. This error typically occurs during workload initialization or when attempting to dispatch compute kernels to GPU hardware.

Typical error message:

[OpenClaw Runtime Error] clw-gpu-unreachable
Failed to establish communication channel with GPU device: NVIDIA Tesla V100 (UUID: GPU-xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx)
Target device is either offline, inaccessible, or has been removed from the compute node.
Error Code: clw-gpu-unreachable
Timestamp: 2024-01-15T10:23:45.123Z
Runtime Version: openclaw-2.4.1

Additional observable symptoms: