Compute 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-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-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: