KV cache and context pressure
Separate vLLM cache budgeting, context length, concurrency and activation peaks instead of treating every startup failure as the same OOM.
For inference and model-serving teams
Inference platform reliability for GPU model serving
Inference incidents cross model, request, kernel, worker and fleet boundaries. A final HTTP 500 says almost nothing about whether the initiating event was KV cache pressure, a bad adapter, a dead EngineCore, a container limit or a failing GPU.
Denpex preserves the first worker failure, correlates it with device and host evidence, and gives the operator one controlled test. That creates a faster path from noisy serving symptoms to the component that owns the fix.
Who this is for
- Inference platform engineers
- Model-serving SREs
- AI platform engineers
- GPU cluster engineers
- Performance engineers
- Production ML engineers
The failure surface
These incidents look adjacent in the final alert, but they require different owners and different proof before recovery.
Worker and engine deaths
Recover the first worker traceback or host kill event that appeared before EngineDeadError and repeated API failures.
Backend and kernel compatibility
Compare attention backend, dtype, quantization, request shape and GPU architecture with one known-good control.
Distributed serving topology
Diagnose tensor-parallel shared-memory, Ray placement and NCCL transport failures across replicas and nodes.
The operating loop
- 1
Capture the first request and worker failure
Preserve the request shape and the earliest distinct traceback before restarts erase the causal state.
- 2
Classify the boundary
Assign the incident to capacity, configuration, request, kernel, process, network or hardware with supporting evidence.
- 3
Run one canary
Change one control such as context, concurrency, backend or node while holding the model and request constant.
- 4
Return traffic after verification
Require the initiating signature to disappear under a production-shaped request and concurrency window.
What Denpex contributes
- Published vLLM, Triton, TensorRT-LLM, CUDA and NCCL failure coverage
- Cross-process and cross-rank causal ordering for distributed serving
- GPU telemetry and host evidence alongside application traces
- Incident metrics that can be exported to existing Prometheus and operations workflows
Measure this in an evaluation
Use your incidents and timestamps. These are measurement definitions, not Denpex customer-outcome claims.
| Metric | Measurement |
|---|---|
| Mean time to causal component | Time from alert to a verified owner such as request, model config, engine, kernel, host or GPU. |
| Repeat incident rate | Serving incidents that return with the same initiating signature after a claimed fix. |
| Capacity recovered | Replica or GPU service time restored after a verified correction, measured from real traffic and scheduler timestamps. |
| Escalation completeness | Vendor or framework issues opened with a minimal reproduction, versions, device data and first failure already attached. |
Start with the technical evidence
vLLM error reference
Decode KV cache, EngineCore, shared-memory, LoRA and paged-attention failures.
CUDA error reference
Find the application, runtime or hardware boundary behind common CUDA messages.
NCCL error reference
Trace tensor-parallel and multi-node serving failures back to the first missing participant.
EngineCore died runbook
Recover the first worker or host error hidden by repeated EngineDeadError responses.
Frequently asked questions
Does Denpex support vLLM production incidents?
The deterministic corpus includes vLLM memory, KV cache, worker, EngineCore, shared-memory, LoRA, placement and kernel signatures. The system still requires the full worker and host evidence to distinguish adjacent causes rather than converting every vLLM 500 into one generic diagnosis.
Can Denpex diagnose silent inference throughput collapse?
It can recognize covered signals such as repeated vLLM preemption and recompute, correlate those events with configuration and telemetry, and give a controlled admission test. It should be evaluated against the serving team's own latency and throughput objectives.
What should we retain from an inference outage?
Retain the failing request shape without sensitive content, model and adapter identifiers, engine launch arguments, worker logs, versions, GPU and driver identity, host kernel events, scheduler state, and a healthy replica comparison.
Will Denpex restart replicas automatically?
Monitoring, diagnosis, dry runs and operator-run steps are available in the standard path. Automatic remote action remains a controlled-pilot capability that requires qualified receivers, scoped authorization, progress readback and rollback.
Prove it on incidents your team already resolved
Freeze the expected owner and outcome, replay the evidence, then compare operator time and decision quality with your current process.
Build the evaluation plan