failed to load model TensorRT engine built with CUDA cannot run on host CUDA runtime

A TensorRT engine (a .plan/.engine file) was built against a different CUDA/TensorRT version than the one the server is running. TensorRT engines are not portable across versions, they are compiled artifacts tied to the exact runtime that produced them. This entry explains how to confirm the cause, apply the fix, and separate it from adjacent inference failures.

Quick answer

failed to load model TensorRT engine built with CUDA cannot run on host CUDA runtime means A TensorRT engine (a .plan/.engine file) was built against a different CUDA/TensorRT version than the one the server is running. TensorRT engines are not portable across versions, they are compiled artifacts tied to the exact runtime that produced them. Preserve the first preceding error, then run the targeted control below.

Symptom: failed to load model TensorRT engine built with CUDA cannot run on host CUDA runtime
Root cause: A TensorRT engine (a .plan/.engine file) was built against a different CUDA/TensorRT version than the one the server is running.
Recommended fix: rebuild the engine on the machine that will serve it, using the same container image. Do not attempt to load a plan built elsewhere.
How Denpex helps: Denpex investigates failed to load model TensorRT engine built with CUDA cannot run on host CUDA runtime using the evidence you provide or your connected workload collects. Earlier rank, host or application evidence is needed to distinguish an initiating failure from a downstream report.

Infrastructure#inference#triton#load#model#cuda#runtime

What this failure is

The literal signature is "failed to load model TensorRT engine built with CUDA cannot run on host CUDA runtime". It is a infrastructure failure associated with vLLM, Triton, and TensorRT-LLM servers. The line identifies the failing operation or subsystem, while the surrounding evidence decides whether it is the initiating fault or a downstream symptom.

Live diagnosis, no signup

Is this what broke your run? Paste your log.

You're reading about failed to load model TensorRT engine built with CUDA cannot run on host CUDA runtime. Paste your own traceback and relevant evidence for an investigation of your workload, with a next action or a specific missing fact. A reference entry does not establish your cause. No account or card for the free diagnosis. Review data handling before submitting sensitive logs.

Before uploading, review cloud data handling and local options.

training_logs.txt

No log to hand? Try one:

3 free diagnoses/day

Why it happens (the mechanism)

A TensorRT engine (a .plan/.engine file) was built against a different CUDA/TensorRT version than the one the server is running. TensorRT engines are not portable across versions, they are compiled artifacts tied to the exact runtime that produced them. The failure becomes visible at this call site because the operation first requires the missing resource, valid state, healthy peer, or correct result. Earlier log lines and a known-good control carry more causal value than the final wrapper exception.

What you'll observe

The workload stops or loses forward progress after emitting "failed to load model TensorRT engine built with CUDA cannot run on host CUDA runtime".
A retry on the same configuration reproduces the failure because the causal state has not changed.
The outer framework exception can hide the rank, node, allocation, or dependency that failed first.
Increasing timeouts or reducing workload size can suppress the symptom without correcting the cause.

Common symptoms and what they mean

Symptom	Why it happens
failed to load model TensorRT engine built with CUDA cannot run on host CUDA runtime	A TensorRT engine (a .plan/.engine file) was built against a different CUDA/TensorRT version than the one the server is running. TensorRT engines are not portable across versions, they are compiled artifacts tied to the exact runtime that produced them.
The same operation fails at a consistent stage of vLLM, Triton, and TensorRT-LLM servers.	The decisive evidence is the first log line that precedes "failed to load model TensorRT engine built with CUDA cannot run on host CUDA runtime" and differs from a healthy run.
The first related warning appears before the final exception and names the causal subsystem.	A nearby failure remains a competing hypothesis until a control separates configuration, capacity, transport, and hardware causes.
A known-good control changes one variable and either reproduces or clears the failure.	A TensorRT engine (a .plan/.engine file) was built against a different CUDA/TensorRT version than the one the server is running. TensorRT engines are not portable across versions, they are compiled artifacts tied to the exact runtime that produced them.

Which systems are affected

vLLM, Triton, and TensorRT-LLM servers
production-shaped multi-accelerator workloads
containerized and bare-metal deployments of the same stack

How to confirm this is the problem

Use this checklist to test the hypothesis against a small reproduction. No single line proves the root cause, so preserve the preceding events and compare one variable at a time.

✓Find the first occurrence of "failed to load model TensorRT engine built with CUDA cannot run on host CUDA runtime" and preserve at least 100 lines before it.
✓Identify which rank, node, device, or process emitted the first related warning.
✓compare the build and serve environments, the TensorRT version, the CUDA runtime, and the GPU compute capability must all match. An engine built on an A100 (sm_80) will not load on an H100 (sm_90) either, regardless of CUDA version.
✓Repeat the same input after the targeted change and require the signature to disappear.
✓Resume from server start, after the engine is rebuilt only after the control passes.

Root cause

A TensorRT engine (a .plan/.engine file) was built against a different CUDA/TensorRT version than the one the server is running. TensorRT engines are not portable across versions, they are compiled artifacts tied to the exact runtime that produced them.
The decisive evidence is the first log line that precedes "failed to load model TensorRT engine built with CUDA cannot run on host CUDA runtime" and differs from a healthy run.
A nearby failure remains a competing hypothesis until a control separates configuration, capacity, transport, and hardware causes.

The fix and how to prevent it

Searchable error signature

search key

failed to load model TensorRT engine built with CUDA cannot run on host CUDA runtime

Use this text as a lookup key in logs and upstream issue trackers. It is not presented as a captured customer log. Confirm the cause from your own preceding events, versions, configuration and the cited references.

The fix and the prevention pattern

The root cause is on this page and stays free. A free account adds the exact remediation steps, saved history, and the fix on every entry in the encyclopedia.

No credit card. Daily allowance follows verified trust tier. Instant access.

Why the recommended fix works

rebuild the engine on the machine that will serve it, using the same container image. Do not attempt to load a plan built elsewhere. This changes the condition in the causal diagnosis instead of hiding the outer exception. The repeated control proves ownership before recovery from server start, after the engine is rebuilt.

Code examples

snippet

# Preserve evidence before restarting
rg -n -i 'error|exception|timeout|failed' <log-file>
nvidia-smi
python -m torch.utils.collect_env

# Find the exact signature in the complete log
rg -n -F -- "failed to load model TensorRT engine built with CUDA cannot run on host CUDA runtime" <log-file>

Adapt the snippet to your framework. The same pattern holds for PyTorch Lightning, Hugging Face Trainer, DeepSpeed, Megatron-LM, and vLLM training wrappers. Where the wrapper exposes a config flag (for examplelr_scheduler_type in Trainer), prefer the flag over the imperative API to keep the schedule declarative and reproducible.

Best practices by model family

Model / Stack	Recommendation	Notes
First response	Preserve the first failure	Keep the context before "failed to load model TensorRT engine built with CUDA cannot run on host CUDA runtime" so aggregation does not erase causality.
Confirmation	Change one variable	Use a known-good node, rank, input, or configuration as the control.
Recovery	Resume from server start, after the engine is rebuilt	Resume only after the literal signature no longer appears in the same control.

With the fix vs without the fix

Dimension	With the fix	Without the fix
Evidence	First preceding error and one controlled comparison	Only the final aggregated exception
Fix	rebuild the engine on the machine that will serve it, using the same container image. Do not attempt to load a plan built elsewhere.	Retrying the unchanged workload
Exit criterion	"failed to load model TensorRT engine built with CUDA cannot run on host CUDA runtime" is absent in the repeated control	The job happened to run once

Diagnostic note

“Treat "failed to load model TensorRT engine built with CUDA cannot run on host CUDA runtime" as a search key and an investigation checkpoint, not as proof of every cause associated with the phrase. The high-value evidence is what changed immediately before it and whether the failure follows the workload, node, or configuration.”

Visual fingerprint

Decision path for failed to load model TensorRT engine built with CUDA cannot run on host CUDA runtime

literal error captured
        |
        v
find first preceding failure
        |
        v
run one known-good control
        |
        +-- follows workload --> inspect input or configuration
        +-- follows node ------> inspect hardware or platform
        +-- disappears --------> validate the targeted fix

The control separates workload, configuration, and node ownership before recovery from server start, after the engine is rebuilt.

Diagnose this failure in VS Code

Select the traceback or open the failed terminal, then run Denpex locally to see the initiating rank, collateral failures, exact fix, and verification command without uploading the log.

Install the free VS Code extension

CUDA errors in context

CUDA reports errors asynchronously, so the traceback usually points at whatever line synchronised next rather than the one at fault. The hub covers every common CUDA error and how to make it report honestly.

Compare every cuda error side by side

Related failures to investigate next

Frequently asked questions

Questions engineers and on-call staff commonly ask about this failure.

What does "failed to load model TensorRT engine built with CUDA cannot run on host CUDA runtime" mean?

Is this line always the root cause?

No. It can be the direct failure or the point where an earlier failure becomes visible. The first preceding error and a controlled comparison decide which.

What should I collect before restarting?

Collect complete log context, the emitting rank or node, component versions, resolved configuration, and the diagnostic output shown above.

What is the fastest confirmation?

compare the build and serve environments, the TensorRT version, the CUDA runtime, and the GPU compute capability must all match. An engine built on an A100 (sm_80) will not load on an H100 (sm_90) either, regardless of CUDA version.

How do I prevent it from recurring?

build engines as part of the same image build that serves them, or key the engine cache on (trt_version, cuda_version, gpu_arch) so a mismatched artifact is never selected.

References

Don't just read the fix, diagnose your run

The encyclopedia tells you what went wrong. Denpex tells you what went wrong in YOUR training run. With your logs, your config, and your stack.

Diagnose your logs free Compare a solved incident Browse all failures

Related Infrastructure errors

Dual ISP BGP Route Withdrawal Causing Complete GPU Cloud Region Outage

Infrastructure · critical

Routine UPS Maintenance Triggering Cascading Power and Cooling Failure Across GPU Cloud Region

Infrastructure · critical

Remediation Storm Prevention via Circuit Breaker Pattern in AutoClusters

Infrastructure · high

Network Storage Volume Causing Process Hangs on H100 GPU Nodes

Infrastructure · high