nvidia-peermem kernel rejected peer-direct DMA mapping BAR1 aperture exhausted

nvidia-peermem could not create another peer-direct mapping because the GPU BAR1 aperture has no usable space for it. Stale peer mappings, an undersized BAR1 window, or an unsupported platform layout can produce the same rejection. This entry explains how to confirm the cause, apply the fix, and separate it from adjacent infiniband failures.

Quick answer

nvidia-peermem kernel rejected peer-direct DMA mapping BAR1 aperture exhausted means nvidia-peermem could not create another peer-direct mapping because the GPU BAR1 aperture has no usable space for it. Stale peer mappings, an undersized BAR1 window, or an unsupported platform layout can produce the same rejection. Preserve the first preceding error, then run the targeted control below.

Symptom: nvidia-peermem kernel rejected peer-direct DMA mapping BAR1 aperture exhausted
Root cause: nvidia-peermem could not create another peer-direct mapping because the GPU BAR1 aperture has no usable space for it. Stale peer mappings, an undersized BAR1 window, or an unsupported platform layout can produce the same rejection. The decisive evidence is the first log line that precedes "nvidia-peermem kernel rejected peer-direct DMA mapping BAR1 aperture exhausted" and differs from a healthy run.
Recommended fix: stop the affected job, confirm no stale RDMA or CUDA processes retain the GPU, and inspect BAR1 usage with nvidia-smi -q before retrying.
How Denpex helps: Denpex investigates nvidia-peermem kernel rejected peer-direct DMA mapping BAR1 aperture exhausted 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.

Communication#infiniband#nvidia#peermem#bar1#aperture#exhausted

What this failure is

The literal signature is "nvidia-peermem kernel rejected peer-direct DMA mapping BAR1 aperture exhausted". It is a communication failure associated with InfiniBand, RoCE, and RDMA queue pairs. 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 nvidia-peermem kernel rejected peer-direct DMA mapping BAR1 aperture exhausted. 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)

nvidia-peermem could not create another peer-direct mapping because the GPU BAR1 aperture has no usable space for it. Stale peer mappings, an undersized BAR1 window, or an unsupported platform layout can produce the same rejection. 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 "nvidia-peermem kernel rejected peer-direct DMA mapping BAR1 aperture exhausted".
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
nvidia-peermem kernel rejected peer-direct DMA mapping BAR1 aperture exhausted	nvidia-peermem could not create another peer-direct mapping because the GPU BAR1 aperture has no usable space for it. Stale peer mappings, an undersized BAR1 window, or an unsupported platform layout can produce the same rejection.
The same operation fails at a consistent stage of InfiniBand, RoCE, and RDMA queue pairs.	The decisive evidence is the first log line that precedes "nvidia-peermem kernel rejected peer-direct DMA mapping BAR1 aperture exhausted" 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.	nvidia-peermem could not create another peer-direct mapping because the GPU BAR1 aperture has no usable space for it. Stale peer mappings, an undersized BAR1 window, or an unsupported platform layout can produce the same rejection.

Which systems are affected

InfiniBand, RoCE, and RDMA queue pairs
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 "nvidia-peermem kernel rejected peer-direct DMA mapping BAR1 aperture exhausted" and preserve at least 100 lines before it.
✓Identify which rank, node, device, or process emitted the first related warning.
✓compare the configured BAR1 size and current usage on a failing node with a healthy peer. Confirm Resizable BAR and Above 4G Decoding are enabled where the platform requires them.
✓Repeat the same input after the targeted change and require the signature to disappear.
✓Resume from job start after a clean peer-memory mapping succeeds only after the control passes.

Root cause

nvidia-peermem could not create another peer-direct mapping because the GPU BAR1 aperture has no usable space for it. Stale peer mappings, an undersized BAR1 window, or an unsupported platform layout can produce the same rejection.
The decisive evidence is the first log line that precedes "nvidia-peermem kernel rejected peer-direct DMA mapping BAR1 aperture exhausted" 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

nvidia-peermem kernel rejected peer-direct DMA mapping BAR1 aperture exhausted

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

stop the affected job, confirm no stale RDMA or CUDA processes retain the GPU, and inspect BAR1 usage with nvidia-smi -q before retrying. This changes the condition in the causal diagnosis instead of hiding the outer exception. The repeated control proves ownership before recovery from job start after a clean peer-memory mapping succeeds.

Code examples

snippet

# Preserve evidence before restarting
NCCL_DEBUG=INFO NCCL_DEBUG_SUBSYS=INIT,NET,COLL torchrun train.py
ibstat
ethtool -S <interface>

# Find the exact signature in the complete log
rg -n -F -- "nvidia-peermem kernel rejected peer-direct DMA mapping BAR1 aperture exhausted" <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 "nvidia-peermem kernel rejected peer-direct DMA mapping BAR1 aperture exhausted" 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 job start after a clean peer-memory mapping succeeds	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	stop the affected job, confirm no stale RDMA or CUDA processes retain the GPU, and inspect BAR1 usage with nvidia-smi -q before retrying.	Retrying the unchanged workload
Exit criterion	"nvidia-peermem kernel rejected peer-direct DMA mapping BAR1 aperture exhausted" is absent in the repeated control	The job happened to run once

Diagnostic note

“Treat "nvidia-peermem kernel rejected peer-direct DMA mapping BAR1 aperture exhausted" 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 nvidia-peermem kernel rejected peer-direct DMA mapping BAR1 aperture exhausted

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 job start after a clean peer-memory mapping succeeds.

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

Related failures to investigate next

Frequently asked questions

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

What does "nvidia-peermem kernel rejected peer-direct DMA mapping BAR1 aperture exhausted" 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 configured BAR1 size and current usage on a failing node with a healthy peer. Confirm Resizable BAR and Above 4G Decoding are enabled where the platform requires them.

How do I prevent it from recurring?

validate BAR1 capacity and a peer-memory transfer during node admission, and drain nodes whose mappings are not released after jobs exit.

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 Communication errors

NCCL CUDA Failure

Communication · critical

NCCL P2P Disabled

Communication · medium

NCCL Hang Detection

Communication · high

NCCL Wrong Rank Configuration

Communication · critical