# Troubleshooting

This document outlines some troubleshooting strategies you can consider. If you think you've discovered a bug, please [search existing issues](https://github.com/vllm-project/vllm/issues?q=is%3Aissue) first to see if it has already been reported. If not, please [file a new issue](https://github.com/vllm-project/vllm/issues/new/choose), providing as much relevant information as possible.

!!! note
    Once you've debugged a problem, remember to turn off any debugging environment variables defined, or simply start a new shell to avoid being affected by lingering debugging settings. Otherwise, the system might be slow with debugging functionalities left activated.

## Hangs downloading a model

If the model isn't already downloaded to disk, vLLM will download it from the internet which can take time and depend on your internet connection.
It's recommended to download the model first using the [huggingface-cli](https://huggingface.co/docs/huggingface_hub/en/guides/cli) and passing the local path to the model to vLLM. This way, you can isolate the issue.

## Hangs loading a model from disk

If the model is large, it can take a long time to load it from disk. Pay attention to where you store the model. Some clusters have shared filesystems across nodes, e.g. a distributed filesystem or a network filesystem, which can be slow.
It'd be better to store the model in a local disk. Additionally, have a look at the CPU memory usage, when the model is too large it might take a lot of CPU memory, slowing down the operating system because it needs to frequently swap between disk and memory.

!!! note
    To isolate the model downloading and loading issue, you can use the `--load-format dummy` argument to skip loading the model weights. This way, you can check if the model downloading and loading is the bottleneck.

## Out of memory

If the model is too large to fit in a single GPU, you will get an out-of-memory (OOM) error. Consider adopting [these options](../configuration/conserving_memory.md) to reduce the memory consumption.

## Generation quality changed

In v0.8.0, the source of default sampling parameters was changed in <https://github.com/vllm-project/vllm/pull/12622>. Prior to v0.8.0, the default sampling parameters came from vLLM's set of neutral defaults. From v0.8.0 onwards, the default sampling parameters come from the `generation_config.json` provided by the model creator.

In most cases, this should lead to higher quality responses, because the model creator is likely to know which sampling parameters are best for their model. However, in some cases the defaults provided by the model creator can lead to degraded performance.

You can check if this is happening by trying the old defaults with `--generation-config vllm` for online and `generation_config="vllm"` for offline. If, after trying this, your generation quality improves we would recommend continuing to use the vLLM defaults and petition the model creator on <https://huggingface.co> to update their default `generation_config.json` so that it produces better quality generations.

## Enable more logging

If other strategies don't solve the problem, it's likely that the vLLM instance is stuck somewhere. You can use the following environment variables to help debug the issue:

- `export VLLM_LOGGING_LEVEL=DEBUG` to turn on more logging.
- `export VLLM_LOG_STATS_INTERVAL=1.` to get log statistics more frequently for tracking running queue, waiting queue and cache hit states.
- `export CUDA_LAUNCH_BLOCKING=1` to identify which CUDA kernel is causing the problem.
- `export NCCL_DEBUG=TRACE` to turn on more logging for NCCL.
- `export VLLM_TRACE_FUNCTION=1` to record all function calls for inspection in the log files to tell which function crashes or hangs. (WARNING: This flag will slow down the token generation by **over 100x**. Do not use unless absolutely needed.)

## Breakpoints

Setting normal `pdb` breakpoints may not work in vLLM's codebase if they are executed in a subprocess. You will experience something like:

``` text
  File "/usr/local/uv/cpython-3.12.11-linux-x86_64-gnu/lib/python3.12/bdb.py", line 100, in trace_dispatch
    return self.dispatch_line(frame)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/uv/cpython-3.12.11-linux-x86_64-gnu/lib/python3.12/bdb.py", line 125, in dispatch_line
    if self.quitting: raise BdbQuit
                      ^^^^^^^^^^^^^
bdb.BdbQuit
```

One solution is using [forked-pdb](https://github.com/Lightning-AI/forked-pdb). Install with `pip install fpdb` and set a breakpoint with something like:

``` python
__import__('fpdb').ForkedPdb().set_trace()
```

Another option is to disable multiprocessing entirely, with the `VLLM_ENABLE_V1_MULTIPROCESSING` environment variable.
This keeps the scheduler in the same process, so you can use stock `pdb` breakpoints:

``` python
import os
os.environ["VLLM_ENABLE_V1_MULTIPROCESSING"] = "0"
```

## Incorrect network setup

The vLLM instance cannot get the correct IP address if you have a complicated network config. You can find a log such as `DEBUG 06-10 21:32:17 parallel_state.py:88] world_size=8 rank=0 local_rank=0 distributed_init_method=tcp://xxx.xxx.xxx.xxx:54641 backend=nccl` and the IP address should be the correct one.
If it's not, override the IP address using the environment variable `export VLLM_HOST_IP=<your_ip_address>`.

You might also need to set `export NCCL_SOCKET_IFNAME=<your_network_interface>` and `export GLOO_SOCKET_IFNAME=<your_network_interface>` to specify the network interface for the IP address.

## Error near `self.graph.replay()`

If vLLM crashes and the error trace captures it somewhere around `self.graph.replay()` in `vllm/worker/model_runner.py`, it is a CUDA error inside CUDAGraph.
To identify the particular CUDA operation that causes the error, you can add `--enforce-eager` to the command line, or `enforce_eager=True` to the [LLM][vllm.LLM] class to disable the CUDAGraph optimization and isolate the exact CUDA operation that causes the error.

## Incorrect hardware/driver

If GPU/CPU communication cannot be established, you can use the following Python script and follow the instructions below to confirm whether the GPU/CPU communication is working correctly.

??? code

    ```python
    # Test PyTorch NCCL
    import torch
    import torch.distributed as dist
    dist.init_process_group(backend="nccl")
    local_rank = dist.get_rank() % torch.cuda.device_count()
    torch.cuda.set_device(local_rank)
    data = torch.FloatTensor([1,] * 128).to("cuda")
    dist.all_reduce(data, op=dist.ReduceOp.SUM)
    torch.cuda.synchronize()
    value = data.mean().item()
    world_size = dist.get_world_size()
    assert value == world_size, f"Expected {world_size}, got {value}"

    print("PyTorch NCCL is successful!")

    # Test PyTorch GLOO
    gloo_group = dist.new_group(ranks=list(range(world_size)), backend="gloo")
    cpu_data = torch.FloatTensor([1,] * 128)
    dist.all_reduce(cpu_data, op=dist.ReduceOp.SUM, group=gloo_group)
    value = cpu_data.mean().item()
    assert value == world_size, f"Expected {world_size}, got {value}"

    print("PyTorch GLOO is successful!")

    if world_size <= 1:
        exit()

    # Test vLLM NCCL, with cuda graph
    from vllm.distributed.device_communicators.pynccl import PyNcclCommunicator

    pynccl = PyNcclCommunicator(group=gloo_group, device=local_rank)
    # pynccl is enabled by default for 0.6.5+,
    # but for 0.6.4 and below, we need to enable it manually.
    # keep the code for backward compatibility when because people
    # prefer to read the latest documentation.
    pynccl.disabled = False

    s = torch.cuda.Stream()
    with torch.cuda.stream(s):
        data.fill_(1)
        out = pynccl.all_reduce(data, stream=s)
        value = out.mean().item()
        assert value == world_size, f"Expected {world_size}, got {value}"

    print("vLLM NCCL is successful!")

    g = torch.cuda.CUDAGraph()
    with torch.cuda.graph(cuda_graph=g, stream=s):
        out = pynccl.all_reduce(data, stream=torch.cuda.current_stream())

    data.fill_(1)
    g.replay()
    torch.cuda.current_stream().synchronize()
    value = out.mean().item()
    assert value == world_size, f"Expected {world_size}, got {value}"

    print("vLLM NCCL with cuda graph is successful!")

    dist.destroy_process_group(gloo_group)
    dist.destroy_process_group()
    ```

If you are testing with a single node, adjust `--nproc-per-node` to the number of GPUs you want to use:

```bash
NCCL_DEBUG=TRACE torchrun --nproc-per-node=<number-of-GPUs> test.py
```

If you are testing with multi-nodes, adjust `--nproc-per-node` and `--nnodes` according to your setup and set `MASTER_ADDR` to the correct IP address of the master node, reachable from all nodes. Then, run:

```bash
NCCL_DEBUG=TRACE torchrun --nnodes 2 \
    --nproc-per-node=2 \
    --rdzv_backend=c10d \
    --rdzv_endpoint=$MASTER_ADDR test.py
```

If the script runs successfully, you should see the message `sanity check is successful!`.

If the test script hangs or crashes, usually it means the hardware/drivers are broken in some sense. You should try to contact your system administrator or hardware vendor for further assistance. As a common workaround, you can try to tune some NCCL environment variables, such as `export NCCL_P2P_DISABLE=1` to see if it helps. Please check [their documentation](https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/env.html) for more information. Please only use these environment variables as a temporary workaround, as they might affect the performance of the system. The best solution is still to fix the hardware/drivers so that the test script can run successfully.

!!! note
    A multi-node environment is more complicated than a single-node one. If you see errors such as `torch.distributed.DistNetworkError`, it is likely that the network/DNS setup is incorrect. In that case, you can manually assign node rank and specify the IP via command line arguments:

    - In the first node, run `NCCL_DEBUG=TRACE torchrun --nnodes 2 --nproc-per-node=2 --node-rank 0 --master_addr $MASTER_ADDR test.py`.
    - In the second node, run `NCCL_DEBUG=TRACE torchrun --nnodes 2 --nproc-per-node=2 --node-rank 1 --master_addr $MASTER_ADDR test.py`.

    Adjust `--nproc-per-node`, `--nnodes`, and `--node-rank` according to your setup, being sure to execute different commands (with different `--node-rank`) on different nodes.

## Python multiprocessing

### `RuntimeError` Exception

If you have seen a warning in your logs like this:

```console
WARNING 12-11 14:50:37 multiproc_worker_utils.py:281] CUDA was previously
    initialized. We must use the `spawn` multiprocessing start method. Setting
    VLLM_WORKER_MULTIPROC_METHOD to 'spawn'. See
    https://docs.vllm.ai/en/latest/usage/troubleshooting.html#python-multiprocessing
    for more information.
```

or an error from Python that looks like this:

??? console "Logs"

    ```console
    RuntimeError:
            An attempt has been made to start a new process before the
            current process has finished its bootstrapping phase.

            This probably means that you are not using fork to start your
            child processes and you have forgotten to use the proper idiom
            in the main module:

                if __name__ == '__main__':
                    freeze_support()
                    ...

            The "freeze_support()" line can be omitted if the program
            is not going to be frozen to produce an executable.

            To fix this issue, refer to the "Safe importing of main module"
            section in https://docs.python.org/3/library/multiprocessing.html
    ```

then you must update your Python code to guard usage of `vllm` behind a `if
__name__ == '__main__':` block. For example, instead of this:

```python
import vllm

llm = vllm.LLM(...)
```

try this instead:

```python
if __name__ == '__main__':
    import vllm

    llm = vllm.LLM(...)
```

## `torch.compile` Error

vLLM heavily depends on `torch.compile` to optimize the model for better performance, which introduces the dependency on the `torch.compile` functionality and the `triton` library. By default, we use `torch.compile` to [optimize some functions](https://github.com/vllm-project/vllm/pull/10406) in the model. Before running vLLM, you can check if `torch.compile` is working as expected by running the following script:

??? code

    ```python
    import torch

    @torch.compile
    def f(x):
        # a simple function to test torch.compile
        x = x + 1
        x = x * 2
        x = x.sin()
        return x

    x = torch.randn(4, 4).cuda()
    print(f(x))
    ```

If it raises errors from `torch/_inductor` directory, usually it means you have a custom `triton` library that is not compatible with the version of PyTorch you are using. See <https://github.com/vllm-project/vllm/issues/12219> for example.

## Model failed to be inspected

If you see an error like:

```text
  File "vllm/model_executor/models/registry.py", line xxx, in _raise_for_unsupported
    raise ValueError(
ValueError: Model architectures ['<arch>'] failed to be inspected. Please check the logs for more details.
```

It means that vLLM failed to import the model file.
Usually, it is related to missing dependencies or outdated binaries in the vLLM build.
Please read the logs carefully to determine the root cause of the error.

## Model not supported

If you see an error like:

```text
Traceback (most recent call last):
...
  File "vllm/model_executor/models/registry.py", line xxx, in inspect_model_cls
    for arch in architectures:
TypeError: 'NoneType' object is not iterable
```

or:

```text
  File "vllm/model_executor/models/registry.py", line xxx, in _raise_for_unsupported
    raise ValueError(
ValueError: Model architectures ['<arch>'] are not supported for now. Supported architectures: [...]
```

But you are sure that the model is in the [list of supported models](../models/supported_models.md), there may be some issue with vLLM's model resolution. In that case, please follow [these steps](../configuration/model_resolution.md) to explicitly specify the vLLM implementation for the model.

## Failed to infer device type

If you see an error like `RuntimeError: Failed to infer device type`, it means that vLLM failed to infer the device type of the runtime environment. You can check [the code](../../vllm/platforms/__init__.py) to see how vLLM infers the device type and why it is not working as expected. After [this PR](https://github.com/vllm-project/vllm/pull/14195), you can also set the environment variable `VLLM_LOGGING_LEVEL=DEBUG` to see more detailed logs to help debug the issue.

## NCCL error: unhandled system error during `ncclCommInitRank`

If your serving workload uses GPUDirect RDMA for distributed serving across multiple nodes and encounters an error during `ncclCommInitRank`, with no clear error message even with `NCCL_DEBUG=INFO` set, it might look like this:

```text
Error executing method 'init_device'. This might cause deadlock in distributed execution.
Traceback (most recent call last):
...
   File "/usr/local/lib/python3.12/dist-packages/vllm/distributed/device_communicators/pynccl.py", line 99, in __init__
     self.comm: ncclComm_t = self.nccl.ncclCommInitRank(
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^
   File "/usr/local/lib/python3.12/dist-packages/vllm/distributed/device_communicators/pynccl_wrapper.py", line 277, in ncclCommInitRank
     self.NCCL_CHECK(self._funcs["ncclCommInitRank"](ctypes.byref(comm),
   File "/usr/local/lib/python3.12/dist-packages/vllm/distributed/device_communicators/pynccl_wrapper.py", line 256, in NCCL_CHECK
     raise RuntimeError(f"NCCL error: {error_str}")
 RuntimeError: NCCL error: unhandled system error (run with NCCL_DEBUG=INFO for details)
...
```

This indicates vLLM failed to initialize the NCCL communicator, possibly due to a missing `IPC_LOCK` linux capability  or an unmounted `/dev/shm`. Refer to [Enabling GPUDirect RDMA](../serving/parallelism_scaling.md#enabling-gpudirect-rdma) for guidance on properly configuring the environment for GPUDirect RDMA.

## CUDA error: the provided PTX was compiled with an unsupported toolchain

If you see an error like `RuntimeError: CUDA error: the provided PTX was compiled with an unsupported toolchain.`, it means that the CUDA PTX in vLLM's wheels was compiled with a toolchain unsupported by your system. The released vLLM wheels have to be compiled with a specific version of CUDA toolkit, and the compiled code might fail to run on lower versions of CUDA drivers. Read [cuda compatibility](https://docs.nvidia.com/deploy/cuda-compatibility/) for more details. The solution is to install `cuda-compat` package from your package manager. For example, on Ubuntu, you can run `sudo apt-get install cuda-compat-12-9`, and then add `export LD_LIBRARY_PATH=/usr/local/cuda-12.9/compat:$LD_LIBRARY_PATH` to your `.bashrc` file. When successfully installed, you should see that the output of `nvidia-smi` will show `CUDA Version: 12.9`. Note that we use CUDA 12.9 as an example here, you may want to install a higher version of cuda-compat package in case vLLM's default CUDA version goes higher.

## ptxas fatal: Value 'sm_110a' is not defined for option 'gpu-name'

If you use triton kernels with cuda 13, you might see an error like `ptxas fatal: Value 'sm_110a' is not defined for option 'gpu-name'`:

```text
(EngineCore_0 pid=9492) triton.runtime.errors.PTXASError: PTXAS error: Internal Triton PTX codegen error
(EngineCore_0 pid=9492) `ptxas` stderr:
(EngineCore_0 pid=9492) ptxas fatal   : Value 'sm_110a' is not defined for option 'gpu-name'
(EngineCore_0 pid=9492) 
(EngineCore_0 pid=9492) Repro command: /home/jetson/.venv/lib/python3.12/site-packages/triton/backends/nvidia/bin/ptxas -lineinfo -v --gpu-name=sm_110a /tmp/tmp95oy_b9d.ptx -o /tmp/tmp95oy_b9d.ptx.o
(EngineCore_0 pid=9492) 
    outputs = self.engine_core.get_output()
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/jetson/.venv/lib/python3.12/site-packages/vllm/v1/engine/core_client.py", line 668, in get_output
    raise self._format_exception(outputs) from None
vllm.v1.engine.exceptions.EngineDeadError: EngineCore encountered an issue. See stack trace (above) for the root cause.
```

It means that the ptxas in triton bundle not compatible with your device. You need to set `TRITON_PTXAS_PATH` environment variable to use cuda toolkit's ptxas manually instead:

```shell
export CUDA_HOME=/usr/local/cuda
export TRITON_PTXAS_PATH="${CUDA_HOME}/bin/ptxas"
export PATH="${CUDA_HOME}/bin:$PATH"
```

## Known Issues

- In `v0.5.2`, `v0.5.3`, and `v0.5.3.post1`, there is a bug caused by [zmq](https://github.com/zeromq/pyzmq/issues/2000) , which can occasionally cause vLLM to hang depending on the machine configuration. The solution is to upgrade to the latest version of `vllm` to include the [fix](https://github.com/vllm-project/vllm/pull/6759).
- To address a memory overhead issue in older NCCL versions (see [bug](https://github.com/NVIDIA/nccl/issues/1234)), vLLM versions `>= 0.4.3, <= 0.10.1.1` would set the environment variable `NCCL_CUMEM_ENABLE=0`. External processes connecting to vLLM also needed to set this variable to prevent hangs or crashes. Since the underlying NCCL bug was fixed in NCCL 2.22.3, this override was removed in newer vLLM versions to allow for NCCL performance optimizations.
- In some PCIe machines (e.g. machines without NVLink), if you see an error like `transport/shm.cc:590 NCCL WARN Cuda failure 217 'peer access is not supported between these two devices'`, it's likely caused by a driver bug. See [this issue](https://github.com/NVIDIA/nccl/issues/1838) for more details. In that case, you can try to set `NCCL_CUMEM_HOST_ENABLE=0` to disable the feature, or upgrade your driver to the latest version.
