Multithreading and parallelism

CTranslate2 has 2 levels of parallelization:

  • inter_threads which is the maximum number of batches executed in parallel.
    => Increase this value to increase the throughput.

  • intra_threads which is the number of OpenMP threads that is used per batch.
    => Increase this value to decrease the latency on CPU.

The total number of computing threads launched by the process is inter_threads * intra_threads.


Even though the model data are shared between parallel replicas, increasing inter_threads will still increase the memory usage as some internal buffers are duplicated for thread safety.

On GPU, batches processed in parallel are using separate CUDA streams. Depending on the workload and GPU specifications this may or may not improve the global throughput. For better parallelism on GPU, consider using multiple GPUs as described below.

Parallel execution

Objects running models such as the Translator and Generator can be configured to process multiple batches in parallel, including on multiple GPUs:

# Create a CPU translator with 4 workers each using 1 thread:
translator = ctranslate2.Translator(model_path, device="cpu", inter_threads=4, intra_threads=1)

# Create a GPU translator with 4 workers each running on a separate GPU:
translator = ctranslate2.Translator(model_path, device="cuda", device_index=[0, 1, 2, 3])

# Create a GPU translator with 4 workers each using a different CUDA stream:
translator = ctranslate2.Translator(model_path, device="cuda", inter_threads=4)

Multiple batches should be submitted concurrently to enable this parallelization. Parallel executions are enabled in the following cases:

  • When calling methods from multiple Python threads.

  • When calling methods multiple times with asynchronous=True.

  • When calling file-based or stream-based methods.

  • When setting max_batch_size: the input will be split according to max_batch_size and each sub-batch will be executed in parallel.


Parallelization with multiple Python threads is possible because all computation methods release the Python GIL.

Asynchronous execution

Some methods can run asynchronously with asynchronous=True. In this mode, the method returns immediately and the result can be retrieved later:

async_results = []
for batch in batch_generator():
    async_results.extend(translator.translate_batch(batch, asynchronous=True))

for async_result in async_results:
    print(async_result.result())  # This method blocks until the result is available.


Instances supporting asynchronous execution have a limited queue size by default. When the queue of batches is full, the method will block even with asynchronous=True. See the parameter max_queued_batches in their constructor to configure the queue size.