
JetStream
JetStream is a throughput and memory optimized engine for LLM inference on XLA devices, starting with TPUs (and GPUs in future -- PRs welcome).
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JetStream is a throughput and memory optimized engine for LLM inference on XLA devices, starting with TPUs (and GPUs in future -- PRs welcome). It is designed to provide high performance and scalability for large language models, enabling efficient inference on cloud-based TPUs. JetStream leverages XLA to optimize the execution of LLM models, resulting in faster and more efficient inference. Additionally, JetStream supports quantization techniques to further enhance performance and reduce memory consumption. By utilizing JetStream, developers can deploy and run LLM models on TPUs with ease, achieving optimal performance and cost-effectiveness.
README:
JetStream is a throughput and memory optimized engine for LLM inference on XLA devices, starting with TPUs (and GPUs in future -- PRs welcome).
Currently, there are two reference engine implementations available -- one for Jax models and another for Pytorch models.
- Git: https://github.com/google/maxtext
- README: https://github.com/google/JetStream/blob/main/docs/online-inference-with-maxtext-engine.md
- Git: https://github.com/google/jetstream-pytorch
- README: https://github.com/google/jetstream-pytorch/blob/main/README.md
- Online Inference with MaxText on v5e Cloud TPU VM [README]
- Online Inference with Pytorch on v5e Cloud TPU VM [README]
- Serve Gemma using TPUs on GKE with JetStream
- Benchmark JetStream Server
- Observability in JetStream Server
- Profiling in JetStream Server
- JetStream Standalone Local Setup
make update-and-install-deps
Use the following commands to run a server locally:
# Start a server
python -m jetstream.core.implementations.mock.server
# Test local mock server
python -m jetstream.tools.requester
# Load test local mock server
python -m jetstream.tools.load_tester
# Test JetStream core orchestrator
python -m unittest -v jetstream.tests.core.test_orchestrator
# Test JetStream core server library
python -m unittest -v jetstream.tests.core.test_server
# Test mock JetStream engine implementation
python -m unittest -v jetstream.tests.engine.test_mock_engine
# Test mock JetStream token utils
python -m unittest -v jetstream.tests.engine.test_token_utils
python -m unittest -v jetstream.tests.engine.test_utils
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JetStream is a throughput and memory optimized engine for LLM inference on XLA devices, starting with TPUs (and GPUs in future -- PRs welcome). It is designed to provide high performance and scalability for large language models, enabling efficient inference on cloud-based TPUs. JetStream leverages XLA to optimize the execution of LLM models, resulting in faster and more efficient inference. Additionally, JetStream supports quantization techniques to further enhance performance and reduce memory consumption. By utilizing JetStream, developers can deploy and run LLM models on TPUs with ease, achieving optimal performance and cost-effectiveness.

JetStream
JetStream is a throughput and memory optimized engine for Large Language Model (LLM) inference on XLA devices, specifically TPUs. It provides reference engine implementations for Jax and Pytorch models, along with documentation for online inference, serving Gemma using TPUs on GKE, benchmarking, observability, profiling, and standalone local setup. Users can easily set up a local server, run tests, and test core modules. JetStream aims to enhance the performance of LLM inference on XLA devices.

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