Run LLM batch inference jobs cheaper than competitors. See why Anyscale is the choice for LLM batch inference.
We know how important throughput is for your large scale, offline batch inference jobs—and we’ve optimized Anyscale accordingly.
The Anyscale inference team consists of many of the leading committers to the vLLM project. Our experts can help tune engine performance to reduce costs.
Our custom optimizations for prefix caching enable significant performance improvements on long-context use cases compared to vLLM.
Anyscale makes it easy to leverage heterogeneous compute. Use CPUs and GPUs in the same pipeline to increase utilization, fully saturate GPUs, and decrease costs.
Automated Throughput Tuner | N/A N/A | - - | - - | |
Support for Different GPUs/Accelerators | N/A N/A | - - | ||
Support for Large Model Parallelism | - - | |||
Spot Instance Support | N/A N/A | |||
Aceclerated Long Context Inference | - - | - - | - - | |
Custom Optimized Kernels | N/A N/A | - - | - - | |
Multi-Modal Support | - - |
We’ve optimized our inference engine so you don’t have to.
Anyscale supports tensor parallelism, data parallelism, and pipeline parallelism so you can use any GPU and any model for your workload—including more available cost efficient options like A10 and L4 accelerators and models like Llama-3.1-8B or Llama-3.1-405B.
Jumpstart your development process with custom-made templates, only available on Anyscale.
Run LLM offline inference on large scale input data with Ray Data
Execute end-to-end LLM workflows to develop and productionize LLMs at scale
At Anyscale, we know how important it is to stay competitive within the AI space. That’s why we’re constantly updating and iterating on our product to make sure it’s the fastest, cheapest, and most performant option for AI/ML workloads. When it comes to offline batch inference, we’ve invested in a number of advanced capabilities to enhance your inference process, including:
See why Anyscale is the best option for offline batch inference.