Tag

#efficient-inference

Every item tagged efficient-inference, newest first.

2 items

No GPU left behind: Unlocking Efficiency with Co-located vLLM in TRL

The Hugging Face blog post discusses Co-located vLLM in TRL, a method for efficient deployment of very large language models. This approach enables running multiple models on a single GPU, improving resource utilization and reducing costs. By co-locating models, developers can deploy AI more efficiently, making it more accessible. The method has been shown to improve performance and reduce latency.

Key takeaways

Co-located vLLM in TRL enables running multiple models on a single GPU.
Improves resource utilization and reduces deployment costs.
Shown to improve performance and reduce latency.

HHugging Face Blog#efficient-inference #gpu-utilization #co-location

modelsMay 24

Making LLMs even more accessible with bitsandbytes, 4-bit quantization and QLoRA

Hugging Face has integrated 4-bit quantization and QLoRA into their transformers library using bitsandbytes. This reduces memory usage and speeds up inference for large language models. You can now deploy LLMs more efficiently on hardware with limited resources. The integration makes it easier for you to run LLMs on devices with restricted memory and processing power.

Key takeaways

4-bit quantization and QLoRA integrated into transformers library.
Reduces memory usage and speeds up LLM inference.
Enables more efficient deployment on resource-constrained hardware.

HHugging Face Blog#quantization #efficient-inference #transformers