DeepSpeed-FastGen
We consider these aspects of our problem:
- Latency : How fast does the system need to respond to user input?
- Task Complexity : What level of understanding is required from the LLM? Is the input context and prompt super domain-specific?
- Prompt Length : How much context needs to be provided for the LLM to do its task?
- Quality : What is the acceptable
Developing Rapidly with Generative AI
ExLlamaV2
ExLlamaV2 is an inference library for running local LLMs on modern consumer GPUs.
Overview of differences compared to V1
ExLlamaV2 is an inference library for running local LLMs on modern consumer GPUs.
Overview of differences compared to V1
- Faster, better kernels
- Cleaner and more versatile codebase
- Support for a new quant format (see below)
turboderp • GitHub - turboderp/exllamav2: A fast inference library for running LLMs locally on modern consumer-class GPUs
SGLang is a structured generation language designed for large language models (LLMs). It makes your interaction with LLMs faster and more controllable by co-designing the frontend language and the runtime system.
The core features of SGLang include:
The core features of SGLang include:
- A Flexible Front-End Language : This allows for easy programming of LLM applications with multiple ch
sgl-project • GitHub - sgl-project/sglang: SGLang is a structured generation language designed for large language models (LLMs). It makes your interaction with models faster and more controllable.
maintain the Transformers Python library, which is used for NLP tasks, includes implementations of state-of-the-art and popular models like Mistral 7B, BERT, and GPT-2, and is compatible with PyTorch, TensorFlow, and JAX.