Maximizing LLM Inference Speed: Proven Strategies and Best Practices

LLMs have the potential to revolutionize applications across industries. However, running these models comes with challenges — from autoregressive generation that leads to progressively increasing generation time to current GPUs not having enough VRAM for large batching. 

Fortunately, techniques for accelerating LLM inference are being developed as fast as the release of new models. Here are some strategies for LLM inference speedup, divided into two levels, that you can explore for your applications:

Algorithmic Level Optimizations

Develop more efficient models

• MQA/GQA vs MHA. Change the attention mechanisms with multi-query or group query versus multi-head attention.

• Fewer transformer layers. Make your model as accurate with fewer layers. It should practically have fewer parameters, but reaches the same accuracy. 

• QAT. Try to quantize your model in a very smarter manner; not just decimate half of its weights. This includes algorithmic or efficient model approaches at the architecture level and the correct training level.

Explore transformer alternatives

There are also nowadays more discussions on Transformer alternatives, such as state space models and convolutional language models like Mamba and Hyena. However, they are still not at the accuracy and performance of Transformers.

Runtime Level Optimizations

• KV caching. KV cache is effectively caching the keys and values, which can be considered representative vectors for every token. A token could be a single word or half of a word. On average, it is 1.3 tokens in a word. As you run these tokens through your model, you accumulate keys and values, which are sort of projections of your tokens. And you want your model to take all of these previously seen tokens into account when generating the next token. So, there are two methods to do that. You either recalculate the keys and values for each token with every forward pass, or you could just cache them.

• Custom (fused) kernels. See how functions around your GPU can be optimized, fused, and work better for different attention mechanisms or different parts of the transformer layers.

• Continuous batching. Explore how you can hot swap different batches, and not wait for the prompt of one client or user to end before returning another client which might be shorter.

• Pipeline orchestration. Oversee the whole LLM inference. Try to see how you can tokenize and detokenize at the right time, so that you don't aggregate a performance penalty. Also, find out how you can earn free CPU cycles while the GPU is working.

Now that you know a few techniques to boost LLM inference, you can explore different LLM optimization libraries and choose an approach that works best for your use case. Watch the webinar to know. ⬇️

Faster, Cost-Effective Inference with Infery-LLM

Infery is a unified inference SDK for optimization and deployment that specializes in generative AI models. Using Infery, you can apply advanced optimization techniques to speed up LLMs by up to 5x. It also includes an inference engine and an inference server add-on. What can you expect from Infery?

Reduced LLM compute cost with faster inference

• SOTA throughput at high batches (up to 5x higher)

• Low latency at small batches 

• Autotuning to find optimal kernels for your GPU

Simplified deployment

• Automated, precompiled installation and deployment 

• Run inference in 3 lines of code

• Tested containers

• Minimal-dependency client

Full control and extendability 

• Models and data will never leave your premises - use in your environment of choice

• Supports DeciLM, DeciCoder, Mistral, and all LLaMA architectures (more on the way)

Are you wondering how Infery-LLM can boost the performance of your specific generative AI applications?

Get ahead with the latest deep learning content

• Microsoft’s AI chatbot introduces a new plug-in with Suno. Copilot users can now use a tool that can compose an original song based on a text prompt (via The Verge).

• Krutrim, Ola founder’s AI startup, releases India’s first multilingual LLM. It is voice-enabled, and able to understand several languages and even a mix of languages such as Hinglish — Hindi and English (via Bloomberg).

• Midjourney version 6 is here. It is the newest version of a widely-used AI model for image generation, featuring enhanced capabilities that produce highly realistic and detailed images. Additionally, it can now generate readable text within images (via VentureBeat).

• LangChain and Ragas to evaluate RAG pipelines. A blog that focuses on creating synthetic data, analyzing RAG performance, and the impact of various retrieval methods on RAG metrics.

• Google launches VideoPoet, an LLM capable of a wide variety of video generation tasks. Its capabilities include text-to-video, image-to-video, video stylization, video inpainting and outpainting, and video-to-audio.

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