Improving Inference Speed with Llama.cpp

Are you a user of Llama.cpp, the popular language model? If so, you might be interested in optimizing its performance and improving the inference speed. In this blog post, we'll explore some of the advancements and considerations when it comes to running Llama.cpp on different hardware configurations.

One of the primary concerns for users is the inference speed. It appears that for the majority of Llama.cpp users, running it on big models can result in slower performance. The table below provides some insights:

Model GPU Tokens/sec
33b RTX 3090 17.61
33b AutoGPTQ with CUDA 21
33b Exllama 40

An important question that arises is the advantage of using the proprietary API over OpenCL. While this depends on your specific use case, it's worth exploring the benefits of the proprietary API in terms of performance, compatibility, and ease of use.

One promising alternative to consider is Exllama, an open-source project aimed at improving the inference speed of Llama.cpp. According to the project's repository, Exllama can achieve around 40 tokens/sec on a 33b model, surpassing the performance of other options like AutoGPTQ with CUDA.

When it comes to hardware considerations, it's important to note the impact of VRAM requirements. Some modifications or pull requests to Llama.cpp may increase the VRAM requirement, which can affect the compatibility of certain GPUs. For example, the pull request mentioned in the repository increases the VRAM requirement from 12.6 GB to 14.2 GB for the q6_k model.

As technology evolves, there might be challenges along the way. For instance, the latest Nvidia drivers have introduced design choices that slow down the inference process. While this may not be a bug, it's something to keep in mind when considering the performance of Llama.cpp on different systems.

For users who find the command-line interface (CLI) of Llama.cpp inconvenient, there might be a solution. It's possible to explore using the textgen_webui to provide a more user-friendly interface for generating text with the Llama.cpp version.

These are just some of the considerations and observations surrounding Llama.cpp and its performance. As you explore and experiment with different hardware configurations and alternatives like Exllama, you can optimize the usage of this powerful language model and achieve faster inference speeds.

Tags: Llama.cpp, inference speed, RTX 3090, AutoGPTQ, Exllama, proprietary

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