Gpu_poor

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gpu_poor calculates GPU memory and token generation speed for any LLM and GPU/CPU. It supports various quantization methods and inference frameworks to optimize performance.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is gpu_poor?

gpu_poor is a specialized tool designed to calculate the GPU memory requirements and token generation speed for any Large Language Model (LLM) across various GPU and CPU configurations. It provides detailed breakdowns of memory usage, including KV Cache, Model Size, Activation Memory, and overheads. The tool supports popular quantization methods like GGML, bitsandbytes, and QLoRA, as well as inference frameworks such as vLLM, llama.cpp, and Hugging Face. It also approximates finetuning times and helps users determine optimal context lengths and batch sizes, making it invaluable for optimizing LLM deployment and training.

Best used for

Ideal for product managers and startup founders who need to assess GPU compatibility for LLMs, estimate token generation speed, and approximate finetuning times. Especially valuable for optimizing resource allocation and understanding memory consumption across different quantization methods and inference frameworks.

Common actions

optimize LLM performance

estimate GPU requirements

analyze memory usage

plan LLM deployment

face swappingopen-sourceworkflowsdeepfakecollaborationlow-code/no-codeautomated workflowgithub copilot"AI Agents"

Capabilities

Key features

Calculate vRAM memory
Estimate token/s
Approximate finetuning time
Memory breakdown analysis
Supports multiple quantizations
Supports multiple frameworks

Target Audience

product managerstartup founder

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

How accurate are the GPU memory and token/s calculations?

The calculations are designed to be within 500MB of actual values, though results can vary based on model, input data, CUDA version, and quantization. The tool aims to provide reliable approximations for planning and optimization purposes.

What quantization methods does gpu_poor support?

gpu_poor supports several popular quantization methods, including llama.cpp/ggml, bitsandbytes (bnb), and QLoRA. This allows users to evaluate memory and performance implications across different quantization strategies.

Can gpu_poor help with finetuning optimization?

Yes, gpu_poor can approximate the time per iteration for finetuning (forward + backward pass) and identify whether the process is memory or compute-bound. This helps users decide on the best finetuning approach, such as Full, LoRA, or QLoRA.

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