llm-x-py 0.1.5

Python library for precise, hardware-aware inference memory estimation of language models.

LLM-X

Introduction

LLM-X is a CLI-first library designed to provide highly-precise, hardware-aware metrics for large language model inference requirements. It supports a wide range of architectures via SafeTensors and Hugging Face integration.

Key features

• Memory Deficit/Surplus Analysis:
  • Calculate as a percentage memory deficits (when memory is not sufficient for inference) and memory surpluses (when memory is sufficient for inference), also considering that memory in use by the operating system and other processes.
  • Example: A +480.7% deficit alert immediately informs the user that the model is nearly 5x larger than the available GPU memory. In the absence of an available or detected GPU, the user will be informed that the deficit is not in VRAM but in RAM.
• Dynamic Overhead Awareness:
  • Through psutil and NVML (nvidia-ml-py), LLM-X determines whether the model can fit in GPU memory (VRAM) or system memory (RAM).
• Resources Required According to Quantization Levels
  • LLM-X offers a comparative matrix with the resources required for inference according to different levels of quantization and context windows.

Installation

Since it is a native Python library, it can be easily installed via PyPI using pip.

• pip install llm-x-py

It can also be installed by cloning this repository and installing it locally.

• git clone https://github.com/Sheikyon/LLM-X.git
• cd LLM-X
• pip install .

Usage

• Core Estimation (--model-src, -m)

  • Model Sources
    • Local: Absolute paths or . for the current directory.
    • Remote: Hugging Face Model IDs or full repository URLs.
  • Inference Configuration
    • Context Window (--context, -c): Set specific token limits.
    • Batch Size (--batch, -b): Defaults to 1.
    • KV Cache Settings:
      • Precision (--kv-dtype, -q): Data type for the cache (e.g., BF16).
      • Quantization (--kv-quant): Compression method for the KV cache.

• Hugging Face Token Management

  • Add (--set-new-token): Save and automatically sort new access tokens.
  • Select (--set-token): Set an existing token as the active one by ID.
  • List (--token-list): Displays a rich table with IDs, Active status, and previews.

• Token Cleanup

  • Single (--del-token): Delete a specific token by index.
  • Purge (--del-all-tokens): Remove all stored tokens from the system.

Accuracy

Using a Qwen2.5-7B (BF16) at 131,072 context as a benchmark:

Metric	hf-mem	Accelerate	LLM-X (Ours)	Status
Weights (BF16)	14.19 GiB	13.17 GiB	14.19 GiB	Consistent
KV Cache (131k)	0.00 GiB	0.00 GiB	14.00 GiB	Ignored
Engine Overhead	0.00 GiB	0.00 GiB	2.12 GiB	Ignored
Total VRAM Required	14.19 GiB	13.17 GiB	30.31 GiB	Reality
Error Rate	113.6%	130.1%	1.8%

LLM-X achieves an accuracy rate of ≈ 98.2%, closely matching the empirical metrics of torch.cuda.memory_allocated(), which tracks actual tensors allocated in memory. This also results in an error rate of only ≈ 1.8% in supported models.