1. Introduction to LMCache TTFT is the time from when a request is issued until the model generates its first token. Because the Prefill phase has to encode the input context into a KV Cache before generation can begin, producing that first token requires a large amount of computation, which makes TTFT high.

1. Installing the Driver Download the driver Go to https://www.nvidia.com/en-us/drivers/ and select the corresponding driver version to download. 1 wget https://us.download.nvidia.com/XFree86/Linux-x86_64/580.76.05/NVIDIA-Linux-x86_64-580.76.05.run Install the driver 1 bash NVIDIA-Linux-x86_64-580.76.05.run Check the GPUs 1 nvidia-smi 1 2 3 GPU 0: NVIDIA GeForce RTX 5090 (UUID: GPU-92fcdc58-4754-73c7-af6c-56740936817d) GPU 1: NVIDIA GeForce RTX 5090 (UUID:

1. What Is Model Quantization Model quantization is the process of converting the weights and activations of a high-precision model (usually 32-bit floating point FP32 or 16-bit floating point FP16) into a low-precision model (such as 8-bit integer INT8). The value range of FP32 is -3.4*10^38 to 3.4*10^38, with 4 billion values.

The same applies to a desktop environment. 1. Prepare the node.js environment Install nvm 1 curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.40.3/install.sh | bash 1 2 echo 'export NVM_DIR="$([ -z "${XDG_CONFIG_HOME-}" ] && printf %s "${HOME}/.nvm" || printf %s "${XDG_CONFIG_HOME}/nvm")"' >> ~/.bashrc echo '[ -s "$NVM_DIR/nvm.sh" ] && \. "$NVM_DIR/nvm.sh"' >> ~/.bashrc 1 source

2025 Intrinsic Fingerprint of LLMs [Published: 07-04] View The paper proposes a robust fingerprinting method based on the inter-layer standard-deviation distribution pattern of attention parameter matrices (Q/K/V/O), used to detect lineage relationships between large language models (LLMs) — for example, whether one model was derived from another through continued training, fine-tuning, or an upgrade.