Recent advancements in Large Language Model (LLM) quantization and processing techniques have been reported, showing significant promise for efficiency and performance improvements, especially for the GPU-poor. The introduction of 'QuIP: 2-Bit Quantization of Large Language Models With Guarantees' demonstrates a method that allows for viable LLM quantization with just two bits per element, enhancing the capabilities of those with limited GPU resources. Another development, 'LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models', outperforms existing quantization methods by optimizing both quantization and fine-tuning processes simultaneously. Additionally, embedding quantization has been highlighted for its impact on improving retrieval speed, reducing memory usage, disk space, and costs. Google has introduced a technique that enables LLMs to process text with infinite context without increasing memory and compute requirements. This is further complemented by a method combining MRL with Binary Quantization, which drastically reduces the size of embedding vectors from 4KB to 64 bytes while retaining 90% accuracy. Lastly, LLoCO extends LLMs' capabilities for long-context processing through context compression, retrieval, and parameter-efficient finetuning, exemplified by enabling LLaMA2-7B to efficiently handle up to 128k tokens.