Long-Context Inference Optimization Solutions for Local Quantized LLMs in GPU-Constrained Environments

This project is based on the Ollama experimental framework and explores optimization strategies for efficient long-context inference in GPU memory-constrained environments. It covers core areas such as quantization strategies, KV cache management, chunk processing, and dynamic memory allocation, providing experimental data and optimization guidance for local LLM deployers. It has significant practical value against the backdrop of rising cloud costs and strict data privacy requirements.

The long-context capability of large language models has evolved from 4K tokens to 128K or even millions of tokens, but the GPU memory demand is enormous, and memory limitations have become the biggest obstacle to local operation. Even quantized models may still exceed the capacity of consumer-grade GPUs when processing long documents.

The project focuses on four key technical challenges: 1. Memory impact of quantization strategies (trade-off between quality and memory for different precisions and changes in long-context scenarios); 2. KV cache management (compression and eviction strategies to reduce memory usage); 3. Chunk processing and sliding window (long document segmentation and cross-chunk information transfer); 4. Dynamic memory allocation (adjusting memory usage based on context length).

A systematic experimental design is adopted: first, establish benchmark tests to measure memory peaks and inference latency; then gradually introduce optimization techniques to quantify gains; finally, conduct combination experiments to find the optimal configuration. It covers models from 7B to 70B parameters and multiple quantization schemes, with test documents including technical papers, code repositories, and books to ensure universality.

1. Non-linear quantization gains: For some models, the memory savings from 8-bit to 4-bit are far greater than the quality degradation, and the difference is related to the architecture and training method; 2. KV cache critical point: After exceeding a certain context length, KV cache becomes the main bottleneck, and an adaptive strategy is proposed; 3. Context-dependent chunking strategy: The optimal chunk size and overlap depend on the document type (technical documents require large chunks to maintain code integrity, while narrative texts can use small chunks).

For consumer-grade GPU users: 4-bit quantization combined with KV cache compression can achieve usable long-context inference; For high-quality requirements: 8-bit quantization combined with intelligent chunk processing; For extreme memory constraints: sliding window attention mechanism (sacrifice long-range dependencies for reduced memory usage).

Currently, only inference-stage optimization is focused on; future work will extend to the training stage; support more local frameworks (such as llama.cpp, vLLM); explore multimodal long contexts (memory management challenges for text + image/audio).

This project provides valuable experimental data and optimization guidance for local LLM deployers, and its open-source nature facilitates community contributions of new optimization techniques. Against the backdrop of rising cloud costs and strict privacy requirements, efficiently running long-context models locally has significant practical value.