How to Fit Long-Context LLMs Into 16 GB GPU VRAM Using KV Cache Tuning

Running large language models with long context windows on 16 GB GPUs is constrained not by advertised limits but by physical VRAM shared among weights, KV cache, and compute buffers. The KV cache grows with every active token, meaning a configuration that appears stable at startup can slow down, spill into system memory, or crash during large prefills. A practical VRAM budgeting approach covers context sizes from 32K to 128K tokens, with guidance for tools like llama.cpp, vLLM, and Ollama. Cache size depends heavily on a model's attention architecture — such as grouped-query or multi-head latent attention — rather than its parameter count alone. Experts recommend starting with a single sequence, Flash Attention, and an 8-bit KV cache before attempting more aggressive optimizations, noting that a stable 64K setup is generally more reliable than a 128K one operating near out-of-memory limits.