LLM VRAM Explained: KV Cache is the Hidden Culprit

Running large language models locally on GPUs can be challenging due to VRAM limitations, even when the model's file size appears to fit within available memory. The KV cache, which stores intermediate calculations for generated tokens, can consume significant VRAM, especially with longer contexts. This can lead to models being partially offloaded to the CPU, drastically slowing down inference. Monitoring GPU usage with tools like `nvidia-smi` and checking Ollama's process status can help diagnose whether the model is fully on the GPU or if CPU offloading is occurring. AI

IMPACT Understanding KV cache is crucial for optimizing local LLM inference performance and VRAM usage.

RANK_REASON The item explains a technical issue related to running LLMs locally and provides solutions, fitting the 'tool' category.

Read on dev.to — LLM tag →

• central processing unit
• Cline
• Cursor+
• GGUF
• GQA
• graphics processing unit
• Hugging Face
• KV cache
• nvidia-smi
• Ollama

• infra

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