Fixing local LLM OOM errors by optimizing KV cache and quantization

Running large open-source language models locally can lead to out-of-memory errors, even if the model's weights seem to fit within the available VRAM. This is primarily due to the significant memory required for the KV cache, which scales with context length, and intermediate activation memory during inference. Developers can address these issues by profiling memory usage with tools like PyTorch's memory snapshot, applying appropriate quantization techniques to model weights and the KV cache, and managing memory fragmentation. AI

IMPACT Provides practical solutions for developers running large language models locally, addressing common memory issues.

RANK_REASON The article provides a technical guide and solutions for a common problem encountered when running LLMs locally.

Read on dev.to — LLM tag →

• bitsandbytes
• KV cache
• llama.cpp
• LLM
• PyTorch
• RTX 4090
• torch.cuda.OutOfMemoryError
• transformers
• vLLM
• VRAM

• infra
• other

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