Local LLM VRAM & GPU Calculator
Avoid CUDA out-of-memory crashes and hardware buyer's remorse. Calculate exact memory footprints for model weights, attention KV cache buffers, and CUDA runtime reservations.
ollama run llama3.1:8b
./llama-server -m model.gguf -ngl 99 -c 16384 -fa --cache-type-k q8_0
Need a deep dive on GGUF quants, FlashAttention, and GPU memory bandwidth?
Read our comprehensive architectural guide covering memory bandwidth bottlenecks, how to avoid the hidden KV cache memory trap, and why a used RTX 3090 or Apple Silicon Mac Studio outperforms costlier consumer cards.
Read Full Guide: Running Local LLMs in 2026Frequently Asked Questions
Why does an LLM become unusable when it spills into system RAM?
LLM inference is almost exclusively memory-bandwidth bound. A GPU like an RTX 3090 delivers 936 GB/s across a 384-bit VRAM bus. System DDR5 RAM delivers only 50–80 GB/s over PCIe. When offloading layers to system RAM, token generation speed plummets from 80+ tokens/sec down to 3–5 tokens/sec.
What is the difference between Q4_K_M and Q8_0?
Q4_K_M uses 4.5 bits per parameter, reducing the model file size by ~70% compared to 16-bit float with negligible real-world degradation in logic. Q8_0 uses 8.5 bits per parameter, offering mathematical precision identical to original unquantized weights at the expense of needing nearly double the VRAM.
Why does context length dramatically increase VRAM usage?
The Transformer architecture must store Key and Value vectors for every prior token across every attention head and layer. For an 8B model, 8k tokens requires only ~1 GB of KV buffer, but 64k tokens requires over 17 GB of VRAM—more than the model weights themselves.