Stop Wasting VRAM: The Setting Most Ollama Users Never Change

Описание: Your model file says 4.9 GB, so why is your GPU full? In this video, I break down what really fills your VRAM when you run AI models locally with Ollama, llama.cpp, or vLLM: the model weights, the KV cache, and the hidden overhead nobody talks about.

If you run local LLMs on an RTX 3060, 4090, 5060 Ti, or any 8 GB to 24 GB card, this will save you hours of out-of-memory errors.

Chapters
0:00 The 5 GB model that uses 16 GB
0:59 Model weights: the part everyone knows
1:27 What is the KV cache?
2:05 KV cache math for Llama 3.1 8B
2:51 Grouped query attention
3:07 Does the formula hold on a real GPU?
3:57 Overhead: the hidden gig
4:30 Why vLLM fills your whole GPU
5:09 Model or budget?
5:24 Ollama’s fine print: silent prompt truncation
6:02 Quantizing the KV cache (q8_0 and q4_0)
6:42 DeepSeek’s multi-head latent attention
7:03 TurboQuant: 3-bit KV cache
7:33 The TurboQuant catch
8:04 Whose numbers are these?
8:23 Verdict and the VRAM budget formula



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