Is 16GB of VRAM Enough for Local AI?

You can now run a 27-billion-parameter model on a 16 GB graphics card, and in one demo it hit more than 45 tokens per second. But a short demo and a long coding session are very different jobs. In this video: - what a 16 GB card costs next to paying for an API, and how long it takes to earn the money back - how GSQ-RCO compression fits Qwen 3.8 27B into 11.8 GB with almost no loss on benchmarks - why the context window and the KV cache eat memory fast in coding agents - two settings that more than double the context you can fit - prefill: the wait before the model writes anything, and why it matters more than tokens per second - Nvidia vs AMD at the same price, and when two GPUs make sense Numbers come from public price comparisons and community tests cited in the video. Prices and speeds change, so check current numbers before you buy. ## Chapters 0:00 A 27B model on a 16 GB card 0:31 The money: GPU vs API 1:41 Why run models locally 1:55 Fitting the model: GSQ-RCO 2:32 Context windows and coding agents 3:12 The KV cache grows with the chat 3:57 Getting more context from the same card 4:27 Prefill: the wait before the answer 5:20 Multi-token prediction 5:42 RTX 5060 Ti vs RX 9070 XT 6:19 Two GPUs 6:48 How I'd decide