Is 16GB All You Need for Serious Local LLM?

16GB of VRAM now runs Qwen 3.8 27B at 45 tokens a second on an RTX 5060 Ti, and the model fitting is only half the story. The viral "16GB is enough" demo was real: ISTA-DASLab's GSQ-RCO build of Qwen 3.8 27B is 11.8 GB and ties the full model on AIME, coding and GPQA. But the same video gave the 16GB card 64,000 tokens of context while the RTX 5090 next to it got 262,000. On a 16GB card, the KV cache (the conversation) competes with the model for memory: 64 KB per token on Qwen 3.8 27B, so 128K tokens is another 8 GB. Agent tools like Claude Code spend 30,000 tokens on their system prompt before you type a word. This video does the 16GB math in tokens, not gigabytes. Two settings (vision file off, 8-bit cache) take the same card from about 47,000 to 110,000 tokens of room. It explains why the same model on "16GB" is reported anywhere from 3 to 70 tokens a second, what prefill costs you before the first word, which 16GB card to buy for bandwidth (RX 9060 XT, RX 9070, RX 9070 XT, RTX 5060 Ti 16GB), why a 16GB Mac isn't a 16GB GPU, and when a 16GB card pays for itself against renting the same model. Prices are new, US, checked October 7th 2026, and they move weekly. Speeds are self-reported by the owners linked below. WHAT THIS VIDEO COVERS - Why Qwen 3.8 27B at Q4_K_M (17.44 GB) doesn't fit 16GB, and how GSQ-RCO gets it to 11.8 GB - Weights vs the KV cache: what actually fills a 16GB card during a coding session - 47K vs 110K tokens on the same card, and the two settings that make the difference - Why one model runs 3 to 70 tok/s: fit vs spill, memory bandwidth, multi-token prediction - Prefill: 41 seconds on an RTX 5060 Ti vs over 3 minutes on an RX 9060 XT for a 30K prompt - RTX 5060 Ti 16GB at $789 (84% over MSRP) vs RX 9070 XT at $790 with 43% more bandwidth - 16GB Mac (12.7 GB for the GPU by default) and the laptop GPU names that hide 8GB or 12GB - Own vs rent: ~97 days of nonstop use to break even against hosted Qwen 3.8 27B - Who should buy a second 16GB card instead (layer split vs tensor split) CHAPTERS 0:00 The viral 16GB video 1:48 Why 16GB finally works: GSQ-RCO 3:46 The number nobody noticed: 64K vs 262K 4:46 Weights vs the KV cache 6:02 What fits in 16GB, counted in tokens 8:01 Same model, 3 to 70 tokens a second 10:40 Prefill: the wait before the first word 11:18 Same price, a different 16GB 12:53 16GB labels that hold less (Mac, laptops) 13:51 Own vs rent: the break-even math 15:14 Who 16GB is wrong for 16:22 How to spend 16GB SOURCES Original video (Manolo Remiddi): https://www.youtube.com/watch?v=qILTuXLxfBM GSQ-RCO model card (ISTA-DASLab): https://huggingface.co/ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-GGUF Qwen 3.8 27B GGUF sizes: https://huggingface.co/bartowski/Qwen3.8-27B-GGUF Micro Center 5070 Ti vs 5090: https://www.youtube.com/watch?v=SzS-3Rxi4QE Luke's Dev Lab, Qwen 3.8 27B on 16GB: https://www.youtube.com/watch?v=1EzVVj7DFPc Bart Slodyczka, 16GB Mac mini: https://www.youtube.com/watch?v=PDxKrp-dTDA 5060 Ti 16GB at 64K, speed at 55K context: https://www.reddit.com/comments/1vupiyh 1M tokens of agentic coding on a 5060 Ti: https://www.reddit.com/comments/1vqrt86 Context vs speed on a 5060 Ti: https://www.reddit.com/comments/1wmav4r RX 9060 XT 16GB prefill: https://www.reddit.com/comments/1welwcx Two 5060 Ti, layer vs tensor split: https://www.reddit.com/comments/1wqhbqt llama.cpp gpt-oss benchmarks: https://github.com/ggml-org/llama.cpp/discussions/15396 GPU prices: https://gpuprix.com/us/gpus/geforce-rtx-5060-ti-16gb NVIDIA specs: https://www.nvidia.com/en-us/geforce/graphics-cards/compare/ 16GB Mac GPU memory on macOS 26: https://github.com/ollama/ollama/pull/14604 OpenRouter Qwen 3.8 27B (weighted average): https://openrouter.ai/qwen/qwen3.8-27b US electricity price (EIA): https://www.eia.gov/electricity/monthly/epm_table_grapher.php?t=epmt_5_6_a #LocalAI #LocalLLM #Qwen