LLM VRAM dataset (2026-09-25)

Estimated memory for 29 open-weight language models at six quantisations and each common context length, and how each fits 13 GPUs. Architecture values are read from each model's config.json; GPU specs are the manufacturers'. Newer models pay far less for context. At 32k tokens, Qwen3 32B (2025, standard attention) spends 8.6 GB on its KV cache; its successor Qwen3.8 27B (2026, hybrid attention) spends 2.3 GB. Across all 29 models, each 1,000 tokens of context costs between 0.006 GB (Nemotron 3.5 Lightning 30B-A3B) and 0.328 GB (DeepSeek-R1 Distill Llama 70B). Active parameters set the speed, not the memory. Qwen3-Coder-Next 80B-A3B reads 3B parameters per token but keeps all 79.7B in memory: 48.9 GB at Q4_K_M with 8k context, against 22.4 GB for the dense Qwen3 32B. 24 of 29 models fit a 24 GB card at Q4_K_M with 8k context. The largest is Qwen3.6 35B-A3B at 22.4 GB, under the 22.8 GB line (95% of the card). Method, columns and live tables: https://nodegrove.io/data