lyogavin/airllm: AirLLM 70B inference with single 4GB GPU
Why CEREBRO kept it
LLM inference optimization with memory constraints
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Quickstart | Configurations | MacOS | Example notebooks | FAQ AirLLM dramatically reduces inference memory usage, letting 70B large language models run on a single 4GB GPU card — without quantization, distillation, or pruning. You can even run 405B Llama 3.1 on 8GB, DeepSeek-V3 (671B) on ~12GB, and Kimi K3 (2.8T) — the largest open-source model released to date — on under 4GB, because sparse MoE models stream one expert at a time rather than a whole layer. [2026/07] Kimi K3 (2.8T) support: the largest open-source model runs on a single card in 3.72GB of VRAM, measured end to end on one RTX 600
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Appeared in 5 briefings
5 briefings re-surfaced this signal. A high number here is a deduplication weakness in the pipeline, not a popularity score.