Story
Run Kimi K3 using 29 GB of RAM at 0.50 tok/s
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Overview
WASTE is an embeddable C inference engine that allows the Kimi K3 model, which has 2.78 trillion parameters, to run on consumer laptops by streaming activated weights directly from NVMe storage. It operates without third-party runtime dependencies and manages memory by keeping the model trunk in memory while streaming selected experts from disk.
Summary from github.com.
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Hacker News · github.com
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Based on 5 sampled comments from this thread. Analyzed 2026-08-01.
- Praise: The 3-bit quantization claim has potential, sparking interest in how this could optimize performance without a massive resource footprint.
- Praise: Some commenters are excited about using the model for long-running tasks, turning idle time on laptops into productive sessions.
- Concern: The project documentation feels like a jumble of contradictions, leaving users questioning the model's actual capabilities and precision.
- Concern: Critics are skeptical about the headline claim of achieving 0.50 tok/s with only 29GB of RAM, labeling it as nonsensical based on existing knowledge of similar models.
- Concern: There's a palpable frustration over the quality of the README, with users noting that LLM-generated documentation often lacks clarity and context for human readers.