The second K3's weights drop, I'm downloading the full FP16 and storing them in mattresses
Summary
A user tweets about downloading the second K3 model's full FP16 weights and storing them, jokingly asking others to leave their doors unlocked.
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Kimi K3 weights to be released on the 27th.
Kimi K3 model weights will be released on the 27th.
@neural_avb: Lmao someone uncensored the Qwen3.6-35B-A3B weights and released on huggingface. 3M downloads last month. Apparently ne…
Someone uncensored the Qwen3.6-35B-A3B model weights and released them on Hugging Face, accumulating 3 million downloads last month. The model reportedly never refuses prompts.
Sparse By Design (5 minute read)
Moonshot's Kimi K3, a 2.8 trillion parameter open weights model with 896 experts (16 active per token), exemplifies the trend of scaling total parameters while holding active compute constant, and uses attention compression to reduce KV cache size, making frontier inference more accessible but with high storage costs.
@0xkeenz: Today I verified something I've been pondering for a long time. The official Qwen3.6 27B model has BF16 weights, but some quantized versions, like cyankiwi / Unsloth, convert some key weights to FP16 instead of keeping BF16. BF16 and FP16 have the same storage footprint, so why not just keep the original BF16 weights...?
The author verified that converting the Qwen3.6 27B model weights from BF16 to FP16 does not cause numerical overflow, and pointed out that FP16 has higher mantissa precision, explaining why quantized versions use FP16 instead of keeping BF16.
Anyone else completely tuning out these massive "open weight" drops?
A community member expresses frustration that massive open-weight models like GLM-5.2 (753B parameters) are impractical for local self-hosting due to extreme VRAM requirements, arguing the trend undermines the original spirit of the local AI community.