Unverified50% confidenceFactExact time
SHADOW-50M is a complete language model trained from scratch that has digested 45 billion tokens, actually has 44M parameters, and has a model size of 19.8 MB.
1
Sources
50%
Confidence
Long-term
Relevance
9/15/2026
First Seen
Sources
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Related Claims
UnverifiedSHADOW-50M runs at approximately 1,900 tok/s on a laptop CPU with a memory footprint of only 41 MB.67% similarUnverifiedSHADOW does not use a trained embedding layer; instead, it represents a vocabulary of 73,880 tokens using fixed 512-bit fingerprints, the frozen table is only 4.7 MB, and new tokens can be added at any time without retraining.66% similarUnverifiedA 7B-parameter model requires approximately 14 GB of VRAM for model weights alone in FP16 format, and the total VRAM requirement for training may balloon to 50–80 GB.65% similarUnverifiedQwen3.5-9B contains approximately 9 billion trainable parameters, and after 4-bit quantization, the model file size can be compressed to approximately 5–6 GB.65% similarUnverifiedA 27B-parameter Q8_0 model, together with vLLM's KV cache and runtime overhead, requires nearly 50 GB of memory space for a single inference service.63% similar
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