Unverified50% confidenceFactExact time
GGUF BF16 is about 20% smaller than safetensors BF16, because llama.cpp serializes and packs tensors more compactly, while safetensors preserves the training framework's original tensor layout and JSON header.
1
Sources
50%
Confidence
Long-term
Relevance
10/14/2026
First Seen
Sources
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Related Claims
VerifiedCompared to FP16, BF16 has the same 8-bit exponent width as FP32 and therefore a larger dynamic range, making it the mainstream precision choice for current large model training.70% similarUnverifiedBF16 uses 1 sign bit, 8 exponent bits, and 7 mantissa bits, sacrificing precision while retaining the same dynamic range as FP32.63% similarUnverifiedThe self-developed W4 kernel's perplexity (probe ppl) is 15.76, better than bitsandbytes NF4's 18.98.62% similarUnverifiedIn a needle-in-a-haystack experiment with a 128K Token context and about 16,000 key-value pairs, changing the position of just one key-value pair produced a maximum accuracy gap of 40.2% for DeepSeek-V4-Flash-Base, 34.8% for DeepSeek-V4-Pro-Base, and still 19.1% and 14.8% respectively after post-training optimization.61% similarUnverifiedIn the same test, the bf16 baseline exceeded the self-developed W4 kernel (25.5 tok/s) and bitsandbytes NF4 (25.7 tok/s) at 31.3 tok/s, reaching 29.8% of the theoretical roofline.61% similar
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