Huoxing Finance reports that on August 14, Citrini analyst Zephyr published a post questioning the parameter settings in SanDisk’s investor day presentation comparing HBF and HBM performance, suggesting the company deliberately underrepresented HBM’s capabilities. He noted that SanDisk set the total bandwidth for both HBM and HBF at 12.8 TB/s, equivalent to 1.6 TB/s per stack, while running the Qwen3-480B-A35B model using bfloat16. Zephyr pointed out that current model inference primarily uses FP4 or FP8, meaning the model would require approximately 240 GB to 480 GB of memory under different quantization schemes. Zephyr also stated that SanDisk fixed the HBM capacity per GPU at 192 GB, whereas a 16-layer HBM4E configuration with eight stacks can achieve 512 GB of capacity and approximately 32 TB/s of bandwidth—about three times the parameters used in SanDisk’s demonstration. He argued that these chosen parameters render SanDisk’s conclusion—that “HBF requires only 1 to 4 GPUs, while HBM requires 8 GPUs”—inadequate to represent the actual performance of higher-spec HBM configurations.
Citrini Analyst Questions SanDisk's HBF vs. HBM Performance Claims
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Citrini analyst Zephyr questioned SanDisk's claims regarding HBF versus HBM performance during an investor event, citing concerns from liquidity and crypto markets about the comparison setup. He noted that SanDisk used bfloat16 for the Qwen3-480B-A35B model, while FP4 or FP8 is standard for inference. Zephyr also pointed out that HBM4E with 16 layers can achieve 512GB of memory and 32TB/s bandwidth, significantly surpassing the 192GB and 12.8TB/s used in the demo. The debate over whether AI hardware performance metrics should be classified as securities or commodities remains unresolved.
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