Author

Onur Mutlu

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Preprint Aug 2026

FLINT: Efficiently Leveraging High Bandwidth Flash for Capacity-Scalable LLM Inference Acceleration

FLINT is proposed, a workload-driven HBF substrate for capacity-scalable LLM inference that integrates HBF as a memory-capacity tier alongside HBM while addressing three adoption challenges.

Geraldo F. Oliveira, Arash Tavakkol, Xiang-Yu Zhu et al. · 0 citations

Open access Jul 2026

Exploring High-Bandwidth Flash for Modern LLM Inference: Opportunities and Challenges

Analysis of HBF-based LLM-serving systems under diverse system configurations and operating scenarios shows that HBF can significantly improve the batch size, throughput, and flexibility of LLM-serving systems while reducing the minimum GPU requirements, but realizing these benefits critically depends on sustaining HBM-comparable read bandwidth and requires significant endurance improvements.

D. Son, Yonggon Park, Hyunuk Cho et al. · IEEE computer architecture l... · 2 citations