This episode explores SuperInfer, a system for serving large language models on GH200-style superchips by treating memory management as the key lever for meeting latency targets rather than just maximizing compute use. It explains why KV cache growth, HBM pressure, and head-of-line blocking often hurt responsiveness first, then breaks down how the paper’s RotaSched policy proactively rotates request state out of fast memory to protect time-to-first-token deadlines. It also covers DuplexKV, the transfer mechanism that makes this practical by batching fragmented KV data, using bidirectional movement across NVLink-C2C, and overlapping transfers with model execution instead of stalling the whole system. Listeners would find it interesting because the discussion ties concrete serving pain points to a specific systems design that reportedly boosts TTFT SLO attainment by up to 74.7 percent while keeping throughput and token pacing roughly stable.

Sources:

1. SuperInfer: SLO-Aware Rotary Scheduling and Memory Management for LLM Inference on Superchips — Jiahuan Yu, Mingtao Hu, Zichao Lin, Minjia Zhang, 2026

http://arxiv.org/abs/2601.20309

2. Pie: Pooling CPU Memory for LLM Inference — Y. Xu, Z. Mao, X. Mo, S. Liu, I. Stoica, 2024

https://scholar.google.com/scholar?q=Pie%3A+Pooling+CPU+Memory+for+LLM+Inference

3. Understanding Data Movement in Tightly Coupled Heterogeneous Systems: A Case Study with the Grace Hopper Superchip — L. Fusco, M. Khalilov, M. Chrapek, G. Chukkapalli, T. Schulthess, T. Hoefler, 2024

https://scholar.google.com/scholar?q=Understanding+Data+Movement+in+Tightly+Coupled+Heterogeneous+Systems%3A+A+Case+Study+with+the+Grace+Hopper+Superchip

4. Memory Offloading for Large Language Model Inference with Latency SLO Guarantees — C. Ma, Z. Ye, H. Zhao, Z. Yang, T. Fu, J. Han, J. Zhang, Y. Luo, X. Wang, Z. Wang, et al., 2025

https://scholar.google.com/scholar?q=Memory+Offloading+for+Large+Language+Model+Inference+with+Latency+SLO+Guarantees

5. Taming Throughput-Latency Tradeoff in LLM Inference with Sarathi-Serve — A. Agrawal, N. Kedia, A. Panwar, J. Mohan, N. Kwatra, B. Gulavani, A. Tumanov, R. Ramjee, 2024

https://scholar.google.com/scholar?q=Taming+Throughput-Latency+Tradeoff+in+LLM+Inference+with+Sarathi-Serve

6. Mooncake: Trading More Storage for Less Computation - a KVCache-centric Architecture for Serving LLM Chatbot — R. Qin, Z. Li, W. He, J. Cui, F. Ren, M. Zhang, Y. Wu, W. Zheng, X. Xu, 2025

https://scholar.google.com/scholar?q=Mooncake%3A+Trading+More+Storage+for+Less+Computation+-+a+KVCache-centric+Architecture+for+Serving+LLM+Chatbot

7. TokenLake: A Unified Segment-level Prefix Cache Pool for Fine-grained Elastic Long-Context LLM Serving — Bingyang Wu et al., 2025

https://scholar.google.com/scholar?q=TokenLake%3A+A+Unified+Segment-level+Prefix+Cache+Pool+for+Fine-grained+Elastic+Long-Context+LLM+Serving

8. KVFlow: Efficient Prefix Caching for Accelerating LLM-Based Multi-Agent Workflows — Zaifeng Pan et al., 2025

https://scholar.google.com/scholar?q=KVFlow%3A+Efficient+Prefix+Caching+for+Accelerating+LLM-Based+Multi-Agent+Workflows

9. SAW-INT4: System-Aware 4-Bit KV-Cache Quantization for Real-World LLM Serving — Jinda Jia et al., 2026

https://scholar.google.com/scholar?q=SAW-INT4%3A+System-Aware+4-Bit+KV-Cache+Quantization+for+Real-World+LLM+Serving

10. KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization — Coleman Hooper et al., 2024

https://scholar.google.com/scholar?q=KVQuant%3A+Towards+10+Million+Context+Length+LLM+Inference+with+KV+Cache+Quantization

11. DistServe: Disaggregating Prefill and Decoding for Goodput-optimized Large Language Model Serving — Yinmin Zhong et al., 2024

https://scholar.google.com/scholar?q=DistServe%3A+Disaggregating+Prefill+and+Decoding+for+Goodput-optimized+Large+Language+Model+Serving

12. Prefill-Decode Aggregation or Disaggregation? Unifying Both for Goodput-Optimized LLM Serving — Chao Wang et al., 2025

https://scholar.google.com/scholar?q=Prefill-Decode+Aggregation+or+Disaggregation%3F+Unifying+Both+for+Goodput-Optimized+LLM+Serving

13. Enhancing LLM Efficiency: Targeted Pruning for Prefill-Decode Disaggregation in Inference — Hao Zhang et al., 2025

https://scholar.google.com/scholar?q=Enhancing+LLM+Efficiency%3A+Targeted+Pruning+for+Prefill-Decode+Disaggregation+in+Inference

14. AI Post Transformers: Mooncake for KV Cache-Centric LLM Serving — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-06-05-mooncake-for-kv-cache-centric-llm-servin-1086d0.mp3

15. AI Post Transformers: LAPS for Length-Aware LLM Serving — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-05-05-laps-for-length-aware-llm-serving-0c6149.mp3

16. AI Post Transformers: AI+HW 2035: Co-Designing Efficient AI Systems — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-06-24-aihw-2035-co-designing-efficient-ai-syst-95c11e.mp3

17. AI Post Transformers: FengHuang for Rack-Scale LLM Inference Memory — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-12-fenghuang-for-rack-scale-llm-inference-m-62708e.mp3

18. AI Post Transformers: KVSwap for Disk-Aware Long-Context On-Device Inference — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-16-kvswap-for-disk-aware-long-context-on-de-f3c15e.mp3

19. AI Post Transformers: Memory-Bound, Not Bandwidth-Limited Batch-1 LLM Decode — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-06-02-memory-bound-not-bandwidth-limited-batch-114799.mp3