Behind the Memory Water Level: How KV Cache Drives Inference Resource Allocation
Introduction
In inference scenarios, GPU memory is not always at full capacity; its water level is primarily driven by the dynamic allocation of KV Cache, rather than solely by model weights. The capacity planning and reuse efficiency of KV Cache directly impact core metrics such as throughput and time-to-first-token (TTFT), making it a key lever for cost optimization in compute centers. Based on measured data from Mingxin's FX100 series and public research, this article breaks down how KV Cache sustains the inference memory water level and provides reproducible optimization directions.
Why KV Cache Becomes the Dominant Variable in Memory Usage
During large model inference, memory usage consists of three parts: model weights, activations, and KV Cache. Weights remain largely constant after loading, activations fluctuate with batch size, while KV Cache grows linearly with sequence length and concurrency, making it the primary driver of dynamic memory levels. According to "Efficient Memory Management for Large Language Model Serving with PagedAttention" (arXiv:2309.06180), fragmentation and redundant storage of KV Cache are the core sources of memory waste, and paged management can keep fragmentation-induced waste at a low level. This means that the allocation strategy of KV Cache directly determines whether memory can be utilized efficiently.
In measured tests on a 480B-parameter model (MoE, weights approximately 450GB), Mingxin's FX100 achieved a 29% throughput improvement at concurrency level 8 and 40% at concurrency level 16 through KV tiered acceleration (measured, report R2/R3). This improvement did not come from adding memory capacity, but from optimizing the storage and read paths of KV Cache, making the memory water level more stable under the same load and avoiding recomputation triggered by cache overflow.
Memory Water Level and Latency: How KV Cache Hit Rate Affects TTFT
Time-to-first-token (TTFT) is a key metric for inference experience, and its value is strongly correlated with the KV Cache hit rate. When the cache misses, the model must recompute historical tokens from external storage, causing latency to spike. Mingxin's measurements show that for the 480B model under TP8 with three concurrency levels, TTFT p50 dropped from 10.17–35.73 seconds to 7.53–26.35 seconds, a reduction of 26–32% (measured, report R2). This improvement stems from FX100's parallel read optimization for KV Cache, which reduces wait time during cold reads from disk.
In comparisons without external recomputation, FX100's acceleration is even more pronounced: the recomputation baseline TTFT p50 was 149.5 seconds (concurrency 16), while FX100 required only 11.85 seconds, with throughput rising from 4.1 to 74.9 tok/s (measured, report R2). This indicates that a higher memory water level is not necessarily better; the key lies in the KV Cache hit rate and read bandwidth. According to "Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving" (arXiv:2407.00079), offloading KV Cache from GPU memory to remote pooled storage can improve prefix reuse rates, but network overhead must be balanced. Mingxin's FX100 NVMe-oF array solution is precisely designed to reduce this overhead through high-speed storage media.
Optimization Paths: From Paged Management to Tiered Storage
Several optimization approaches exist in the industry for KV Cache memory usage. Paged management (e.g., PagedAttention) reduces fragmentation through on-demand allocation but cannot solve the capacity ceiling problem. Tiered storage (e.g., keeping hot cache in memory and offloading cold cache to SSD) can expand effective capacity but requires efficient data migration mechanisms.
Mingxin's FX100 measured data demonstrates the potential of tiered storage: in LMCache parallel read patch tests, under single-card concurrency 16 with cold disk reads, TTFT dropped from 37.97 seconds to 9.30 seconds, and bandwidth improved from 0.98 to 5.23 GB/s (measured, report R1). This result shows that by optimizing the storage-side read path, the memory water level can be managed more flexibly without relying on unlimited memory expansion.
Additionally, Checkpoint saving in training scenarios benefits from similar mechanisms. In Mingxin's measurements, full-model snapshot save time for 8-card 32B LoRA training dropped from 178 seconds to 94 seconds, with sustained write bandwidth improving by 96% (measured, report R1). This suggests that KV Cache and training data storage optimization can share the same high-speed storage infrastructure, reducing total cost of ownership.
Selection Recommendations: Balancing Memory and Storage
For compute center decision-makers, memory planning should not focus solely on capacity size but should evaluate the workload characteristics of KV Cache. If long-context or high-concurrency scenarios dominate, the memory water level will rise rapidly, and tiered storage may offer better cost-effectiveness than simply increasing HBM capacity. According to "FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness" (arXiv:2205.14135), attention computation is limited by HBM bandwidth rather than compute power, meaning storage-side optimization has a non-negligible impact on inference efficiency.
Mingxin's FX100 series (PCIe 3.0/4.0/5.0, single-interface 100–400Gb, IOPS 16M–60M) offers different bandwidth tiers to suit various memory water level requirements. Its reference prices range from ¥331,200 to ¥924,000 (approximately ¥1,797–5,014/TB), allowing decision-makers to choose based on KV Cache scale and latency SLAs. For memory water level estimation tailored to specific workloads, validation can be performed in joint testing.
Conclusion
KV Cache is the core variable in the inference memory water level, and its management efficiency directly determines throughput and latency performance. Through paging and tiered storage optimization, performance can be significantly improved without adding memory. Mingxin's FX100 measured data shows that high-speed storage arrays can effectively mitigate cache overflow issues, providing a reproducible optimization path for compute centers. To assess the memory water level and storage requirements of your own workloads, please contact the Mingxin technical team for gated joint testing.
Key Q&A
Q: Why is KV Cache the dominant factor in memory usage? A: KV Cache grows linearly with sequence length and concurrency, and its allocation strategy determines the dynamic memory water level. Paged management can reduce fragmentation, but the capacity ceiling still requires tiered storage to address.
Q: What is the practical impact of optimizing KV Cache on latency? A: In Mingxin's measurements, TTFT for the 480B model decreased by 26–32%, and throughput improved 8.6–20x without external recomputation (measured, report R2). Higher hit rates directly reduce cold-read disk latency.
Q: How should memory and storage be balanced in selection? A: For high-concurrency or long-context scenarios, prioritize tiered storage rather than indefinitely increasing HBM. The FX100 series offers 100–400Gb bandwidth tiers, selectable based on SLA and workload.
References
- Efficient Memory Management for Large Language Model Serving with PagedAttention — https://arxiv.org/abs/2309.06180
- Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving — https://arxiv.org/abs/2407.00079
- FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness — https://arxiv.org/abs/2205.14135