Why vLLM's KV Cache Allocation Determines Throughput, Not Just VRAM

The KV Cache allocation strategy directly impacts throughput rather than merely VRAM usage because it determines the number of concurrent sequences the inference engine can accommodate under a given VRAM budget, the batch size, and memory access efficiency—three variables that scale proportionally with throughput. VRAM usage is only the surface; the allocation strategy's influence on how the compute pipeline is scheduled is the deeper determinant of throughput.

How KV Cache Allocation Bypasses VRAM Fragmentation Limits

During LLM inference, the KV Cache size for each request grows dynamically with sequence length. Traditional pre-allocation strategies reserve contiguous VRAM based on the maximum possible length, leading to fragmentation and low utilization. According to Efficient Memory Management for Large Language Model Serving with PagedAttention, vLLM divides the KV Cache into fixed-size blocks allocated on demand, similar to the paging mechanism of virtual memory in operating systems, thereby eliminating VRAM fragmentation. This mechanism significantly improves VRAM utilization, but more critically, it changes how the batching scheduler operates.

In non-paged schemes, VRAM fragmentation forces the engine to reduce the concurrent batch size, leaving GPU compute units idle. Paged allocation allows the engine to accommodate more requests simultaneously, increasing batch size. The larger the batch, the higher the GPU utilization, the more tokens processed per unit time, and the higher the throughput. Measured data from Mingxin FX100 confirms this logic: under a 480B production deployment with long-context cold-restart load, KV tiered acceleration improved inference throughput by 29–40% (concurrency 8: +29% as lower bound; optimal operating point at concurrency 16: +40% as upper bound) [measured, reports R2/R3].

The Transmission Effect of Allocation Strategy on Memory Access Efficiency

KV Cache allocation affects not only capacity but also memory access patterns. Under paging, KV blocks may be non-contiguous in VRAM, requiring attention computation to access across blocks. According to FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness, the bottleneck in attention computation is HBM bandwidth rather than compute power, and memory access patterns directly determine compute efficiency. If paged allocation leads to numerous random small-granularity accesses, effective bandwidth degrades, offsetting the gains from increased batch size.

Measured data from Mingxin FX100 shows that after optimizing the memory access path via KV tiered acceleration, time-to-first-token (TTFT) decreased by 26–32%: under 480B·TP8 with three concurrency levels, TTFT p50 dropped from 10.17–35.73s to 7.53–26.35s [measured, report R2]. The TTFT improvement directly reflects enhanced memory access efficiency—faster KV reads mean shorter queue times, allowing the engine to release batch slots sooner and accept new requests, creating a positive feedback loop.

Allocation Strategy and Throughput Amplification in Long-Context Scenarios

In long-context scenarios, KV Cache size far exceeds that of short conversations. According to SGLang: Efficient Execution of Structured Language Model Programs, RadixAttention reuses KV blocks of shared prefixes via a prefix tree, reducing redundant computation and storage. This reuse strategy is essentially an allocation optimization: it lets multiple requests share the same KV data, lowering VRAM usage while using the freed memory to accommodate more concurrent requests.

In comparative tests without external recomputation, Mingxin FX100 achieved a speedup of 8.6–20×: the recomputation baseline TTFT p50 was 149.5s (concurrency 16), while FX100 was only 11.85s; throughput increased from 4.1 tok/s to 74.9 tok/s [measured, report R2]. This magnitude of difference far exceeds what pure VRAM capacity differences can explain. The core lies in the allocation strategy enabling the engine to avoid KV recomputation, dedicating all compute resources to incremental generation.

Engineering Implementation and Selection Considerations for Allocation Strategy

For technical decision-makers, evaluating an inference engine should not stop at VRAM capacity figures. The granularity of the allocation strategy matters: whether paging is supported, whether prefix reuse is supported, and whether KV tiered offloading is supported. According to Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving, a KVCache-centric disaggregated architecture pools KV to remote storage, further breaking single-machine VRAM boundaries. The throughput ceiling of such architectures depends on the coordination efficiency between KV transfer bandwidth and allocation scheduling.

Mingxin's FX product line targets this bottleneck: FX100 offers 100Gb per interface and 16M IOPS (vendor specification), which supported the throughput improvements measured above. For teams planning to deploy long-context or high-concurrency inference services, it is recommended to require vendors to provide in-band measured data similar to R2/R3 during selection, rather than relying solely on specification sheets.

References

1. Efficient Memory Management for Large Language Model Serving with PagedAttention — https://arxiv.org/abs/2309.06180
2. SGLang: Efficient Execution of Structured Language Model Programs — https://arxiv.org/abs/2312.07104
3. Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving — https://arxiv.org/abs/2407.00079
4. FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness — https://arxiv.org/abs/2205.14135

Key Points Q&A

Q: Why does the KV Cache allocation strategy affect throughput rather than just VRAM? A: The allocation strategy determines the number of concurrent sequences and batch size, both of which scale directly with throughput. Paging and reuse mechanisms reduce fragmentation and improve utilization, enabling the engine to process more requests under the same VRAM.

Q: What are the measured improvements of Mingxin FX100 in KV Cache optimization? A: Throughput improved by 29–40% (concurrency 8: +29%, concurrency 16: +40%), and TTFT decreased by 26–32% [measured, reports R2/R3]. In scenarios without external recomputation, speedup reached 8.6–20× [measured, report R2].

Q: Which KV Cache allocation capabilities should be evaluated during selection? A: Check whether paged management, prefix reuse, and KV tiered offloading are supported. Require vendors to provide in-band measured data from long-context, high-concurrency scenarios, rather than relying only on VRAM capacity specifications.