Is your bottleneck compute or memory movement?
Under long context, the bottleneck often shifts from matrix multiplication to moving key-value (KV) cache data. A 2026 study on commodity GPUs found that autoregressive decoding is increasingly limited by KV-cache movement rather than dense compute [1]. So your monitoring should track memory bandwidth utilization, PCIe traffic, and cache hit rates, not just GPU compute utilization.
For example, PersistentKV showed that low-active long-context decode can under-utilize commodity GPUs, meaning you might see low GPU utilization even though the system is slow [1]. If your monitoring only looks at compute, you'll miss the real problem. Watch for high memory traffic with low FLOPs—that's the signature of a memory-bound decode.
Why one-size-fits-all scheduling fails under mixed loads
Under peak load, you'll have a mix of short and long sequences, and the best kernel for one batch may be terrible for another. A 2026 study found that the best single-kernel implementation is not always the best serving schedule [1]. They showed that an adaptive policy—switching between a dense kernel for small batches and a sparse workqueue for long-context steps—improved throughput by 1.063–1.265x on bimodal, uniform, and Zipf-like workloads, and by 1.399x on a bucketed trace [1].
The lesson for monitoring: track the distribution of sequence lengths and active KV-head groups. If you see many small active batches, use a dense kernel; if you see long-context steps with few active heads, switch to a sparse schedule. The same study noted that on a boundary case, the adaptive policy avoided a regression by selecting the dense kernel [1]—so your monitoring should trigger kernel switches based on batch composition.
When KV caches spill to CPU, monitor the PCIe bottleneck
For very long contexts, KV caches may not fit in GPU memory, so you spill to CPU. This introduces a new bottleneck: retrieving fine-grained, irregular KV subsets across the GPU-CPU boundary can erase the benefits of sparsity [2]. A 2026 framework called SPIN addressed this with a locality-aware cache manager that dynamically sizes per-request HBM budgets and uses a bucketed LRU policy to cut PCIe round-trips [2].
In practice, this means you should monitor PCIe transfer rates and cache hit rates at the page level. SPIN achieved 1.66–5.66x higher end-to-end throughput and 7–9x lower time-to-first-token (TTFT) compared to vLLM, and reduced time-per-output-token (TPOT) by up to 58% [2]. If your monitoring shows high PCIe traffic, consider adjusting HBM budgets or using a more locality-aware eviction policy.
Don't ignore prefill and quantization effects on paged attention
Peak load includes the prefill phase, which is compute-intensive and can be a bottleneck for long contexts. FlashPrefill V2, a 2026 study, showed that block-sparse prefill attention can speed up prefill by up to 47.26x over FlashAttention-2 at 128K context length under FP8 precision [5]. This suggests that monitoring prefill time separately is crucial—if prefill dominates, you may need a sparse attention backend.
Quantization of KV cache can also reduce memory pressure and improve throughput. A 2025 study on vLLM found that group quantization of KV cache improved inference throughput by up to 18% under concurrent multi-request scenarios while maintaining low accuracy loss [3]. So monitor memory usage and consider quantization if you're hitting memory limits, but also watch for accuracy degradation.
About These Sources
This answer is built on 5 studies (1 peer-reviewed, 4 preprints) — published from 2025 to 2026, 5 from 2024 or later — selected as the most relevant from 6 studies that passed quality screening, drawn from 39 papers retrieved from a database of over 500 million.
Sources used in this answer
PersistentKV: Page-Aware Decode Scheduling for Long-Context LLM Serving on Commodity GPUs
PersistentKV showed that adaptive page-aware decode scheduling (switching between dense and sparse kernels) improved throughput by 1.063–1.265x on bimodal/uniform/Zipf workloads and 1.399x on a bucketed trace, compared to a single best kernel, on an RTX 3060.
Unifying Sparse Attention with Hierarchical Memory for Scalable Long-Context LLM Serving
SPIN, a sparse-attention framework with hierarchical KV storage, achieved 1.66–5.66x higher end-to-end throughput and 7–9x lower TTFT than vLLM, and reduced TPOT by up to 58%, using a locality-aware cache manager and bucketed LRU policy.
KV Cache Group Quantization Based on vLLM Inference Engine
KV Cache group quantization in vLLM improved inference throughput by up to 18% under concurrent multi-request scenarios while maintaining low accuracy loss, tested on LLaMA2-7B, LLaMA3-8B, and DeepSeek-R1-Distill-LLaMA3-8B.
ML and systems co-design for resource-efficient LLM inference serving
A thesis on ML and systems co-design for LLM serving analyzed trade-offs between accuracy and performance for optimizations like SoT prompting, prompt compression, and KV cache quantization, and implemented system components on vLLM, but full integration remains ongoing.
FlashPrefill V2: Block-Sparse Prefill Attention for Long-Context LLM Serving
FlashPrefill V2, a block-sparse prefill attention kernel, delivered up to 47.26x and 27.19x speedups over FlashAttention-2 at 128K context length under FP8 and BF16, respectively, and supports paged KV cache and continuous batching.