Does Your KV Cache Offload Tier Actually Help? We Ran the Numbers.
New Graid Technology validation shows that RAID protection alone isn’t enough — the storage backend has to be fast enough to matter.
KV cache overflow is one of the most consequential performance problems in production AI inference today. We covered the architecture problem in depth in our recent blog and solution brief. But there’s a more specific question that infrastructure teams face once they’ve decided to offload KV cache: does the offload tier actually help, or does it just add complexity? The answer depends entirely on the storage backend.
Read the full white paper: Break the Memory Wall and Unlock Faster LLM Inference with SupremeRAID™ AE
The Problem With Protected-But-Slow Storage
When GPU HBM overflows and KV cache spills to local NVMe, you have two requirements that have to be met simultaneously: the tier needs RAID protection so a single drive failure doesn’t corrupt your inference sessions, and it needs to be fast enough that retrieving cached context is actually faster than recomputing it. If the storage path is too slow, RAID protection becomes irrelevant — the cache tier costs you latency instead of saving it. That’s exactly what this validation set out to test.
What We Tested
Graid Technology ran a controlled benchmark using vLLM, LMCache, and a 235B-parameter MoE model on four NVIDIA H200 GPUs under a memory-pressure workload — 100 long documents, up to 100 inflight requests, with a reuse pattern that made KV cache hit rates meaningful. Three scenarios were compared: no KV cache offload at all, local Linux MD RAID5 using default settings, and local SupremeRAID™ AE RAID5. The primary metrics were query round mean Time to First Token (TTFT) and total query round time.
The Results
The no-offload baseline measured 29.4s mean TTFT and 95.1s query round time. Linux MD RAID5 with default settings actually made things worse — 36.6s TTFT and 117.7s query round time — demonstrating that adding RAID protection to an insufficiently fast storage path doesn’t help inference; it hurts it.
SupremeRAID™ AE RAID5 told a different story entirely: 9.0s mean TTFT and 29.7s query round time — a 3.26x TTFT speedup and 3.20x query round time speedup versus no offload. Relative to Linux MD RAID5, the gap was even larger: 4.06x faster TTFT and 3.96x faster query round time.
What This Means for Your Inference Stack
The takeaway isn’t that RAID is bad. It’s that RAID protection only delivers value if the storage path preserves NVMe performance. SupremeRAID™ AE is GPU-accelerated RAID software — it protects local NVMe with RAID5 while preserving over 95% of raw NVMe performance, with no CPU bottleneck in the data path. That combination is what makes KV cache offload viable at inference speed.
If your inference stack is hitting GPU memory limits, the question isn’t whether to offload KV cache. It’s whether your offload tier is fast enough and protected enough to depend on in production.
Read the full white paper:
Break the Memory Wall and Unlock Faster LLM Inference with SupremeRAID™ AE
Learn more about SupremeRAID™ AE:
https://graidtech.com/products/supremeraid-ae
Explore the full Graid Technology KV Cache portfolio:
https://graidtech.com/ai
.jpg)