08/18/2026
GPU memory pressure is a real infrastructure bottleneck — and it's getting worse as context windows grow. 📈
As long-context LLM inference scales, KV cache demand scales with it. When the cache working set outgrows GPU memory, serving stacks either degrade or teams are forced to scale compute nodes just to get more local SSD headroom. That's an expensive and inflexible answer.
We built the SupremeRAID™ KV Cache for Rack to offer a better one.
The architecture is straightforward: a dedicated SupremeRAID™ NVMe storage appliance connects to GPU servers over high-speed Ethernet and exports a shared NFS cache path via LMCache. GPU servers stay focused on inference. Cache capacity scales independently — without touching the compute fleet.
We validated it with a production-grade benchmark:
🖥️ Model: Qwen3-235B on 4x NVIDIA H200 GPUs
⚙️ Stack: vLLM + LMCache + EvalScope multi-turn workload
📦 Storage: 10 × KIOXIA CM7-V NVMe SSDs in RAID 5 on Supermicro SSG-221E-DN2R24R
🔗 Network: Dual 200 Gb/s Ethernet
Total Throughput improvement vs. no KV cache offload:
→ +32.7% at 512-token prefix length
→ +47.0% at 2,048-token prefix length
→ +51.6% at 4,096-token prefix length
→ +53.4% at 8,192-token prefix length
Zero fails across all 512 requests, at every prefix length tested.
The trend is the story: the longer the reusable context prefix, the more the external cache tier contributes. And inference workloads are moving toward exactly this shape — longer contexts, higher concurrency, more multi-turn depth.
Graid Technology has qualified SupremeRAID™ KV Cache for Rack on 20 storage server platforms across AIC, Dell, Giga Computing, Lenovo, and Supermicro — giving architects validated options across a range of form factors, processor platforms, and NVMe densities.
📄 Read the full white paper — benchmark methodology, test configuration, and complete results — here: https://zurl.co/gaomR
