Context Without Limits: Breaking the LLM Inference Bottleneck

As enterprises rapidly shift from simple question-and-answer chatbots to complex, autonomous agentic workflows and multi-turn assistants, artificial intelligence infrastructure is hitting a severe economic and physical wall. Modern language models now routinely handle context windows spanning hundreds of thousands of tokens. At this scale, a critical infrastructure bottleneck emerges: the rapid growth of the Key-Value (KV) cache data structure which holds the model's intermediate attention state, is completely outpacing GPU memory capacity.

When high-bandwidth memory (HBM) on a GPU is exhausted, the system is forced to discard the cached context and recompute it from scratch on subsequent requests. This redundant computation consumes expensive GPU cycles, introduces massive response latency, and drastically drives up the total cost of ownership. Adding more expensive GPUs is a costly fix. Instead, the solution requires a tiered architecture that allows KV cache to be persistently stored, shared, and globally reused across AI clusters.

The Tiered KV Cache Storage Approach

KV cache design options require balancing access speed, capacity, and cost across a range of storage tiers.  NVIDIA defines a G1 to G4 hierarchical implementation options, where “G” stands for Generation. Within this hierarchy, G1 represents high-bandwidth memory (HBM) directly on the GPU; G2 uses host system DRAM; G3 utilizes host based local NVMe storage; G3.5 is NVIDIA’s Inference Context Memory Storage appliance (CMX) for KV Cache; and G4 uses network-attached, shared storage like IBM Storage Scale Erasure Coding Edition (ECE).

The trade-offs across these tiers center on KV Cache retrieval speed, capacity, and data isolation. G1 provides the ultra-low, microsecond latency necessary for KV Cache retrieval, but it has high cost and capacity constraints. Offloading KV Cache down to G2 or G3 expands storage capacity but local memory and NVME drives still face high cost and capacity constraints. Because G1 through G3.5 architectures are restricted to a single hardware node, they offer no High Availability (HA) protection.  Any hardware component failure will cause expensive and time-consuming GPU re-computation of the whole KV Cache.

These tradeoffs are why a G4 architecture powered by IBM Storage Scale ECE represents the ideal KV Cache design. An IBM Storage Scale KV Cache implementation introduces critical advantages in HA and capacity scalability. IBM Storage Scale ECE distributes its content with robust erasure coding.  This approach provides a persistent, fault-tolerant layer to protect against any server or component failure. Furthermore, organizations can scale seamlessly from terabytes to petabytes of KV Cache by adding drives in real time to IBM Storage Scale without disrupting the KV Cache operations.  A G4 based approach easily allows every GPU across the wider enterprise fabric to share and read large, cached context histories simultaneously. 

The Architectural Stack: Slicing the Bottleneck

The true value of a G4-based IBM Storage Scale architecture lies in its ability to deliver enterprise resilience while maintaining high-performance KV Cache throughput for AI factories and large-scale inference deployments. To validate this premise, IBM, Supermicro, and NVIDIA jointly conducted a series of benchmarks on a reference design (see Figure 1) featuring NVIDIA Dynamo, IBM Storage Scale ECE on Supermicro servers, and NVIDIA Spectrum-X networking:

·       NVIDIA Dynamo: A distributed inference-serving framework built specifically to manage KV caching. It provides cache-aware routing to map cache states across thousands of GPUs, disaggregated serving to decouple compute-heavy prefill operations from memory-bound decode processes, and automated KV cache offloading via low-latency software libraries.

·       IBM Storage Scale Erasure Coding Edition (ECE): An enterprise software-defined parallel file system that provides a high-capacity, fault-tolerant persistence layer at the G4 shared storage tier. It ensures massive KV cache datasets remain globally accessible across the entire GPU cluster.

·       Supermicro Petascale Storage Servers: High-density storage nodes optimized with a symmetrically balanced I/O design utilizing AMD EPYC processors. Each node dedicates 64 PCIe Gen5 lanes to 16 hot-swap E3.S NVMe drives and another 64 lanes for network connectivity, eliminating hardware-level I/O bottlenecks.

·       NVIDIA Spectrum-X Ethernet: An end-to-end, high-bandwidth fabric featuring integrated adaptive routing, programmable congestion control, and robust Quality of Service (QoS) mechanisms designed specifically to eliminate multi-tenant network contention.  We implemented our test environment with NVIDIA Spectrum-X network using a spin-leaf network configuration to mirror a real-world deployment scenario.

Figure 1 – IBM Storage Scale ECE KV Cache Benchmark architecture

Benchmark Results: Sub-Second TTFT at 130k Tokens

The core objective of the joint benchmark was to stress-test a large 120-billion parameter model (Hugging Face GPT-OSS-120B, pre-quantized to mxfp4) executing on a cost-effective single-node GPU configuration featuring four NVIDIA RTX Pro 6000 GPUs. By forcing the framework to manage millions of tokens exclusively through the external G4 shared storage tier, the testing evaluated the real-world viability of IBM Storage Scale ECE as a high-performance KV Cache persistence layer.

In single request Time-To-First-Token (TTFT) testing (see Figure 2), the performance differences are very noticeable. Without a KV cache layer, the GPU recompute baseline degrades exponentially as the context window expands.  TTFT increased dramatically from 0.572 seconds for a 10k token prompt to 32.14 seconds at 130k tokens. In contrast, when IBM Storage Scale ECE on Supermicro storage servers is used as a KV Cache, the TTFT curve remains remarkably flat.  This design maintains sub-second latencies across all prompt sizes, delivering a 56x speedup over raw GPU re-computation at the maximum 130k token context window.

Figure 2 - Cached requests are up to 56x faster on IBM Storage Scale ECE

In another concurrent load test (200 total requests with 28 requests being concurrent, with each request processing 100 unique long prompts), the throughput increased from 0.19 Requests Per Second (RPS) using basic GPU recompute versus 4.26 RPS when IBM Storage Scale was used as a KV Cache.  This represents a 22x efficiency gain.  From a latency perspective, total time to process all 200 requests went from 1,048 seconds with GPU recompute down to just 46.94 seconds with IBM Storage Scale, a reduction of over 95%. Together, these results demonstrate that IBM Storage Scale enables significantly higher concurrency KV Cache queries, lower end-to-end latency, and far more efficient utilization of expensive GPU resources across high-volume production workloads.

Enterprise Resilience Under Stress and Future Performance Headroom

In multi-tenant environments, inference response times are frequently degraded by "noisy-neighbor" scenarios where multiple enterprise applications vie for the same network resources. To evaluate the resilience of this G4 KV Cache architecture, a stress test was conducted by introducing four independent client nodes that flooded the shared storage fabric with 200 GB/s of competing network I/O traffic. Even under this extreme network load, the integrated architecture sustained a throughput of 3.6 requests per second (RPS). This represents an 18x efficiency improvement over the un-cached baseline and reflects only a minor 15.5%throughput reduction when compared with the original 4.26 RPS result.  This real-world network testing scenario verifies the production readiness of IBM Storage Scale as a KV Cache.

Unlocking Additional Performance Headroom

While the current benchmark delivered exceptional metrics, further analysis reveals that these results merely scratch the surface of the underlying hardware capability.  Our benchmark results were bound by a 2x400Gb client-side network configuration supporting up to 80 GB/s of client-facing bandwidth. However, the 8-node Supermicro Petascale storage backend running IBM Storage Scale ECE can deliver up to 315 GB/s of sustained read throughput. This means the file system layer was operating with nearly 4x more performance headroom than the installed networking fabric could ingest during the test.

By scaling out the client-side networking layer with additional 400Gb links to match the immense line-rate capabilities of the Supermicro Petascale storage servers, enterprise deployments can expect to scale request throughput linearly and drive TTFT numbers even lower. Decoupling the KV cache memory fabric via a globally accessible parallel file system transforms IBM Storage Scale into an active, resilient engine for scaling AI inference operations.

Transforming Storage into an Active Inference Engine

The joint IBM, Supermicro, and NVIDIA solution demonstrates that a shared G4-tier KV cache model should be strongly considered by enterprises running AI inference and agentic workloads at scale. By eliminating costly GPU re-computation, this reference architecture, fully optimized using NVIDIA Dynamo, NVIDIA Spectrum-X Ethernet, IBM Storage Scale ECE, and Supermicro Petascale servers can unlock massive efficiency gains:

·       56x reduction in Time-to-First-Token (TTFT) for large prompt context lengths.

·       22x improvement in request throughput under concurrent production workloads.

·       18x throughput advantage sustained even under intense 'noisy-neighbor' network contentions.

The true value of this solution extends beyond the benchmarks. The IBM, Supermicro, and NVIDIA products used for this testing, have been fully integrated, tested, and tuned together in a real-world environment. For enterprises looking to maximize their GPU infrastructure investment, this architecture provides a clear, deployable path to a high throughput, low latency, and cost-efficient KV Cache platform.