Topic starter
18/08/2026 2:39 pm
TL;DR: LLM inference is stateful, so round robin and sticky sessions waste GPU capacity because performance is determined by KV cache locality, prefix overlap, and cache-aware routing rather than generic request balancing, according to Equixly. That makes routing logic, cache indexing, and autoscaling part of the security and governance conversation whenever AI systems depend on shared compute and predictable access patterns.
Editorial analysis by NHI Mgmt Group, based on content published by Equixly: “How we fight GPU scarcity without compromise”.
Key questions
Q: How should teams route LLM inference when cache locality affects latency?
A: Use cache-aware routing that scores nodes by prefix overlap and current cache occupancy, then prefer the node that can reuse the most KV cache state.
Q: Why does sticky session routing still waste GPU capacity for LLMs?
A: Sticky sessions pin users to nodes, but LLM efficiency depends on whether the node holds the relevant tokenised prefix, not only the same user.
Q: What signs show that an inference fleet is routing requests poorly?
A: Look for high prefill frequency, low cache hit ratios, rising p99 latency, and nodes that are near full while other nodes remain underused.
Practitioner guidance
- Implement cache-aware routing Score candidate nodes by contiguous tokenised prefix overlap and send requests to the node that already holds the relevant KV cache whenever possible.
- Index cache state at token level Maintain a prefix index keyed on token block hashes, not raw text, so routing decisions reflect the actual cache layout on each inference node.
- Tune autoscaling to inference signals Scale the fleet using queue depth, cache hit ratio, p99 latency, and cache utilisation rather than generic CPU or memory thresholds.
Bottom line: LLM serving behaves like a stateful system because the KV cache determines whether the next request reuses work or recomputes it.
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18/08/2026 3:24 pm
Stateful inference is now a governance problem, not just a performance problem. Once routing choices determine whether compute is reused or wasted, cache locality becomes part of capacity control, service reliability, and cost governance. That is especially true when shared prompts and multi-turn workflows make the inference fleet behave like a stateful system. Practitioners should treat routing policy as an operational control with direct service impact.
A few things that frame the scale:
- AI-related credential leaks surged 81.5% year-over-year in 2025, with the surrounding AI infrastructure leaking 5x faster than core LLM providers, according to the State of Secrets Sprawl 2026.
- 92% of organisations expose NHIs to third parties, raising concerns about supply chain security, according to the Ultimate Guide to NHIs.
A question worth separating out:
Q: Should teams scale LLM infrastructure on CPU usage or inference metrics?
A: Teams should scale on inference metrics such as queue depth, cache hit ratio, latency, and cache utilisation. CPU and memory can miss the real bottleneck in LLM serving, which is often the cost of repeated prefill and cache churn. A workload-aware scaler produces better cost and service outcomes.
👉 Read our full editorial: Cache-aware LLM routing shows why inference needs stateful control