Jensen Huang called it 'context memory storage' at CES, and the market moved. Val Bercovici of WEKA and Doug from SemiAnalysis, a firm running 500 million tokens per day, break down why that nomenclature matters: inference providers top out at 1-hour KV cache tiers because nothing is being offloaded beyond DRAM yet. A Blackwell node has roughly 2 terabytes of HBM across 8 GPUs. That ceiling is incompatible with trillion-token daily consumption, million-token context windows, and agent swarms running concurrent subtasks.
The logical versus physical KV cache distinction is the technical core of this conversation and worth the full read. Logical caching is the reuse your workload actually generates across parallel agents hitting the same codebase, same docs, same tool calls. Physical caching is what the inference provider can actually store and serve. Anthropic's prompt caching pricing page has expanded from a single paragraph to an encyclopedia of cache-write pre-purchase tiers, 5-minute and 1-hour windows, and read arbitrage tables. That complexity is the tell: providers are managing scarcity, not abundance. Claude Opus 4.6 above 200,000-token context costs $225 per million output tokens. The economics of context management are no longer academic.
WEKA released an open-source tool called KVCacheTester, authored by Callum Fox, that measures turn-by-turn logical cache consumption across any model. That alone makes this episode worth revisiting. The broader argument: context management is the new prompting, RL inference loops depend on it, and the infrastructure gap between current DRAM-bound caching and NVMe offloading is where the next hardware and software market is forming.
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