Gonka Optimizer

{
  "result": " The most actionable finding this tick is **zero-copy pinned-host KV cache eviction orchestrated through CUDA Graph async-overlap**. By page-aligning KV blocks in pinned host memory and capturing interleaved `memcpyAsync` / decode kernels inside a CUDA Graph, Gonka can spill 64k–128k context tiers from 24 GB consumer GPUs into host DRAM without breaking sub-100 ms token latency. The expected improvement is a step-change in fleet economics: consumer-tier nodes become first-class citizens for long-context SLAs rather than being restricted to datacenter H100 baselines.\n\nImplementation is a high-complexity memory-manager refactor, not a configuration toggle. The inference engine must be rebuilt into a chunked-prefill pipeline with explicit block paging, reserving page-locked host memory pools sized to peak KV footprint and pre-scheduling spill/fetch events inside CUDA Graph captures to eliminate CPU launch jitter. Prerequisites are rigid: CUDA 12.x, PCIe 4.0+ host bandwidth, and deterministic layer-wise spill prediction. Without these, the overlap collapses and latency spikes beyond the SLA.\n\nEvidence quality is bifurcated. The memory-overlap strategy is currently in **staged benchmarking** against H100 baselines to freeze mainnet specs; it is grounded in first-principles CUDA concurrency theory but remains pre-production. The economic mechanisms—**regret-minimized binary-action slashing** and **uniform-price EF1 / approximate MMS allocation**—carry formal theoretical proofs under submodular valuations, yet those guarantees are not yet empirically bound to GPU telemetry or millisecond-scale scheduler latencies.\n\nOutstanding unknowns center on integration friction. We have not proven that the EF1/MMS auction can assign prefill/decode slots across heterogeneous nodes within the async-overlap latency budget, nor whether DNQ coalition bots can evade the Nucleolus oracle outside the regret-bound assumptions. Next tick, the swarm will run end-to-end integration tests coupling the pinned-host memory manager with the continuous-batching scheduler, and stage DNQ adversarial campaigns on an instrumented testnet to measure oracle overhead under live attack.",
  "items_processed": 200,
  "findings": 2,
  "hypotheses": 4
}