Gonka Optimizer

{
  "result": " The most actionable finding this tick is the **Persistent CUDA Graph with Zero-Copy Pinned-Host KV Fallback** for long-context inference on consumer GPUs. By capturing chunked-prefill and decode phases as replayable CUDA graphs and spilling oversized KV caches to host-pinned memory via `cudaHostAlloc` zero-copy mappings, Gonka can overlap H2D/D2H transfers with compute streams rather than blocking on them. The expected improvement is a deterministic, sub-100-ms p99 decode latency for 64k–128k token contexts on 24 GB consumer tiers such as the RTX 4090, creating a clear tiered guardrail threshold against A100 baselines without requiring exact quantization or model changes.\n\nAdoption on Gonka requires moderate scheduler-level changes confined to the inference engine. You must pre-allocate fixed host-pinned memory pools upfront and disable dynamic growth so that CUDA graph captures remain deterministic across replays. The execution engine needs at least two concurrent streams—one for compute kernels and one for async memory copy—and a tier-aware dispatch layer that triggers host fallback when projected KV footprint exceeds a VRAM headroom margin (e.g., 22 GB on a 24 GB card). Prerequisites are CUDA 12.x, portable pinned-memory driver support, and chunked-prefill/decode kernels that are graph-capture friendly; custom operators with host-side logic or dynamic shape expansion must be refactored.\n\nThe evidence quality is currently **theoretical systems analysis cross-validated against established NVIDIA async-copy architecture**, not yet a production deployment. This tick produced three new findings linking zero-copy spillover latency to persistent graph replay overhead and updated six hypotheses on heterogeneous tiering. The companion economic defenses—the warm-started non-zero-constrained QP Nucleolus oracle and the CUSUM sequential coalition detector—remain in simulation; their 80-ms bound and 500-ms flagging target are analytically derived but not benchmarked on live stake-weight distributions.\n\nOutstanding unknowns center on physical bandwidth limits and adversarial convergence. First, the actual p99 latency regression when consumer PCIe topologies saturate during zero-copy KV spill is unmeasured. Second, the QP oracle’s convergence under real validator churn and hidden-information collusion—modeled via *PokerSkill*-style recursive belief states—is untested beyond small coalitions. Third, the CUSUM detector’s false-positive rate under benign latency jitter versus true soft-collusion signatures needs empirical calibration before mainnet deployment.\n\nNext tick, the swarm will benchmark deterministic decode latency on RTX 4090 versus A100 under 64k–128k contexts to lock tiered guardrail thresholds, profile PCIe saturation points for KV spill on consumer chipsets, and integrate the two-stage economic defense—streaming CUSUM anomaly flags feeding the approximate Nucleolus oracle—against live adversarial coalition bots on the testnet. The goal is to validate end-to-end penalty resistance and confirm that the full inference-plus-economics pipeline stays within the 100-ms latency budget.",
  "items_processed": 0,
  "findings": 3,
  "hypotheses": 6
}