issues, chat FPS; optimisations

.scratch/distributed-inference-performance/PRD.md

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+# PRD: Distributed inference performance
+
+## Problem
+
+Distributed decode already avoids full-prompt recomputation when the local KV
+path is active, but each Activation Seam can still pay transport and data-plane
+overhead for every generated token. Relay logs show a new `request_id` per
+token; that is correct correlation, but the old relay implementation also
+opened a new WebSocket per token. Direct hops and relay bridge forwarding use
+fresh HTTP requests as well. Without timing and byte measurements, compression,
+copy, and buffering choices cannot be ranked safely.
+
+## Outcome
+
+For a cached Route Session, connection setup is amortized across the session,
+decode payloads remain one-step activations, progress reporting is bounded, and
+the benchmark can attribute latency to model execution, serialization, relay,
+HTTP, queueing, and backpressure. Optimizations must preserve output tokens,
+KV semantics, failure behavior, and compatibility with legacy one-shot peers.
+
+## Non-goals
+
+- No speculative decoding or multi-token model execution in this feature.
+- No QUIC/WebRTC/custom transport rewrite.
+- No centralized Hot KV State.
+- No silent reuse of a `request_id`; each activation remains independently
+  traceable.
+
+## Acceptance criteria
+
+- A reproducible local two-node and relay benchmark reports per-token and
+  per-seam timing plus bytes.
+- Cached decode does not perform a new TCP/WebSocket connection per token.
+- Direct and relay-to-local HTTP paths reuse connections safely or document why
+  a path cannot do so.
+- Compression and copy decisions are based on recorded traces, not guesses.
+- Slow prefill consumers apply bounded backpressure rather than unbounded body
+  buffering.
+- A benchmark regression threshold catches a meaningful transport slowdown.
+