feat: GPU context 256K→128K fleet-wide + Genesis Hermes V3 on Strix Halo

GPU Changes:
- All 3 GPUs reduced from 256K (-c 262144) to 128K (-c 131072) for stability
- Observed instability near 100K at 256K — 128K is the stable ceiling
- VRAM improved: RTX 3090 ~70% (was 90%), RTX 5070 ~65% (was 88%)
- Strix Halo swapped to LuffyTheFox/Genesis Hermes V3 APEX
  - Hermes agent fine-tune, tensor repair (3 SSM layers, 76% W1 improvement)
  - Uncensored (0/465 refusals), multimodal (mmproj F16)
  - Speed: 65 tok/s gen, 140 tok/s prompt
  - Alias strix-moe maintained

Agent Updates:
- Mumuni: max_context_window 262144→131072, already aligned on strix-moe/0.65
- Tanko: max_context_window 262144→131072
- Koonimo: max_context_window + context_length 262144→131072
- CT114 SSH access confirmed (was 'Zulip only')

LiteLLM (CT116):
- Updated backend model references qwen3.6-35B-udq4→strix-moe
- Removed stale ornith-1.0-35b from model_cost
- Fallback chains updated

Contracts Updated:
- gpu-fleet.prose.md: topology, VRAM, benchmarks, config lines, model assignments
- gpu-self-heal.prose.md: Rule 9/10 context targets
- hermes-config-template.prose.md: template values, Rules 7-9, compression thresholds
- inference-optimization.prose.md: added to repo, 128K recommendation

Compression: 0.65 fires at ~85K (~43K headroom before 128K ceiling)
For >128K workloads: route to external providers (deepseek)

inference-optimization.prose.md

@@ -0,0 +1,100 @@
+---
+name: inference-optimization
+kind: responsibility
+id: 067NC6KP02RG60S50M40E30928
+---
+
+### Goal
+
+Syslog inference response times reduced to sub-15s average by optimizing the full
+stack: LiteLLM routing weights, GPU model assignments, Hermes agent context
+management, and prompt caching — without sacrificing agent capability.
+
+### Requires
+
+- `inference-metrics`: current SpendLogs from CT116 LiteLLM Postgres — avg
+  request_duration_ms, prompt_tokens, completion_tokens, model_group breakdown,
+  cache_hit rate over the last 3 hours
+- `agent-configs`: current config.yaml from each active Hermes agent (Mumuni
+  .123, any others on .129/.122) including compression, model, context_window,
+  prompt_caching, memory settings
+- `gpu-health`: health check response from all 3 GPU backends (ornith .15:8080,
+  qwen .8:8080, gemma .110:8080)
+
+### Maintains
+
+The optimized inference stack configuration — every change is applied and
+verified end-to-end. Postcondition: avg request_duration_ms ≤ 15000 for 90% of
+non-ornith traffic; ≤ 30000 for ornith-bound agentic calls.
+
+#### liteLLM-routing
+The syslog-auto routing weights, model-specific timeouts, RPM limits, and
+model_list entries on CT116 `/opt/inference-harness/litellm_config.yaml`.
+
+#### agent-compression
+Each Hermes agent's `~/.hermes/config.yaml` compression, context_window,
+prompt_caching, and model sections.
+
+#### prompt-caching
+LiteLLM cache configuration and llama.cpp `--cache-prompt` flag on GPU hosts.
+
+#### verification
+End-to-end latency measurements after changes applied — at least 3 test
+inference calls per model path measuring ttft (time-to-first-token) and total
+duration.
+
+### Continuity
+
+- input-driven
+
+### Strategies
+
+**Context is the root cause.** Every ~46K prompt token costs ~87s of ornith
+prefill time at 532 tok/s. Fix context first, routing second.
+
+- **Route by task**: ornith for multi-step reasoning only; qwen for code/standard
+  queries; gemma for compression/auxiliary. Never send simple completion to a
+  35B MoE.
+- **Compress aggressively**: threshold at 40% (not 65%) — a 256K window should
+  compact at 102K, not 166K. Target 15% tail (not 30%).
+- **Cache everything repeated**: system prompts, skill docs, AGENTS.md — these
+  never change between turns. Single-digit cache hit rate is unacceptable.
+- **Lower context ceiling**: 128K window is the stable ceiling for agent conversations.
+  GPUs reduced from 256K to 128K (2026-07-17). For larger contexts, route to external providers.
+
+### Shape
+
+- `self`: analyze metrics, compute optimal configs, apply changes, verify
+- `delegates`:
+  - `apply-liteLLM`: update litellm_config.yaml and reload
+  - `apply-agent-config`: update hermes config.yaml per agent
+  - `verify-latency`: run test inference calls and measure response
+
+### Execution
+
+```prose
+-- Phase 1: Analyze current state (already complete)
+-- Phase 2: Apply LiteLLM routing optimization
+
+call apply-liteLLM-routing
+  config_path: /opt/inference-harness/litellm_config.yaml
+  host: 192.168.68.116
+
+-- Phase 3: Apply agent context compression optimization  
+
+call apply-agent-compression
+  agent: mumuni
+  host: 192.168.68.123
+  config_path: /root/.hermes/config.yaml
+
+-- Phase 4: Enable llama.cpp prompt caching on GPU hosts
+
+call enable-prompt-caching
+  hosts: [192.168.68.15, 192.168.68.8, 192.168.68.110]
+
+-- Phase 5: Verify end-to-end latency
+
+call verify-latency
+  host: 192.168.68.116
+  models: [syslog-auto, qwen3.6-27B-code, gemma-4-12b, ornith-1.0-35b]
+```