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prose-contracts/inference-optimization.prose.md
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root 1c44bf1259 contract updates: ornith decommissioned, 256K→128K context, Mumuni Discord disabled
Change 1: Strix Halo — ornith decommissioned
- gpu-fleet: Genesis Hermes V3 APEX → qwen3.6-35B-udq4 throughout
- inference-optimization: ornith→strix-moe/qwen3.6-35B-udq4
- gpu-monitor: ornith status → Strix Halo status
- infrastructure-control: Strix Halo — ornith → qwen3.6-35B-udq4
- infrastructure-update: ornith→strix-moe via router
- proxmox-monitor: Strix Halo LLM (ornith) → (qwen3.6-35B-udq4, strix-moe)

Change 2: GPU context 256K→128K fleet-wide
- hermes-agent-baseline: frontmatter description updated
- litellm-health: GPU Fleet Topology table 256K→128K
- litellm-self-heal: GPU Fleet Topology, engine flags, VRAM alert
- inference-optimization: compress threshold 256K→128K compact at 85K
- gpu-fleet: instability note updated

Change 3: Mumuni Discord platform disabled
- gpu-fleet: Mumuni platforms: removed discord
2026-07-23 09:00:42 +00:00

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Markdown

---
name: inference-optimization
kind: responsibility
description: >
Optimizes the full Syslog inference stack — LiteLLM routing weights, GPU model
assignments, agent context management, and prompt caching — to reduce response
times to sub-15s average. All GPUs now at 128K context (stable ceiling).
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 (strix-moe .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
inference 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
prefill time at 532 tok/s. Fix context first, routing second.
- **Route by task**: qwen for code/standard queries; gemma for
compression/auxiliary; strix-moe for compression tasks.
- **Compress aggressively**: threshold at 40% (not 65%) — a 128K window should
compact at 85K, 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). 128K window should compact at 85K (0.65 threshold). 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]
```