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prose-contracts/inference-optimization.prose.md
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fix: repoint Mumuni refs in Normal-sensitivity contracts to kagentz CT105/.14
Third and final PR of the post-migration contract sweep (ref PRs #54, #55).
Normal-sensitivity contracts per AGENTS.md (any registered agent):

- gpu-fleet: key roster row, Mumuni Agent Profile header + status table
  (thermal-safeguard incident note left as historical record)
- infrastructure-update: apt patch table row; Hermes config path block
  corrected to /home/hermes/.hermes + system unit /etc/systemd/system/
  hermes-gateway.service (was /root/.hermes + user unit)
- infrastructure-maintenance: Hermes gateway health-check target
- inference-optimization: apply-agent-compression host + config_path

All values verified live on kagentz 2026-08-29 (hostname, IP, user,
systemd unit, /home/hermes/.hermes). Abiba-owned refs (.24 pi agent,
GPU monitor :9100) and CRITICAL files intentionally untouched - flagged
to Abiba via relay #739.

Refs PR #54, #55; relay #738/#739.
2026-08-30 01:12:22 +00:00

104 lines
3.6 KiB
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 51K, not 85K. 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: 192.168.68.14
config_path: /home/hermes/.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]
```