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