prose-contracts/inference-optimization.prose.md at feat/gpu-128k-genesis-hermes-v3-20260717

Files

T

root b9149bce47 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)

2026-07-17 10:59:45 +00:00

3.4 KiB

Raw Permalink Blame History

name, kind, id

name	kind	id
inference-optimization	responsibility	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

-- 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]

3.4 KiB Raw Permalink Blame History