AI · AI inference category
AI inference — Services
Structurally reshapes LLM inference. Token-Exact audit, hallucination suppression, 3-way byte-exact inference.
Services in this category
- SlimeTree-RLM application (hallucination suppression)○ 早期協業段階 Integrate hallucination-suppression layer into your LLM, 66%→22% measured without weight changes
- Local LM migration / on-prem AI deployment○ Accepting engagements Move off cloud LLM billing. Run on-prem Gemma 4 12B class + SlimeTree-RLM quality gate; cut monthly token spend to 1/10 - 1/20. R-meta verdict allows escalation to cloud frontier in the same pipeline.
Local LM migration — 4 viable patterns
For enterprises moving off cloud LLM billing, run a 12B-class model (Gemma 4 12B etc.) on in-house GPU. SlimeTree-RLM's R-meta verdict treats cloud and local LMs through the same interface, so it slots into your escalation design unchanged.
A
Compliance-bound domains
Healthcare / legal / finance / defence — sectors where cloud LLMs are blocked by regulation. SHA-256 audit chain meets audit requirements out of the box.
B
High-volume routine inference
10M+ tokens/month on classification, summarisation, drafting, RAG ingestion. One RTX 5060 Ti sustains 3.6M tokens/day; capex recovers in ~3 months.
C
Narrow-domain specialist (LoRA)
Tax Q&A, manufacturing SOP, internal policy lookup. LoRA fine-tuning lifts a 12B base to frontier-general parity inside the domain.
D
Hybrid (the headline)
90-95% handled locally, 5-10% escalated to cloud frontier. Frontier-class quality at 1/10 - 1/20 of the bill, measured on real traffic.
In-house measurement (2026-06-05, RTX 5060 Ti / Gemma 4 12B)
| Metric | gemma4:12b Q4_K_M | Notes |
|---|---|---|
| Decode speed | 43.5 tok/s | Sustained on a single GPU |
| Peak VRAM | 8.6 GB | Plenty of headroom on a 16 GB GPU |
| SlimeTree-RLM judge p99 | ~100 µs | 4-5 orders faster than cloud LLM-as-judge |
| Quality "sufficient" rate (n=50) | 47/50 | First-draft + human-review business grade |
See /integrations/#multi-agent Local LM extension for the technical detail.
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