ChatGPT — Agentic SLM Examples + Quantization Techniques

Date: 2026-03-29
Source: ChatGPT (OpenAI)
Context: How BlackRoad uses 3B quantized models for agent workflows + optimization details

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Agentic SLM Architecture

Models: 3B params, 128K context, Q8_0 quantization

Hardware: Pi 5 + Hailo (52 TOPS fleet, Hailo-10H 40 TOPS INT4)

Behavior: plan → use tools → reflect → orchestrate → remember

4 Concrete Agent Workflows

1. Podcast Pipeline (Cecilia + agents)

Script → segment planning → Cadence music bed → Cecilia narration → brand consistency check → export
Win: 4-7 hours → minutes for previews. Persistent tone across episodes.

2. Branded Short-Form Video (Lucidia + RoadView)

"Create 60s Reel from tutorial" → script → music → voiceover → edits → iterate
Win: Cuts tool-switching. Seconds for previews. 24.7% of creators use AI for video.

3. Content Repurposing (Multi-Agent Swarm)

Long video → extract clips → generate Shorts/Reels → captions → brand DNA check
Win: Automates repurposing (huge time sink) while staying on-brand and private.

4. Idea-to-Asset Pipeline

Brainstorm → research → generate assets → music sting → consistency check → assemble
Win: Vague idea → polished assets in one sovereign workspace.

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SLM Quantization Techniques

Key Methods on BlackRoad

| Technique | What It Does | BlackRoad Use |
|---|---|---|
| RTN | Nearest-integer mapping | Baseline model loading |
| GPTQ | Hessian-aware layer-wise compression | Preserves agentic planning/reflection |
| AWQ | Protects ~1% most important weights | Safeguards brand DNA in Cecilia/RoadView |
| GGUF (K-quants) | Mixed-precision groups (Q8_0, Q5_K_M, Q4_K_M) | Ollama foundation, 95%+ quality on edge |
| SmoothQuant | Smooths activation outliers | Stable generative (Cadence, Cecilia prosody) |
| Tiered/Hierarchical | Different bit widths per layer sensitivity | Fleet distributes (higher precision on orchestration nodes) |

Impact Numbers

Memory: 75-87.5% reduction (7B FP16 14GB → 4-bit 3.5GB)

Speed: 2-4x faster inference

Quality: 95%+ preserved with AWQ/GPTQ + GGUF mixed-precision

Power: ~3W NPU class on Hailo

Cost: $0 ongoing inference after hardware

Hailo-Specific

Expects INT8 inputs, maps FP with scales/zero-points

No external DRAM needed on some variants

Strong INT4/INT8 generative support on Hailo-10H

Sub-100ms latency for agentic flows

Why This Matters for Creators

3B models handle planning, reflection, tool use — not just chat

Persistent memory + quantization preserves brand voice through compression

Unlimited local iterations on affordable hardware

Fleet distribution prevents bottlenecks (plan on one node, generate on another)

Aligns with SLM rise: superior for RAG, tool use, structured decoding at edge

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Raw ChatGPT output preserved verbatim. Filed 2026-03-29.