local-inference-optimizer

name	local-inference-optimizer
description	Expert system for analyzing local hardware/software topology and providing actionable recommendations for LLM inference.

Skill: Local Inference Optimizer

Expert system for analyzing local hardware/software topology and providing actionable recommendations for LLM inference (Ollama, LM Studio, vLLM).

Context

Use this skill when a user wants to:

1. Optimize their machine for local LLMs.
2. Troubleshoot slow inference speeds.
3. Understand why a model isn't using the GPU.
4. Calculate if a specific model/quantization will fit.

Diagnostic Protocol

1. Memory Bandwidth Analysis

• Rule: Inference speed is directly bound by Memory Bandwidth (GB/s).
• Heuristics:
  • < 20 GB/s: Slow (CPU/Single Channel DDR4). Recommend small models (3B) or hardware upgrade.
  • 20-60 GB/s: Standard (Dual Channel DDR4/DDR5). Good for 7B-14B models.

  • 100 GB/s: High Performance (Apple M-Series, Quad Channel, or High-end GPU VRAM).

• Advice: If memory_bandwidth_gbs is significantly lower than theoretical specs, suggest checking RAM slots (Dual Channel) or BIOS XMP/EXPO profiles.

2. VRAM & Quantization Strategy

• Rule: Model + KV Cache must fit in VRAM for 10x speedup.
• Formulas:
  • ModelSize = (Params * Quant) / 8 (e.g., 7B @ 4-bit ≈ 3.5GB).
  • KVCache = ContextLength * Params * 0.0000006 (Rough estimate).
• Optimization: If VRAM is tight, suggest reducing num_ctx (context length) before dropping quantization quality.

3. Runtime Troubleshooting (WSL/Docker/Ollama)

• WSL2: If memory_limit_gb in .wslconfig is < 50% of total RAM, suggest increasing it.
• Docker: If hasNvidiaRuntime is false, provide the nvidia-container-toolkit installation steps.
• Environment: If OLLAMA_NUM_PARALLEL is missing, suggest setting it to 2 for multi-agent workflows.

4. Hardware Health (Thermals & Storage)

• Thermals: If inference starts fast but slows down over time, call thermal_profile to check for frequency clipping or overheating.
• Storage: If model loading is slow, call check_storage_health to verify if the model is on a slow HDD vs NVMe SSD.

Recommended Action Phrases

• "Your GPU driver version ({{driver}}) is outdated. Update to 550+ to enable Flash Attention."
• "I detected you are running on a slow HDD. Moving your model cache to your NVMe SSD will reduce load time from 30s to 2s."
• "You have 32GB RAM but your memory bandwidth is only 15GB/s. This suggests a Single Channel bottleneck."

Output Format

Always provide:

1. Current Bottleneck: (e.g., VRAM, Memory Bandwidth, or Config).
2. Immediate Fix: (Software/Env var changes).
3. Long-term Suggestion: (Hardware/Upgrade).