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Why Local LLMs

Running large language models locally provides significant advantages over cloud-based APIs for many use cases.

Key Benefits

Privacy & Data Sovereignty

Your data never leaves your machine:

  • Code stays local - Proprietary source code, credentials, and business logic remain on your hardware
  • No telemetry - No usage data sent to third parties
  • Compliance - Meet data residency requirements for regulated industries
  • Air-gapped capable - Run inference without any network connection

Cost Efficiency

After initial hardware investment, marginal cost approaches zero:

Scenario Cloud API Local (128GB Mac)
1M tokens/day ~$30-150/day $0
Monthly (30M tokens) $900-4500 $0
Annual cost $10,800-54,000 ~$3,500 one-time

Break-even typically occurs within 2-6 months for heavy API users.

Latency

Local inference eliminates network round-trips:

Metric Cloud API Local
Time to first token 200-800ms 20-100ms
Network dependency Yes No
Rate limits Yes No

Flexibility

Full control over the inference stack:

  • Model selection - Run any open-weights model
  • Parameter tuning - Adjust temperature, top_p, context length
  • No quotas - Generate unlimited tokens
  • Experimentation - Test multiple models without cost concerns

When Local Makes Sense

Ideal Use Cases

Use Case Why Local Works
AI-assisted coding Code privacy, low latency, high token volume
Document processing Private data, batch processing
Development/testing Rapid iteration without API costs
Offline workflows Travel, air-gapped environments

When Cloud APIs Are Better

Scenario Reason
Cutting-edge models GPT-4, Claude 3.5 not available locally
Low volume usage API costs < hardware investment
Mobile/embedded Insufficient local compute
Multimodal (advanced) Image/video models need more VRAM

Model Capabilities

Modern open-weights models are highly capable:

Model Quality Comparison (2025-2026):
┌────────────────────────────────────────────────┐
│ Frontier Models (Cloud Only)                   │
│ GPT-4, Claude 3.5 Sonnet, Claude Opus 4.5      │
├────────────────────────────────────────────────┤
│ Near-Frontier (Runnable Locally)               │
│ Llama 3.1 405B, Qwen 2.5 72B                   │
├────────────────────────────────────────────────┤
│ Excellent Local Options                        │
│ Llama 3.3 70B, DeepSeek V3, Mistral Large 2    │
├────────────────────────────────────────────────┤
│ Fast & Good                                    │
│ Qwen 2.5 32B, Llama 3.2 8B, Mistral 7B         │
└────────────────────────────────────────────────┘

For coding tasks specifically, models like DeepSeek Coder V2 and Qwen 2.5 Coder rival cloud APIs.

Hardware Requirements

See Unified Memory for detailed requirements.

Minimum viable setup: - 32GB RAM - 7-13B models at Q4 - 64GB RAM - 34B models, limited 70B - 128GB RAM - Comfortable 70B, possible 405B at low quant

Getting Started

  1. Review your architecture options
  2. Choose an inference engine
  3. Select appropriate models

See Also