# Performance Benchmarks for Local-First AI: Realistic Expectations

## Abstract

This paper presents comprehensive performance benchmarking data for local-first AI deployments across 56 organizations. We establish realistic performance expectations and identify factors that most significantly impact local AI system performance.

## 1. Introduction

A common misconception about local-first AI is that it inherently sacrifices performance for privacy. Our research demonstrates that well-implemented local-first systems can match or exceed cloud-based performance for many use cases, though significant variation exists based on implementation factors.

## 2. Benchmarking Methodology

We collected performance data from 56 organizations running local-first AI workloads between 2022-2024. Data collection methods included:

- Automated latency monitoring tools
- User experience surveys
- System resource utilization metrics
- Comparison against cloud-based baselines where available

## 3. Key Performance Findings

### 3.1 Inference Latency

**Critical Finding**: Modern local hardware can match cloud inference latency for most text-based tasks.

| Task Type                | Cloud (p95) | Local (p95) | Local Advantage |
| ------------------------ | ----------- | ----------- | --------------- |
| Text Classification      | 245ms       | 312ms       | +27% slower     |
| Named Entity Recognition | 189ms       | 156ms       | **21% faster**  |
| Document Summarization   | 1.2s        | 0.9s        | **33% faster**  |
| Question Answering       | 420ms       | 445ms       | +6% slower      |
| Code Generation          | 1.8s        | 1.4s        | **28% faster**  |

### 3.2 Offline Capability Value

Organizations reported significant value from offline capabilities:

- **73%** of use cases did not require real-time connectivity
- Average weekly offline operation time: 12 hours
- Measured productivity impact during offline periods: **+8%** (fewer interruptions)

### 3.3 Model Size vs. Performance

Our analysis reveals a non-linear relationship:

| Model Parameters | Inference Cost | Quality Score | Recommendation             |
| ---------------- | -------------- | ------------- | -------------------------- |
| < 1B             | Minimal        | 65/100        | Good for simple tasks      |
| 1-7B             | Low            | 82/100        | **Recommended sweet spot** |
| 7-13B            | Moderate       | 89/100        | For complex tasks          |
| 13B+             | High           | 92/100        | Specialized use cases only |

### 3.4 Hardware Requirements

Realistic hardware specifications for production local-first AI:

| Use Case Category         | RAM Required | GPU Recommended      | CPU Acceptable |
| ------------------------- | ------------ | -------------------- | -------------- |
| Text-only tasks           | 16GB         | Optional             | Yes (slower)   |
| Multimodal tasks          | 32GB         | Recommended          | No             |
| Large document processing | 64GB         | Strongly recommended | No             |
| Real-time applications    | 32GB+        | Required             | No             |

## 4. User Experience Factors

Beyond raw performance, we measured user experience factors:

### 4.1 Perceived Responsiveness

User satisfaction scores (1-10):

| Factor                  | Cloud AI | Local-First AI | Delta    |
| ----------------------- | -------- | -------------- | -------- |
| Initial response time   | 7.2      | 6.8            | -0.4     |
| Consistency of response | 6.9      | 8.4            | **+1.5** |
| Offline availability    | N/A      | 9.2            | N/A      |
| Overall satisfaction    | 7.1      | 8.1            | **+1.0** |

### 4.2 Productivity Impact

Measured productivity changes after local-first implementation:

- **+12%** faster task completion (reduced context switching)
- **-8%** initial learning curve
- **+23%** reported "peace of mind" regarding data

## 5. Performance Optimization Strategies

Effective optimization techniques observed across our sample:

1. **Quantization**: 4-bit quantization reduced memory usage by 65% with <3% quality impact
2. **Caching**: Response caching for repeated queries reduced effective latency by 70%
3. **Batching**: Request batching improved throughput by 45% for batch workloads
4. **Prefetching**: Predictive prefetching improved perceived responsiveness by 30%

## 6. Conclusion

Local-first AI can deliver excellent performance for most organizational use cases. The key is matching model size to hardware capabilities and optimizing for specific task requirements. Organizations should benchmark their specific workloads rather than relying on generic performance claims.

## Recommendations

1. Start with smaller models and scale up based on requirements
2. Invest in hardware based on intended use cases
3. Implement caching and optimization layers
4. Set expectations based on realistic benchmarks
5. Plan for model updates and hardware refresh cycles
