Evaluating the Promise and Limitations of Local Large Language Models

Here we explore the potential of local LLMs—models that run fully on-device without cloud connectivity—to address some of these barriers while also examining their current limitations.

For organizations exploring AI adoption, local LLMs offer a compelling value proposition:

• Enhanced Privacy and Data Security: With local models, all data stays on device rather than transmitting to the cloud. This avoids potential privacy issues related to cloud storage, transmission, and third-party access of potentially sensitive data. Data governance is simplified without the need to comply with external provider policies.

• Lower Latency: Eliminating roundtrips to the cloud reduces delays between prompts and responses. Local LLMs offer near real-time performance critical for applications like live translations and real-time analytics.

• Customization and Control: In theory, housing models internally rather than relying on external providers offers more control over customization to specific domains. However, as explored later, current constraints around tuning large models may limit this advantage in practice.

• Reduced Long-Term Costs: For high-volume use cases, local LLM costs can eventually become more affordable than ongoing cloud service fees. But upfront infrastructure and training costs can be extremely high. For many organizations, this combination of privacy, speed, control, and potential cost savings makes investing in local LLM development appealing despite their evolving capabilities.

Current Barriers to Widespread Local LLM Adoption

Organizations should also carefully consider several key factors that currently limit viability of production-level local LLM deployments:

• Possible Prohibitive Upfront Costs: Training and running large neural networks is enormously computationally expensive. Developing local LLMs on the cutting-edge costs millions to tens of millions of dollars depending on model size and data needs—likely unfeasible for most organizations.

• Inadequate Accuracy and Reliability: To date, the majority of local LLMs significantly underperform leading cloud-based models in critical areas like output accuracy, completeness, and error rate. They often fail even basic test cases that commercial models handle reliably.

• Insufficient Customization Capabilities: Fine-tuning is necessary to optimize most LLMs for specific applications. But full fine-tuning remains computationally infeasible for large local models. This severely limits customization compared to commercial offerings.

• Lack of Responsible Governance: As local models become more advanced, risks around bias, privacy, security, and responsible AI practices intensify. Most organizations lack governance frameworks to address these concerns.

• Ongoing Infrastructure Constraints: The computational requirements of local LLMs can still strain even high-end hardware when deployed at scale. Specialized hardware and optimizations are necessary to run efficiently. These factors can make local LLM adoption at scale impractical currently for most organizations. However, rapid open-source innovation aims to overcome these barriers.

Tracking Local LLM Progress

Despite current limitations, local LLMs have advanced remarkably in recent months. Initiatives like Meta’s Code Llama70B and Mistral’s 8x7B Mixture of Experts models demonstrate impressive gains in model performance.

To track progress, organizations should leverage testing practices from software engineering. Techniques like prompt engineering combined with regression test suites allow quantifying local LLM improvements over time. Frameworks like PromptFu lower the barrier to rigorous benchmarking.

Key metrics to monitor include:

• Output accuracy: % of correct, complete responses across diverse test cases.

• Latency: Time delay between prompt and response.

• Error rate: Frequency of nonsensical, illogical, or factually incorrect responses.

• Customization lag: Performance relative to cloud models for organization-specific domains. As model architectures, training processes, and hardware infrastructure continue advancing rapidly, local LLM accuracy and capabilities are expected to improve dramatically.

Organizations must balance tradeoffs based on their specific priorities, use cases, and constraints using rigorous benchmarking.

Implications for Organizations Contemplating Local LLMs

For executives exploring AI adoption, how should local LLM uncertainty factor into strategy?

Here are some best practices based on the current state of technology:

• Evaluate Tradeoffs Holistically: Frame decisions by weighing factors like privacy, security, accuracy, latency, and customization based on organizational needs—not technology hype. Let use cases and constraints guide evaluations.

• Build Governance Up Front: Don't wait for risks to emerge. Proactively implement responsible governance spanning model development, testing, monitoring, and oversight before deployment. Prioritize ethics alongside performance gains.

• Monitor Innovation Closely: Stay on top of local LLM advancements through benchmarking and don't overcommit to long-term cloud contracts. But also, don't stall waiting for immature technology. Find the right balance for your needs.

• Prepare Infrastructure Strategically: Pursue hardware and optimization strategies that leave flexibility for future local LLM adoption without over-investing preemptively while technology remains unstable. Focus infrastructure buildouts on organizational requirements rather than hypothetical local LLM needs.

• Customize Conservatively: Resist over-customizing cloud models before evaluating if local alternatives can meet needs more aligned to priorities like privacy and cost. But don't assume local LLMs guarantee full control—customize thoughtfully based on constraints.

Without a doubt, the local LLM landscape will continue evolving rapidly. Our advice is to avoid either under or over-committing. But with rigorous benchmarking, responsible governance, and evaluating tradeoffs holistically, organizations can strategically navigate this uncertainty.

Sources:

[1] https://youtu.be/urymhRw86Fc?si=lL7BQ9TQnvoKU7L

[2] https://www.promptfu.com/