Navigating the landscape of open source LLM classifications for enterprises

As enterprises stand on the brink of the artificial intelligence revolution, the emergence of Large Language Models (LLMs) has presented a game-changing opportunity across industries. Now, more than ever, executives, managers, and directors must recognize the significance of incorporating open source LLMs into their operations, which promises not only enhanced efficiency but also strategic advantages over traditional proprietary solutions. 

In this article I delve deep into understanding the potential of open source LLMs, their benefits, challenges, performance benchmarks, infrastructure needs, costs, drawbacks of using popular online LLMs, and a comparative rating of popular LLMs.

Why open source?

The transition from proprietary technologies to open source is not merely a trend; it’s a fundamental shift in how organizations handle data and technology. Proprietary LLMs—such as those offered by industry giants like OpenAI—often raise concerns around trust and transparency. Companies utilizing these models rely heavily on external vendors, and the lack of insight into the underlying technologies creates potential vulnerabilities.

Open source LLMs offer a refreshing alternative. By allowing organizations to access the source code, open source LLMs empower businesses with trust through transparency—an increasingly vital aspect in today’s data-driven world. Not only that but the cost structure of open source models is an attractive differentiator. Most, whether through free licenses or modest costs associated with cloud services, provide you with a pathway toward effective AI deployment without the exorbitant costs often linked with proprietary options.

The benefits of open source LLMs for enterprises

Adopting open source LLMs comes with a plethora of benefits:

• Enhanced data privacy and control: Open source solutions allow your enterprise to retain ownership of your data, significantly reducing risks associated with data breaches or unauthorized access. Open source LLMs’ transparency enables thorough audits and verification of data handling, unlike proprietary on-premise/cloud solutions. Access to the source code allows businesses to implement customized privacy measures tailored to their specific regulatory requirements. Additionally, managing their own infrastructure reduces third-party access to sensitive data, lowering the risk of unauthorized access or leaks. Furthermore, open source solutions prevent dependency on single vendor policies, ensuring long-term data privacy and mitigating vendor lock-in risks.
• Cost savings and reduced vendor dependency: Open source models eliminate expensive licensing fees, allowing your business to redirect resources toward innovation or operational efficiency.
• Increased flexibility and adaptability: Your organization can customize open source LLMs for specific use cases, tailoring the models to meet your unique challenges and requirements.

What are the main challenges in the implementation of open source LLMs?

While the benefits are substantial, transitioning to open source solutions is not without hurdles. Businesses must navigate several challenges:

• Risks associated with open source solutions: Concerns about support and stability can deter organizations. Enterprises must integrate comprehensive governance structures to mitigate risks related to security and compliance.
• Performance disparities: While open source models deliver excellent results, they may exhibit performance limitations compared to proprietary counterparts, impacting service quality.
• Hidden costs: Potential expenses associated with maintenance, training, and infrastructure must be considered when deploying LLMs. Understanding these factors is crucial to achieving true cost savings.

Not so fast… these are the drawbacks of using popular online LLMs for enterprises

While popular online LLMs can offer considerable benefits, their application in enterprise settings comes with inherent pitfalls:

• Limited customization: Online LLMs typically come with fixed architectures and capabilities. This lack of customization can hinder organizations from fine-tuning the models to suit their specific needs.
• Data privacy concerns: Using online LLMs means sending data to external servers, which can raise serious concerns about data confidentiality, especially for sensitive business information. This can lead to compliance challenges, particularly in regulated industries.
• Recurring costs: While initial usage might be affordable, the per-token pricing model associated with many online LLMs can lead to escalating costs as usage grows. Enterprises might find scaling these solutions financially challenging in the long run.
• Dependency on internet connectivity: Relying on online LLMs necessitates constant internet access, which can lead to disruptions in service during outages or connectivity issues.
• Performance variability: Online LLMs can experience performance issues due to server load or other external factors, potentially affecting the response time and reliability expected in an enterprise context.

Let’s understand performance metrics for enterprises

For enterprises keen on evaluating the performance of open source LLMs, identifying the right Key Performance Indicators (KPIs) is crucial. Your metrics could include for example:

• Response time: The speed at which the model generates responses.
• Accuracy: The fraction of accurate outputs versus total responses.
• Scalability: The ability of the model to maintain performance under larger workloads.

Performance benchmarks are essential for establishing baseline expectations and optimizing models for various use cases. Utilizing resources such as the Hugging Face Open LLM Leaderboard can guide your organization toward models that align with your performance goals.

Find “the one”: Rating of popular open source LLMs

To further assist decision-makers, here are comparisons of some leading open source LLMs:*

Model	Parameters	Key Characteristics	Best Use Cases	Performance Rating (out of 10)	Memory Requirement	GPU/CPU Requirement
LLaMA 2	7B, 13B, 70B	High flexibility, transparency, suitable for conversational AI	Chatbots, summarization	9	6GB (7B), 32GB (13B), 80GB (70B)	1 x A100 (80GB) GPU or 2 x RTX 3090
Falcon 180B	180B	Robust performance, cost-for-performance advantage	Natural language processing tasks, multi-turn dialogue	9.5	256GB	4 x A100 (80GB) or equivalent high-end GPUs
BLOOM	176B	Multilingual capabilities, community-driven development	Translation, content generation	9	384GB	8 x A100 (80GB) for efficient operation
Mistral 7B	7B	Fast inference speed, strong conversational capabilities	Customer service applications	8.5	16GB	1 x RTX 3090 or similar GPU
OPT-175B	175B	Comparable performance to larger proprietary models	Academic research	8	220GB	2 x A100 (40GB) or higher
GPT-NeoX	20B	High accuracy with wide domain applicability	Text generation, summarization	8	40GB	1 x A100 (40GB) or higher
Vicuna 13B	13B	Fine-tuned for conversational tasks, near ChatGPT quality	Intelligent chatbots	9	32GB	1 x RTX 3090 or similar GPU

* As hardware requirements can vary between versions and workloads, please view the data in the table as indicative of the ranges of hardware needs, keeping in mind that these may evolve over time.

Got what it takes? The infrastructure and hardware requirements

Good question. Deploying open source LLMs forces enterprises to confront the realities of infrastructure and success hinges on understanding computational resource needs:

• CPU vs. GPU: While CPU options can be cheaper, GPUs typically provide significant speed advantages for training and inference tasks. A hybrid approach—utilizing GPUs for intensive tasks while leveraging CPUs for other operations—is often advisable.
• Memory requirements: As highlighted in the table above, memory requirements can vary drastically based on the model size and complexity under consideration. For instance, while smaller models (7B) can operate effectively with around 16 GB of GPU memory, larger models like BLOOM require robust setups containing upwards of 384 GB.
• Server configurations: Enterprises should assess hardware configurations suitable for their chosen LLM. For instance, 7B parameter LLMs can perform admirably with single GPUs like the Nvidia RTX 3090, while models with larger parameters, such as Falcon 180B, require substantial infrastructure investments to deliver optimal performance.

Things to consider when implementing an open source LLM

Cost analysis of scaling

Long-term effectiveness and sustainability rest on powerful cost analysis. Balancing anticipated short-term expenses with long-term operational costs is crucial. Initial deployment and infrastructure setup may present significant costs, but ongoing ownership and maintenance of open source LLMs can yield substantial savings, especially when there are no licensing fees.

Be mindful of the skills gap in AI and LLMs

Given the AI-driven landscape, developing technical competencies within organizations is essential. Key skills required for successful LLM deployment and management include:

• Data science and Machine Learning: Proficiency in these areas is paramount, as teams must understand model training, evaluation, and fine-tuning methodologies.
• Cloud architecture: As many open source models are deployed on cloud platforms, familiarity with cloud infrastructure is crucial for effective model management.

A word on RAG (Retrieval-Augmented Generation)

Recent advancements like Retrieval-Augmented Generation (RAG) significantly enhance the capabilities of open source LLMs. By integrating embeddings with robust databases like MongoDB, organizations can optimize data retrieval and task performance, allowing enterprises to dynamically access pertinent information. This synergy facilitates the generation of accurate and contextually relevant responses, resulting in improved customer experiences and operational efficiencies.

Open source LLMs have a bright future

Open source LLMs present a promising horizon for enterprise innovation. The shift from proprietary to open source models is not just a technical transition; it embodies a foundational change in corporate strategy that values transparency, control, and adaptability. 

As businesses explore open source solutions, understanding their benefits, facing the challenges ahead, and recognizing performance metrics will be critical. Embracing these models not only accelerates digital transformation but also positions organizations for future success in a rapidly evolving AI landscape.

Open source LLMs will become increasingly integral to enterprise strategies, and the opportunity to lead in this space rests squarely within the grasp of decision-makers today.