I am conducting research on open-source and proprietary LLMs.Who can help me run a query?

Great! I will conduct an extensive research report on open-source and proprietary LLMs. This will include:

• Definitions of open-source and proprietary LLMs.
• A detailed comparison of both types.
• Advantages and disadvantages of each.
• Feasibility of running certain LLMs locally, especially on low-powered hardware.
• Benefits of running open-source models for legal tasks.
• Prioritization of academic and serious sources over opinion-based articles.
• Coverage of relevant information in multiple languages (not just English).

I will ensure the report follows an academic structure, includes historical context and the latest developments, and incorporates major LLMs and benchmarks.

I will notify you once the research is complete!

... had a data leak in March 2023 where some users’ conversation histories were exposed to others – an incident that underscores the trust issues with third-party data handling (Private LLMs: Data Protection Potential and Limitations - Skyflow). In a local deployment of an open model, such risks are internalized and controllable by the user.

Summary – Compliance Advantages of Open-Source Local Models: Running LLMs locally (open-source) offers clear benefits for privacy and compliance: sensitive data stays in-house, satisfying data protection regulations and alleviating concerns of confidential information leaking to outsiders (How Open-Source LLMs are Democratizing AI – Blog). Organizations can more easily document and audit what data the model was trained on and how it’s used, aiding compliance efforts (for example, ensuring a model used for GDPR-related tasks was trained on GDPR-compliant data categories (The Open-Source Advantage in Large Language Models (LLMs))). They can implement their own content moderation and bias mitigation strategies tailored to legal requirements (something noted as an advantage in an academic comparison of open vs closed models for GDPR tasks (The Open-Source Advantage in Large Language Models (LLMs))). In contrast, proprietary models entail trust in the vendor’s compliance and data security measures, which many organizations are reluctant to extend for sensitive contexts (Private LLMs: Data Protection Potential and Limitations - Skyflow) (14 Companies That Issued Bans or Restrictions on ChatGPT - Business Insider).

Intellectual Property and Licensing: With open-source LLMs, users have explicit licenses that grant broad usage rights (e.g. Apache 2.0, MIT), which is legally straightforward – they know what they can and can’t do (e.g., an Apache-2.0 model can be used in commercial products freely as long as attribution is given) (OpenAI vs. open-source LLM: Which model is best for your use case? - UbiOps - AI model serving, orchestration & training). Proprietary models come with service agreements that might restrict usage (for instance, some APIs disallow using outputs to train competing models, or require adherence to content policies). Using an open model avoids being bound by such terms, giving an organization more freedom to use outputs as it sees fit. However, as mentioned, open models do not eliminate IP risk around content – they too learn from possibly copyrighted material. The difference is, if a problematic output appears, a company using an open model can directly address it (e.g., by filtering that output or retraining the model on cleaner data). In one study focusing on generating GDPR-compliant data categories, researchers noted that having an open model allowed them to tweak it to ensure compliance with legal definitions, whereas a closed model was a fixed entity (The Open-Source Advantage in Large Language Models (LLMs)). Moreover, open models can be trained on datasets that exclude proprietary content if needed, reducing IP infringement concerns proactively. Proprietary model users cannot control or know the training corpus – they must take the vendor’s word that it was obtained lawfully and hope the model doesn’t verbatim emit copyrighted text. If legal challenges arise (as in the ongoing cases against OpenAI and others), users of those services might get entangled or at least face uncertainty about who holds liability. With an open model, an organization can better insulate itself: it selects the model and data, and can demonstrate due diligence in respecting IP (for example, by using models trained only on public-domain data, or by applying filters to outputs longer than N words that match a known copyrighted source).

In summary, from a legal/compliance perspective, open-source LLMs (especially when self-hosted) are often advantageous because they allow full control over data handling (critical for privacy laws), transparency for auditing (important for ethical and regulatory compliance), and clear usage rights (reducing licensing ambiguities). Proprietary LLM services may raise red flags with data protection authorities (due to cross-border data flows or indefinite data retention for model training (Italy's ChatGPT ban attracts EU privacy regulators | Reuters)) and can complicate an organization’s compliance stance because of their opacity and one-size-fits-all policies. Many organizations with strict compliance mandates choose open-source or on-premise LLM solutions for these reasons, even if it means accepting slightly lower performance or investing more in engineering, as the trade-off for legal peace of mind is worth it (Private LLMs: Data Protection Potential and Limitations - Skyflow) (How Open-Source LLMs are Democratizing AI – Blog).

6. Additional Relevant Insights and Trends

Beyond the comparisons above, there are broader trends and nuances in the landscape of open-source and proprietary LLMs that merit discussion:

• The Blurring Line Between Open and Closed: The distinction between “open-source” and “proprietary” is not always binary. For instance, some models are released openly with restrictions – often termed “open access” models. Meta’s LLaMA 1 was such a case: the weights were shared with researchers under a non-commercial license (not truly open-source by OSI definition), and yet the model leaked and became widely used in the open community. Its successor LLaMA 2 was released under a license allowing commercial use with certain conditions, indicating a hybrid approach: not entirely unrestricted, but far more open than typical proprietary models. We also see BloombergGPT (50B model for finance) which is proprietary to Bloomberg but described in a paper, and OpenAI now sometimes shares model cards and evaluation results even if not weights. This suggests a future where big players might release smaller or older versions of their models to the public (for research or goodwill) while keeping the cutting-edge version closed. Hybrid approaches are emerging: some companies deploy a closed model for general tasks and fall back to an open model on-prem for tasks involving sensitive data – getting the “best of both worlds.” Researchers Manchanda et al. note that “increasing pressure to adopt openness may lead to hybrid approaches that mediate both paradigms” (The Open-Source Advantage in Large Language Models (LLMs)). We already see this in products like Microsoft’s Azure OpenAI offering a “bring your own key” feature for customer-managed data, or services that let you run community models in a managed cloud. The industry is experimenting with different levels of openness to balance innovation, safety, and commercial interest.

• Competition Driving Innovation: Open-source LLMs have drastically lowered the barrier to entry in AI development across the world. They enable countries and organizations without huge AI labs to participate in advancing the technology. For example, the UAE’s Falcon 40B open model topping leaderboards (UAE's first LLM is open source and tops Hugging Face Leaderboard | WIRED Middle East) spurred pride and further investment in AI regionally. Similarly, the international BigScience project with BLOOM was motivated by ensuring non-English languages and diverse perspectives are represented in language models (A 176B-Parameter Open-Access Multilingual Language Model - arXiv). This open collaboration model has infused the field with fresh ideas – it’s no coincidence that many techniques to reduce model size (like LoRA, quantization methods, distillation recipes) came from academics and independent developers working with open models. On the other hand, the presence of a few dominant proprietary models has set high benchmarks that galvanize the open community (“beat GPT-3” became a rallying cry, achieved by models like EleutherAI’s GPT-NeoX and later LLaMA derivatives). This mutual push-pull drives rapid progress. As VentureBeat reported, closed models dominated early on, but open models “have since closed the gap in quality” and are growing fast (The enterprise verdict on AI models: Why open source will win). It’s telling that within months of ChatGPT’s release, multiple open-source chat models (Vicuna, Alpaca, etc.) reached ~90% of its quality (Who needs a ChatGPT Sports Car When An Alpaca can get you ...). This vibrant competition benefits end users by leading to constant improvements and more choice. We are also seeing specialization as an area of advantage for open models: while big proprietary models aim to be generalists, the community builds niche models (for coding, for chemistry, for specific languages). These specialized open models often outperform general models on domain-specific tasks (e.g., OpenBioMed’s DNA language model, or Med-Alpaca for medical Q&A). This underscores that open-source LLM development isn’t just mimicking closed models; it’s exploring new directions that large companies might not prioritize.

• Ethical and Social Impact Considerations: Openness in AI has ethical ramifications. Many ethicists argue that transparency (open models) is crucial for fairness and accountability, especially for systems that could affect society (e.g., in healthcare or justice) (Open-Source AI Matches Top Proprietary LLM in Solving Tough Medical Cases | Harvard Medical School). A Nature article (2023) titled “The path forward for LLMs in medicine is open” contends that only transparent and controllable models can be trusted in high-stakes medical applications (What percent of your usage of LLMs are closed-source ones (GPT ...). The Harvard Medical School study showing an open model matching GPT-4 on clinical cases explicitly frames its result as a win for open-source, suggesting it will lead to “greater competition…to better serve patients and clinicians” (Open-Source AI Matches Top Proprietary LLM in Solving Tough Medical Cases | Harvard Medical School). Conversely, some AI leaders caution against fully open-sourcing the most powerful models due to misuse risks. OpenAI’s co-founder Ilya Sutskever has spoken about the need for “closed” approaches for very advanced AI to prevent malicious use (Open-Source vs Closed-Source AI Foundation Models - Medium). The balance between these views is shaping policy: for example, the EU AI Act is considering requiring certain foundation model disclosures (leaning toward transparency), while others propose licensing regimes for very advanced models (leaning toward controlled release). We can expect ongoing debate on how to ensure AI safety without hampering the open collaborative spirit that has driven much of AI’s progress. One emerging idea is “responsible open-source” – releasing models with documentation of limitations and guidelines for safe use, and perhaps with technical safeguards (like built-in filters), thus combining openness with a degree of safety. This is already seen in some releases (Meta’s LLaMA 2 came with an extensive responsible use guide).

• Impact on Democratization and Education: The availability of open-source LLMs has tremendous educational value. Students and researchers can study how these models work, adapt them, and even contribute improvements. This builds expertise widely, not just within tech giants. It also means that developing countries or smaller tech firms can experiment with LLMs without needing to purchase expensive API access or have huge compute clusters – lowering the entry bar fosters a more inclusive AI community. Proprietary LLMs, while offering impressive functionality, concentrate AI knowledge and capability in a few firms. This raises concerns of technological sovereignty – for instance, European leaders have voiced worry that AI capability is dominated by US companies, which motivated projects like BLOOM to ensure an open European-led model exists. China has taken a somewhat different route: while many Chinese tech companies are building LLMs, they are mostly proprietary (Baidu’s ERNIE Bot, Alibaba’s Tongyi, etc.), yet the Chinese academic community has also released some open models (e.g., GLM by Tsinghua University, and a multitude of smaller open models on GitHub). They often balance it by open-sourcing base models but not instruction-tuned ones due to censorship requirements. The global trend, however, clearly shows that open-source LLMs are here to stay and are shaping the ecosystem. Even OpenAI, which has become more closed, acknowledges the value of open research – they still publish papers and some model weights (like smaller GPT-2 versions, or embeddings model) for the community. And notably, many proprietary services incorporate open-source components: for example, Microsoft’s Bing Chat uses OpenAI’s model but also the open-source ONNX Runtime and DeepSpeed libraries for optimization; many cloud providers use the Hugging Face Transformers library (open-source) to deploy models. This interdependence means progress in open source often directly benefits closed models’ performance, and breakthroughs in closed settings eventually trickle down to open implementations.

• Future Outlook – Coexistence and Cooperation: It’s likely that open-source and proprietary LLMs will continue to co-evolve in a complementary manner. Proprietary models will push the envelope in scale (reaching trillions of parameters) and in highly optimized interactions (multimodal, tool use, etc.), often underwritten by vast resources. Open models will adopt many of these innovations and also pioneer in directions that serve specific communities or values (like privacy-preserving models, or models that anyone can extend). We may also see more public-private partnerships – e.g., government or academic initiatives using a mix of open tools and private cloud infrastructure to build powerful models for public benefit (similar to how CERN collaborates openly but with big resources). An interesting development is the rise of model hubs (like Hugging Face Hub) and LLM ecosystems where both open and closed models can be compared under common benchmarks (e.g., OpenLLM Leaderboard (Falcon 40B: World's Top AI Model Rewards Most Creative Use ...)). As benchmarking and evaluation become more standardized (the Helm benchmarks, MASSIVE Evaluations, etc.), users will be able to make more informed choices between open and closed models based on empirical evidence (UAE's first LLM is open source and tops Hugging Face Leaderboard | WIRED Middle East). This could further level the playing field, highlighting cases where an open small model is “good enough” or where a closed model is truly superior for a task, enabling a more rational deployment strategy that might mix both. There is also a trend of open models being integrated into closed products: for example, some smaller software vendors integrate an open LLM with their proprietary software to avoid relying on external APIs – effectively creating a proprietary application powered by an open engine. This indicates a symbiosis where open LLMs increase the value of many commercial products (without end-users even knowing, in some cases).

In essence, the additional insight is that the dichotomy of open vs closed is not a zero-sum game; rather, the interplay between the two is advancing the field. Open-source LLMs inject diversity, accessibility, and collaborative scrutiny, while proprietary LLMs drive top-line performance and heavily invest in polishing the user experience and safety. A healthy AI ecosystem will likely involve both: open models ensuring no one is left behind (technologically or in terms of language/cultural representation) and closed models providing cutting-edge capabilities with managed risk until those capabilities diffuse to the open world. This dynamic, if guided well by policies and continued research, could maximize the benefits of LLM technology to society while mitigating downsides.

Conclusion

In summary, open-source LLMs are defined by their transparency and community-driven development, offering freely available model code/weights that encourage adaptation and collaborative improvement (What are open source LLMs? - ServiceNow ) (The Open-Source Advantage in Large Language Models (LLMs)). Proprietary LLMs, conversely, are closed models owned by companies, accessible only through restricted channels (often paid APIs) and maintained as trade secrets (What are open source LLMs? - ServiceNow ). Each paradigm has distinct characteristics: open models excel in accessibility, customizability, and fostering trust via transparency (What are open source LLMs? - ServiceNow ) (OpenAI vs. open-source LLM: Which model is best for your use case? - UbiOps - AI model serving, orchestration & training), whereas closed models typically lead in raw performance, deployability (as a turnkey service), and integrated safety measures (The Open-Source Advantage in Large Language Models (LLMs)) (What are open source LLMs? - ServiceNow ).

A comparison across key dimensions reveals nuanced trade-offs. Open models democratize AI – anyone can use or tweak them, which has accelerated innovation and enabled use-cases in resource-constrained settings. They come with lower (direct) costs and no subscription fees (OpenAI vs. open-source LLM: Which model is best for your use case? - UbiOps - AI model serving, orchestration & training), but require investment in hardware and expertise to deploy effectively (OpenAI vs. open-source LLM: Which model is best for your use case? - UbiOps - AI model serving, orchestration & training). Proprietary models offer convenience and top-notch results out-of-the-box, backed by enterprise support, but they entail ongoing costs and cede control to the provider (OpenAI vs. open-source LLM: Which model is best for your use case? - UbiOps - AI model serving, orchestration & training) (What are open source LLMs? - ServiceNow ). In terms of security and ethics, open models provide transparency (allowing outside scrutiny for biases (The Open-Source Advantage in Large Language Models (LLMs))) but can be wielded by bad actors without restriction (What are the risks of open source LLMs?). Closed models can enforce usage policies and monitor abuse (The Open-Source Advantage in Large Language Models (LLMs)), yet their opacity can hide biases and limit accountability (The Open-Source Advantage in Large Language Models (LLMs)).

Both approaches have strengths and weaknesses illustrated by real-world cases. Open-source LLMs like Alpaca and Vicuna demonstrated that relatively small, community-built models can approximate the capabilities of large proprietary models at a fraction of the cost (Stanford researchers make a new ChatGPT with less than $600) (Who needs a ChatGPT Sports Car When An Alpaca can get you ...), showcasing the power of open innovation – though issues like Alpaca’s shortfall in content filtering also highlighted the challenges of handling safety without a centralized system (Stanford researchers make a new ChatGPT with less than $600). Proprietary LLMs such as GPT-4 have enabled hugely popular applications and business solutions with state-of-the-art performance, but incidents like companies banning employee use of ChatGPT due to privacy concerns (14 Companies That Issued Bans or Restrictions on ChatGPT - Business Insider) underscore the trust and compliance hurdles they face.

The feasibility of local execution of LLMs has significantly improved, to the point that end-users can run models with tens of billions of parameters on consumer-grade hardware by leveraging optimizations like 4-bit quantization (Deploying LLMs on Small Devices: An Introduction to Quantization | by TitanML | Medium). This opens up a middle path where smaller open models running locally handle sensitive or offline tasks, while cloud-based proprietary models handle others – a strategy many organizations are adopting to balance privacy, cost, and quality. Indeed, the ability to keep data local with open models addresses critical legal and compliance needs, making them especially attractive for regulated industries or any scenario dealing with confidential information (How Open-Source LLMs are Democratizing AI – Blog) (Private LLMs: Data Protection Potential and Limitations - Skyflow). Open-source models empower organizations to adhere to data protection laws and maintain intellectual property control, whereas proprietary models, by default, introduce a third-party into the data flow, raising issues that regulators and legal departments are increasingly scrutinizing (Italy's ChatGPT ban attracts EU privacy regulators | Reuters) (14 Companies That Issued Bans or Restrictions on ChatGPT - Business Insider).

Finally, the evolving landscape suggests that rather than one paradigm “winning” over the other, we are likely to see complementary coexistence. Serious academic sources and industry analyses indicate that open-source contributions are vital for igniting innovation and ensuring ethical deployment, even as proprietary efforts push the frontier of capability (The Open-Source Advantage in Large Language Models (LLMs)) (Open-Source AI Matches Top Proprietary LLM in Solving Tough Medical Cases | Harvard Medical School). Many experts advocate that critical applications (like medicine, law, government) incorporate transparent open-source models for better oversight (What percent of your usage of LLMs are closed-source ones (GPT ...) (Open-Source AI Matches Top Proprietary LLM in Solving Tough Medical Cases | Harvard Medical School), while proprietary models might serve general consumer applications where top performance is desired and the data stakes are lower.

In conclusion, choosing between open-source and proprietary LLMs involves careful consideration of factors like use-case requirements, resource availability, and risk tolerance. Open-source LLMs offer accessibility, freedom from vendor lock-in, and community-vetted ethics – aligning well with needs for customization, privacy, and academic exploration (How Open-Source LLMs are Democratizing AI – Blog) (The Open-Source Advantage in Large Language Models (LLMs)). Proprietary LLMs offer cutting-edge performance, ease of use, and often more robust safety out-of-the-box – aligning with needs for high-quality results, reliable support, and fast integration (The Open-Source Advantage in Large Language Models (LLMs)) (What are open source LLMs? - ServiceNow ). A well-informed decision will weigh these factors, possibly integrating both: using open-source models where control and transparency are paramount, and leveraging proprietary models where their advantages outweigh the downsides.

Ultimately, the continued advancement of LLM technology will be best served by a synergy between open and closed efforts, ensuring that AI’s benefits are widely accessible while still enabling the focused investments that drive dramatic improvements. The academic literature and industry trends reviewed here support the notion that openness in AI is not just a philosophical stance but a practical driver of innovation and trust (The Open-Source Advantage in Large Language Models (LLMs)) (Open-Source AI Matches Top Proprietary LLM in Solving Tough Medical Cases | Harvard Medical School). By understanding the distinctions and leveraging the strengths of each model type, stakeholders can responsibly harness LLMs to their fullest potential – whether running an open-source model on a local server for a sensitive task, or calling a proprietary API to solve a complex problem – and even combine them in creative ways to achieve optimal outcomes with well-founded confidence.

Sources:

1. Manchanda, J. et al. (2024). The Open-Source Advantage in Large Language Models (LLMs) (The Open-Source Advantage in Large Language Models (LLMs)) (The Open-Source Advantage in Large Language Models (LLMs)) (The Open-Source Advantage in Large Language Models (LLMs)) – Position paper discussing open vs closed LLM development, transparency, and ethics.
2. ServiceNow (2023). What are open source LLMs? (What are open source LLMs? - ServiceNow ) (What are open source LLMs? - ServiceNow ) (What are open source LLMs? - ServiceNow ) – ServiceNow AI blog explaining differences in transparency, ownership, and support between open-source and proprietary LLMs.
3. UbiOps (2023). OpenAI vs. open-source LLM: Which model is best for your use case? (OpenAI vs. open-source LLM: Which model is best for your use case? - UbiOps - AI model serving, orchestration & training) (OpenAI vs. open-source LLM: Which model is best for your use case? - UbiOps - AI model serving, orchestration & training) (OpenAI vs. open-source LLM: Which model is best for your use case? - UbiOps - AI model serving, orchestration & training) – Technical report comparing costs, development time, and maintenance of open-source vs OpenAI models in enterprise.
4. Stanford Daily (Turk, 2023). How Stanford researchers made a new ChatGPT with <$600 (Alpaca) (Stanford researchers make a new ChatGPT with less than $600) – News article on Stanford’s Alpaca model (open 7B LLaMA) achieving GPT-3.5-like performance, but with noted filtering limitations (Stanford researchers make a new ChatGPT with less than $600).
5. Harvard Medical School News (Gaige, 2025). Open-Source AI Matches Top Proprietary LLM in Medical Cases (Open-Source AI Matches Top Proprietary LLM in Solving Tough Medical Cases | Harvard Medical School) (Open-Source AI Matches Top Proprietary LLM in Solving Tough Medical Cases | Harvard Medical School) – NIH-funded study showing an open 405B model equaling GPT-4 on clinical diagnostics, advocating open models for competition and choice.
6. Wired (Middle East) (2023). UAE’s Falcon 40B tops Hugging Face Leaderboard (UAE's first LLM is open source and tops Hugging Face Leaderboard | WIRED Middle East) – Report on an open-source 40B model outperforming many others with less training compute, demonstrating open model excellence.
7. TitanML (2023). Deploying LLMs on Small Devices (Quantization) (Deploying LLMs on Small Devices: An Introduction to Quantization | by TitanML | Medium) – Medium article quantifying memory requirements: 7B LLaMA at 4-bit uses ~3.5 GB (enabling smartphone deployment).
8. Hugging Face (Pagnoni et al., 2023). 4-bit Quantization and QLoRA (Making LLMs even more accessible with bitsandbytes, 4-bit quantization and QLoRA) – Article/paper introducing QLoRA, allowing finetuning of a 65B model on a single 48GB GPU (99% of ChatGPT performance), illustrating resource reduction techniques.
9. LaunchPod (2023). Leveraging Open-Source LLMs for Data Privacy and Compliance (How Open-Source LLMs are Democratizing AI – Blog) (How Open-Source LLMs are Democratizing AI – Blog) – Blog highlighting that on-prem open LLMs keep sensitive data internal, a big advantage in finance/healthcare for confidentiality and GDPR compliance.
10. Business Insider (2023). Companies banning ChatGPT (14 Companies That Issued Bans or Restrictions on ChatGPT - Business Insider) (14 Companies That Issued Bans or Restrictions on ChatGPT - Business Insider) – Article listing firms (Verizon, Samsung, etc.) restricting ChatGPT use due to risks of source code or customer data leakage, underscoring corporate privacy concerns with proprietary LLMs.