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Episode-XXVI: The AI Rings
Authors: Sankar Panneerselvam , Francisco Mellado K., Marc Methot, Fatih E. Nar.
In Tolkien’s legendary universe, the Rings of Power gifted unique abilities on their borrowers (Nobody is Ever-King in the Land of Time) while binding them to different destinies. Today’s AI foundation models similarly grant people & organizations distinct capabilities while creating various dependencies/addictions.
As we navigate the rapidly evolving AI landscape, a useful framework emerges — one that categorizes these powerful models based on their capabilities, deployment strategies (+ their needs from servants), and implications.
The One Ring: Supreme Power
GPT-4o (OpenAI) sits on top of our pyramid as The One Ring (at the time of writing this article) — the supreme model that rules them all. Like Sauron’s creation, it offers unmatched power; multimodal capabilities across text, vision, and speech with real-time performance.
With over 175 billion parameters and a massive 128,000-token context window (lately overshadowed with Llama-4’s 10M context window length), GPT-4o delivers unparalleled reasoning abilities, creative generation, language tokenizations and multimodal understanding. Its capabilities extend from solving complex mathematical problems to generating code, understanding images, and producing nuanced responses that feel distinctly human.
However, just as the One Ring corrupted its bearer, relying too heavily on GPT-4o carries risks: vendor lock-in, high operational costs, and sacrificing data sovereignty. Organizations must consider these tradeoffs carefully when wielding such power.
Challenger to the Throne: Google’s Gemini 2.5 Pro & Meta’s Llama-4 (offered as 3 flavors — Behemoth, Maverick and Scout) have emerged as a serious contender, outperforming GPT-4 on several key benchmarks and capability limits in 2025.
The Three Rings: Elite Proprietary Models
The Three Elven Rings were crafted for preservation, understanding, and far-seeing wisdom. Their AI counterparts serve similar purposes:
Claude 3.7 Sonnet (Anthropic) — Nenya
Like Nenya (The Ring of Water), Claude preserves and protects knowledge with its extensive 200K context window and ethical guardrails. It excels at understanding lengthy documents, maintaining consistent conversation over thousands of turns, and providing responses that adhere to constitutional AI principles. Claude consistently ranks among the top three models on most benchmarks, with particular strengths in factual accuracy and bias avoidance.
Note: Even if you hold pro-subscription you are still bound by the context length per Claude-session, that may kill the vibe :-).
Gemini 2.0 Flash (Google) — Narya
Reminiscent of Narya (The Ring of Fire), Gemini inspires action with its agentic capabilities and million-token context window. This empowers complex, multi-step reasoning and allows it to execute tasks by internally calling APIs and tools without human intervention. Gemini Flash particularly excels at mathematical reasoning, scoring impressive results on challenging benchmarks.
Note: As Google being Google, it serves their model on proprietary platforms and hardware (tensorflow processing unit -tpu-) tuned for their models, the horizontal scale approach (with hybrid cloud; public cloud + on-prem) may not be possible.
Llama 4 (Meta) — Vilya
Similar to Vilya (The Ring of Air), Llama 4 offers far-seeing capabilities with its open approach and remarkable 10 million token context window. This context length is unprecedented, allowing the model to analyze entire codebases or books in a single pass. Meta’s “Maverick” fine-tuned version ranked second on LM Arena in early 2025, demonstrating that open models can compete with their proprietary counterparts.
Note: As honoring Meta’s secretive way of working, apparently the benchmarked Llama 4 model instance is not necessarily the same model available for public use.
These models offer elite capabilities, but each comes with its own form of “binding” — Claude with its sometimes excessive caution, Gemini with its Google ecosystem integration, and Llama with Meta’s complex licensing terms.
The Seven Rings: Powerful Domain Specialists
Seven rings were gifted to the Dwarf-lords, known for their craftsmanship and singular focus. In our AI landscape, the “Seven Rings” correspond to specialized models that excel in particular domains or regions:
> DeepSeek R-1 (DeepSeek AI) — The Mixture of Expert -MoE- Marvel
A 671B parameter MoE model with only 37B active parameters per token, delivering 30× more cost-efficiency than GPT-4 with impressive reasoning capabilities. Its MIT license makes it fully open for any use.
Note: Mixture of Expert approach prevents complete model run through for a given token, only runs relevant layers and expert networks. This lowers the infrastructure need and latency of a model output generation.
> Qwen2.5-Max (Alibaba) — The Efficiency Champion
With 72B parameters and exceptional low latency, this model balances power with practical deployment needs. It excels in code generation, automated forecasting, and bilingual Chinese-English tasks.
> QwQ-32B (Alibaba) — The Math Specialist
This model outperforms much larger counterparts specifically on mathematical reasoning while requiring significantly fewer resources, making it ideal for scientific applications.
> Mistral Large (Mistral AI) — The European Contender
Developed in the EU with a focus on speed and efficiency, processing up to 150 tokens/second — 3× faster than comparable models, while maintaining strong performance across general tasks.
> Reka Lightning (Reka AI) — The Rising Star
A relatively new entrant gaining rapid adoption due to its innovative architecture and performance-to-resource ratio, particularly effective for creative writing and storytelling.
> Cohere Command (Cohere) — The Enterprise Solution
Optimized for business applications with 104B parameters and a 128K context window, excelling at structured outputs and document analysis with a focus on enterprise data privacy.
> BloombergGPT (Bloomberg) — The Financial Expert
A 50B parameter model specialized for finance that outperforms general models on financial analysis, market data interpretation, and SEC filing summarization by significant margins.
These models offer greater freedom and specialization but demand more expertise to deploy effectively. Their domain-specific prowess often makes them superior choices for targeted applications compared to more general models.
The Nine Rings: Models for Edge
The Nine Rings granted to mortal men offered specific powers but with limitations. Similarly, these lightweight models bring AI capabilities to the edge, though with reduced overall power:
- LLaMA-2 7B/13B (Meta) — It can run on smartphones through Qualcomm’s optimization.
- Vicuna-13B (LMSYS) — It has achieved 90% of ChatGPT quality on a fraction of the resources.
- Mistral-7B (Mistral AI) — State-of-the-art quality for its size, Apache 2.0 licensed.
- Orca (Microsoft) — 13B parameters with reasoning abilities rivaling much larger models.
- Granite(IBM) — State of art decoder language models spanning multiple modalities, which can be taken forward with Instruct Lab (iLAB) for knowledge & model customizations.
- Vicuna (LMSYS) — Edge infrastructure community favorite for local deployments.
- Qwen (Alibaba) — Efficient on-device processing for Asian languages.
- QwQ (Alibaba) — Compact mathematical assistant.
- GPT-3.5-Turbo (OpenAI) — The accessible via commercial (as a service) option.
These models excel in bringing AI capabilities directly to users’ devices, enabling:
- Privacy-preserving inference without sending data to cloud servers.
- Offline operation in environments with limited connectivity.
- Ultra-low latency responses for time-sensitive applications.
- Cost-effective deployment across large numbers of devices.
The tradeoff? Reduced capabilities in handling complex reasoning, shorter context windows, and more limited knowledge compared to their larger counterparts.
The LLR Pyramid
To maximize the potential of these AI Rings, organizations should consider a Latency-Reliability-Redundancy (LLR) pyramid approach:
Top Tier (Central-DC) — Barad-dûr: Tower of Supreme Power
Place The One Ring and Three Rings models in central data centers where computing resources are abundant. These handle the most complex queries requiring maximum reasoning power and large context windows: The true “Eye” of AI, guiding and overseeing the flow of data with unmatched computational might.
Middle Tier (Regional-DC) — Rivendell — Sanctuary of Balanced Wisdom
Deploy The Seven Rings models at regional facilities to balance performance with lower latency. This tier processes moderately complex tasks without the roundtrip time to central data centers. Like Elrond’s haven, Rivendell is a strategic refuge where insights are processed swiftly yet thoughtfully. It bridges the speed of the edges with the depth of the central core, ensuring reliable decisions closer to where they’re needed.
Bottom Tier (Edge) — The Shire (Edge-DC) — Realm of Everyday Brilliance
Utilize The Nine Rings models directly on end-user devices for specialized tasks where resource constraints are significant but immediacy is critical. These handle common queries, pre-processing, and serve as fallbacks during connectivity issues.
This tiered approach creates a resilient AI infrastructure that intelligently routes queries based on complexity, latency requirements, and available resources.
Just as hobbits quietly shape the fate of Middle-earth, these models handle everyday queries, pre-processing, and fallback logic when connectivity is limited. Their strength lies in speed, locality, and quiet resilience — proving that even the smallest nodes can play a vital role in a greater quest.
Choose Your Ring Wisely
Every organization must determine which of these AI Rings best aligns with their goals, resources, and values. The most powerful models aren’t always the right choice — sometimes a specialized model at the edge will outperform a general-purpose giant for specific tasks.
Key considerations when selecting AI models:
- Strategic alignment: Does the model’s capability match your organization’s core needs?
- Deployment flexibility: Can you run the model where it delivers the most value?
- Resource requirements: Do you have the infrastructure to support the model?
- Data governance: What happens to your data when using the model?
- Cost structure: Does the pricing align with your expected usage patterns?
- Specialization needs: Would a domain-specific model deliver better results?
- Latency and Throughput: Does the model perform and tolerate business needs?
Remember: A great AI power comes with great responsibility & sacrifice. Choose your ring wisely.
