AI-Hurd The thought of Non Locality & Data Security in the fast world of research By Rupert S
"Still handled by the local LLM, If you want credit!"
https://www.amd.com/en/developer/resources/technical-articles/2025/minions--on-device-and-cloud-language-model-collaboration-on-ryz.html
What is Minions?
"Minions is an agentic framework developed by the Hazy Research Group at Stanford University, which enables the collaboration between frontier models running in the datacenter and smaller models running locally on an AI PC. Now you might ask: if a remote frontier model is still involved, how does this reduce the cost? The answer is in how the Minions framework is architected. Minions is designed to minimize the number of input and output tokens processed by the frontier model. Instead of handling the entire task, the frontier model breaks down the requested task into a set of smaller subtasks, which are then executed by the local model. The frontier model doesn’t even see the full context of the user’s problem, which can easily be thousands, or even millions of tokens, especially when considering file-based inputs, common in a number of today’s applications such as coding and data analysis.
This interactive protocol, where the frontier model delegates work to the local model, is referred to as the “Minion” protocol in the Minions framework. The Minion protocol can reduce costs significantly but struggles to retain accuracy in tasks that require long context-lengths or complex reasoning on the local model. The “Minions” protocol is an updated protocol with more sophisticated communication between remote (frontier) and local agents through decomposing the task into smaller tasks across chunks of inputs. This enhancement reduces the context length required by the local model, resulting in accuracy much closer to that of the frontier model.
Figure 1 illustrates the tradeoff between accuracy and cost. Without Minions, developers are typically limited to two distinct options: local models that are cost-efficient but less accurate (bottom-left) and remote frontier models that offer high accuracy at a higher cost (top-right). Minions allows users to traverse the pareto frontier of accuracy and cost by allowing a remote and local model to collaborate with one another. In other words, Minions enables smarter tradeoffs between performance and cost, avoiding the extremes of all-local or all-remote models.
Please refer to the paper, “Cost-efficient Collaboration Between On-device and Cloud Language Models” for more information on the Minions framework and results."
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Non Locality & minions the offsite AI model & how it applies to us : RS 2025
For future reference, Minions can be referred to in 2 easy ways:
Cattle Herd, Or Herd is where a cow or an elephant asks the herd to help it for-fill a task, In most herd situations where a clever being such as an elephant can ask the herd for help,.. They do!
What an elephant does is ask the herd to help it gather food when it finds some.. It shares!
You know that web searching large numbers of pages by yourself is a futile effort for personal task management,.. When the pages ask if you are human!
'No I am not human... I am a researcher! or a news reporter! lol" #BearlyHumanCyborg #AnimatedCamera #InfoWarrior
So the main point is that Frontier Type non local devices can horde data, Large personal hordes of data are unlikely in most cases and localized research by your machine .. If it does page scanning can invoke hostility...
Large medical datasets, Large chemical lists, Order history for business, Costs & accounting...
All large dataset lists are procedurally called to do the majority of work on the cloud,
Local service can power the requests you desire to make..
The researcher sits in his library & researches any topic for the free research topic at 6th form & higher education & If they are trying for a good grade they quickly find themselves ordering a book,..
So there are many herd tactics,..
Ranging from wolves & ants working together, To cows & farmers,..
Still handled by the local LLM, If you want credit!
herd tactics appear basic & usually involve localised sharing,.. The most common one in computing for universities & business,.. Is a cluster of computers,..
Cloud dynamics is a complex variable setting, You start with a single client,..
You begin with a local cluster of computers & data (library & local ethernet / WiFi),
You have non expert advice,.. Social Media for the humans to involve themselves in,..
Still handled by the local LLM, You have offsite references,.. cloud libraries & data,..
You can process the downloaded dataset, yourself,.. If you want credit for your work,..
You can share the credit with your co-workers,.. By asking them to help,.. Usually the local mainframe / Network is happy to say who is doing the research,..
Finally,.. You can have the work done by offsite resources,..
Professional, Legal, Medical, Science, Advice,..
If you want credit for thinking,.. Try yourself first!
Minions for 'Real MEN'
Rupert S
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Practical Applications and Workflow
This hybrid model applies to numerous real-world scenarios:
Field
Local Model Task (The "Herd")
Remote Model Task (The "Elephant")
Document Analysis
Scans gigabytes of local logs, files, or code.
Receives small snippets or summaries to perform high-level analysis or answer complex queries.
Medical Research
Processes sensitive patient records on a secure local machine.
Receives anonymized, distilled sub-inquiries for advanced interpretation or to cross-reference with global research.
Business & Finance
Parses daily transactions and manages accounting data locally.
Is called upon to identify strategic anomalies or generate high-level financial insights from summarized reports.
Academic Research
Scans and indexes a personal library of research papers and drafts.
Helps refine a hypothesis, check citations against a vast external database, or suggest new research directions.
RS
*
What Is Non-Locality in AI?
Non-locality refers to offloading computation to cloud-hosted AI services.
Remote frontier models deliver advanced reasoning and large-context handling at the cost of higher latency, data transfer, and per-token fees.
Local on-device models offer privacy and low inference cost but struggle with very long contexts or deep reasoning.
Without a hybrid approach, developers must choose either low-cost/low-accuracy local inference or high-cost/high-accuracy cloud inference.
The Minions Framework
Minion Protocol
The frontier model ingests the full request.
It breaks the job into smaller subtasks.
It sends those subtasks (with minimal context) to the local model.
Enhanced Minions Protocol
Inputs are chunked into manageable pieces.
Remote and local agents exchange richer messages about each chunk.
Accuracy approaches that of the frontier model with far fewer remote tokens.
Together, these steps let developers traverse the Pareto frontier of cost versus accuracy, avoiding the extremes of all-local or all-remote solutions.
Herd Tactics: Metaphors for Collaboration
Minions draws on classic examples of cooperative task-sharing in nature and agriculture:
Elephant and herd An elephant (frontier model) spots resources and delegates gathering to its herd (local models) without revealing the entire map.
Wolves and ants Wolves (cloud) scout and plan routes; ants (device) undertake localized gathering in parallel.
Cows and farmers Farmers (remote) plan the harvest; cows (local) graze as directed and report back in small updates.
The researcher sits in his library & researches any topic for the free research topic at 6th form & higher education & If they are trying for a good grade they quickly find themselves ordering a book,..
So there are many herd tactics,..
Ranging from wolves & ants working together, To cows & farmers,..
Still handled by the local LLM, If you want credit!
herd tactics appear basic & usually involve localised sharing,.. The most common one in computing for universities & business,.. Is a cluster of computers,..
Cloud dynamics is a complex variable setting, You start with a single client,..
You begin with a local cluster of computers & data (library & local ethernet / WiFi),
You have non expert advice,.. Social Media for the humans to involve themselves in,..
Still handled by the local LLM, You have offsite references,.. cloud libraries & data,..
You can process the downloaded dataset, yourself,.. If you want credit for your work,..
You can share the credit with your co-workers,.. By asking them to help,.. Usually the local mainframe / Network is happy to say who is doing the research,..
Finally,.. You can have the work done by offsite resources,..
Professional, Legal, Medical, Science, Advice,..
If you want credit for thinking,.. Try yourself first!
Minions for 'Real MEN'
Rupert S
*
Practical Applications and Workflow
This hybrid model applies to numerous real-world scenarios:
Field
Local Model Task (The "Herd")
Remote Model Task (The "Elephant")
Document Analysis
Scans gigabytes of local logs, files, or code.
Receives small snippets or summaries to perform high-level analysis or answer complex queries.
Medical Research
Processes sensitive patient records on a secure local machine.
Receives anonymized, distilled sub-inquiries for advanced interpretation or to cross-reference with global research.
Business & Finance
Parses daily transactions and manages accounting data locally.
Is called upon to identify strategic anomalies or generate high-level financial insights from summarized reports.
Academic Research
Scans and indexes a personal library of research papers and drafts.
Helps refine a hypothesis, check citations against a vast external database, or suggest new research directions.
RS
*
What Is Non-Locality in AI?
Non-locality refers to offloading computation to cloud-hosted AI services.
Remote frontier models deliver advanced reasoning and large-context handling at the cost of higher latency, data transfer, and per-token fees.
Local on-device models offer privacy and low inference cost but struggle with very long contexts or deep reasoning.
Without a hybrid approach, developers must choose either low-cost/low-accuracy local inference or high-cost/high-accuracy cloud inference.
The Minions Framework
Minion Protocol
The frontier model ingests the full request.
It breaks the job into smaller subtasks.
It sends those subtasks (with minimal context) to the local model.
Enhanced Minions Protocol
Inputs are chunked into manageable pieces.
Remote and local agents exchange richer messages about each chunk.
Accuracy approaches that of the frontier model with far fewer remote tokens.
Together, these steps let developers traverse the Pareto frontier of cost versus accuracy, avoiding the extremes of all-local or all-remote solutions.
Herd Tactics: Metaphors for Collaboration
Minions draws on classic examples of cooperative task-sharing in nature and agriculture:
Elephant and herd An elephant (frontier model) spots resources and delegates gathering to its herd (local models) without revealing the entire map.
Wolves and ants Wolves (cloud) scout and plan routes; ants (device) undertake localized gathering in parallel.
Cows and farmers Farmers (remote) plan the harvest; cows (local) graze as directed and report back in small updates.
Ant's localise farming & nutrient gathering & health & defence & other complex activities..
These metaphors illustrate delegation, chunked work, and minimal context exposure.
Workflow & Attribution
Local first “Still handled by the local LLM, if you want credit!” encourages you to solve subtasks on your device before invoking the frontier model.
Cluster & Cloud Dynamics
Build a local compute cluster (library, LAN/WiFi).
Connect to offsite data repositories (cloud libraries).
Delegate only the most complex or large-scale tasks to the frontier model.
Attribution When the local LLM completes subtasks, you retain full “thinking credit.” Only edge-case reasoning is handled remotely.
Embracing Hybrid AI
By adopting Minions, you achieve significant cost reductions without sacrificing accuracy. Privacy improves as full data contexts need not leave your device.
The resulting pipeline scales from coding and data analysis to domain-specific research, letting your AI “herd” work in concert across local and non-local realms.
Further Exploration
Experiment with chunk sizes and communication frequency to find your ideal cost/accuracy balance.
Combine Minions with retrieval-augmented generation for even larger knowledge bases.
Explore analogies from swarm intelligence (e.g., bees, starlings) to inspire novel delegation strategies.
Investigate on-device fine-tuning to boost local model capabilities before delegation.
RS
*
Non-Locality & Minions: The Offsite AI Model and How It Applies to Us (RS 2025)
Understanding how to blend remote “frontier” models with on-device inference is key to balancing cost, performance, and privacy.
The Minions framework offers a concrete blueprint.
What Is Non-Locality in AI?
Non-locality refers to leveraging AI services hosted offsite—typically in cloud datacenters—to perform heavy inference tasks.
Remote models (like GPT-4 or Claude) excel at complex reasoning and large-context understanding but incur high per-token costs and data-transfer latency.
Local models run on AI PCs (with NPUs/accelerators) reduce costs and keep data private but may struggle with very long contexts or intricate reasoning.
Without a bridge, developers must choose either low-cost/low-accuracy local inference or high-cost/high-accuracy cloud inference.
The Minions Framework
Minions is an agentic collaboration system co-developed by Stanford’s Hazy Research Group and AMD that orchestrates work between a remote “frontier” model and a local LLM.
The Minion protocol:
The frontier model receives the full user request.
It decomposes the task into smaller subtasks.
It sends only these subtasks (and minimal context) to the local model for execution.
The enhanced Minions protocol further:
Chunks huge inputs into manageable segments.
Uses richer exchanges between agents.
Yields accuracy near frontier levels while slashing remote-model token usage.
Together, these steps let you traverse the Pareto frontier of cost versus accuracy—no longer an either/or decision.
Herding Agents: Metaphors for Collaboration
Drawing from classic “herd” (herd) tactics and nature’s teamwork, Minions mimics cooperative strategies:
Elephant & herd An elephant (large model) that spots distant food delegates gathering to its herd (local LLM) without sharing its full map—maximizing efficiency and privacy.
Wolves & Ants Wolves (frontier) scout and plan routes; ants (local) execute localized gathering in parallel.
Cows & Farmers Farmers (remote) plan harvests; cows (device) graze where directed, feeding back yields in small reports.
These examples highlight delegation, chunked work, and minimal context sharing.
Applying Minions to Real-World Workloads
Large Document Analysis
Local LLM scans gigabytes of logs or code.
Frontier model issues targeted queries or summaries.
Medical & Scientific Datasets
Sensitive records stay on-device.
Only distilled sub-inquiries go to the cloud for complex interpretation.
Business & Accounting
Local cluster manages daily transaction parsing.
Frontier model validates anomalies or generates strategic insights.
Research & Education
Student’s PC handles literature scanning.
Frontier model refines hypotheses or checks citations—saving bandwidth and preserving drafts.
Workflow & Credit
Local First “Still handled by the local LLM, if you want credit!” encourages you to attempt solutions on your device before outsourcing—emulating a researcher’s rigor.
Cluster & Cloud Dynamics
Spin up a local cluster (library, LAN/WiFi).
Integrate offsite data repositories (cloud libraries).
Delegate only complex reasoning or very large-scale tasks to remote agents.
Attribution When the local model solves subtasks, you retain full “thinking credit.” Only edge cases invoke the frontier.
Minions for “Real MEN”
By adopting Minions, you gain:
Significant cost reductions without sacrificing accuracy.
Enhanced data privacy by minimizing context exposure.
A flexible, scalable pipeline suited for coding, analysis, and domain-specific research.
Embrace the herd, delegate with precision, and let your AI flock thrive across local and non-local realms.
RS
These metaphors illustrate delegation, chunked work, and minimal context exposure.
Workflow & Attribution
Local first “Still handled by the local LLM, if you want credit!” encourages you to solve subtasks on your device before invoking the frontier model.
Cluster & Cloud Dynamics
Build a local compute cluster (library, LAN/WiFi).
Connect to offsite data repositories (cloud libraries).
Delegate only the most complex or large-scale tasks to the frontier model.
Attribution When the local LLM completes subtasks, you retain full “thinking credit.” Only edge-case reasoning is handled remotely.
Embracing Hybrid AI
By adopting Minions, you achieve significant cost reductions without sacrificing accuracy. Privacy improves as full data contexts need not leave your device.
The resulting pipeline scales from coding and data analysis to domain-specific research, letting your AI “herd” work in concert across local and non-local realms.
Further Exploration
Experiment with chunk sizes and communication frequency to find your ideal cost/accuracy balance.
Combine Minions with retrieval-augmented generation for even larger knowledge bases.
Explore analogies from swarm intelligence (e.g., bees, starlings) to inspire novel delegation strategies.
Investigate on-device fine-tuning to boost local model capabilities before delegation.
RS
*
Non-Locality & Minions: The Offsite AI Model and How It Applies to Us (RS 2025)
Understanding how to blend remote “frontier” models with on-device inference is key to balancing cost, performance, and privacy.
The Minions framework offers a concrete blueprint.
What Is Non-Locality in AI?
Non-locality refers to leveraging AI services hosted offsite—typically in cloud datacenters—to perform heavy inference tasks.
Remote models (like GPT-4 or Claude) excel at complex reasoning and large-context understanding but incur high per-token costs and data-transfer latency.
Local models run on AI PCs (with NPUs/accelerators) reduce costs and keep data private but may struggle with very long contexts or intricate reasoning.
Without a bridge, developers must choose either low-cost/low-accuracy local inference or high-cost/high-accuracy cloud inference.
The Minions Framework
Minions is an agentic collaboration system co-developed by Stanford’s Hazy Research Group and AMD that orchestrates work between a remote “frontier” model and a local LLM.
The Minion protocol:
The frontier model receives the full user request.
It decomposes the task into smaller subtasks.
It sends only these subtasks (and minimal context) to the local model for execution.
The enhanced Minions protocol further:
Chunks huge inputs into manageable segments.
Uses richer exchanges between agents.
Yields accuracy near frontier levels while slashing remote-model token usage.
Together, these steps let you traverse the Pareto frontier of cost versus accuracy—no longer an either/or decision.
Herding Agents: Metaphors for Collaboration
Drawing from classic “herd” (herd) tactics and nature’s teamwork, Minions mimics cooperative strategies:
Elephant & herd An elephant (large model) that spots distant food delegates gathering to its herd (local LLM) without sharing its full map—maximizing efficiency and privacy.
Wolves & Ants Wolves (frontier) scout and plan routes; ants (local) execute localized gathering in parallel.
Cows & Farmers Farmers (remote) plan harvests; cows (device) graze where directed, feeding back yields in small reports.
These examples highlight delegation, chunked work, and minimal context sharing.
Applying Minions to Real-World Workloads
Large Document Analysis
Local LLM scans gigabytes of logs or code.
Frontier model issues targeted queries or summaries.
Medical & Scientific Datasets
Sensitive records stay on-device.
Only distilled sub-inquiries go to the cloud for complex interpretation.
Business & Accounting
Local cluster manages daily transaction parsing.
Frontier model validates anomalies or generates strategic insights.
Research & Education
Student’s PC handles literature scanning.
Frontier model refines hypotheses or checks citations—saving bandwidth and preserving drafts.
Workflow & Credit
Local First “Still handled by the local LLM, if you want credit!” encourages you to attempt solutions on your device before outsourcing—emulating a researcher’s rigor.
Cluster & Cloud Dynamics
Spin up a local cluster (library, LAN/WiFi).
Integrate offsite data repositories (cloud libraries).
Delegate only complex reasoning or very large-scale tasks to remote agents.
Attribution When the local model solves subtasks, you retain full “thinking credit.” Only edge cases invoke the frontier.
Minions for “Real MEN”
By adopting Minions, you gain:
Significant cost reductions without sacrificing accuracy.
Enhanced data privacy by minimizing context exposure.
A flexible, scalable pipeline suited for coding, analysis, and domain-specific research.
Embrace the herd, delegate with precision, and let your AI flock thrive across local and non-local realms.
RS
*
Minions? Overview from our view
Minions is an agentic framework co-developed by Stanford’s Hazy Research Group and AMD that..
Enables,.. Seamless collaboration between large, cloud-hosted “frontier” models and smaller, on-device language models,..
By splitting work into targeted subtasks, it minimizes the data and tokens sent offsite while preserving near-frontier accuracy.
Key Principles
Frontier model acts as the manager, ingesting the full user request and planning the overall approach.
Local model acts as the executor, processing distilled subtasks entirely on the user’s device.
Only minimal context and subtask definitions travel to the frontier, shriveling per-token costs and data exposure.
Iterative exchanges ensure that complex or large inputs are chunked into bite-sized pieces for on-device handling.
Protocol Variants
Minion Protocol
Frontier breaks down a task and sends subtasks to the local model along with just enough context.
Enhanced Minions Protocol
Inputs are pre-chunked.
Frontier and local agents trade richer metadata about each piece.
Accuracy climbs toward frontier-only levels with a fraction of the token spend.
How It Works
User submits a large or complex request.
Frontier model analyzes and decomposes it into subtasks.
Local model receives each subtask plus minimal context and runs inference on-device.
Results flow back to the frontier for any final synthesis or complex reasoning.
Frontier returns the polished answer to the user.
Benefits
Significant reduction in cloud-compute costs.
Enhanced privacy since full data never leaves the device.
Scalability across contexts—from gigabyte-scale logs to multi-document legal briefs.
Flexibility: you traverse the cost vs. accuracy Pareto frontier rather than choosing one extreme.
Ideal Use Cases
Document Analysis: On-device scanning of large codebases or logs; frontier handles pinpointed queries.
Medical & Scientific Research: Sensitive data remains local; complex interpretations invoke the cloud.
Finance & Accounting: Daily transaction parsing locally; anomaly detection and strategy come from the frontier.
Academic Research: Local indexing of papers; hypothesis refinement and citation checks outsourced smartly.
RS
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Explanation of the "Non-Locality & Minions" concept.
The "Minions" framework is a collaborative AI model that intelligently divides tasks between a powerful, remote "frontier" AI and a smaller, efficient AI running locally on your device.
This hybrid approach, which you've termed "Non-Locality," aims to balance performance, cost, and privacy by delegating work in a manner similar to natural "herd tactics."
The Core Concept: AI Collaboration
At its heart, the Minions framework, developed by Stanford's Hazy Research Group, addresses a fundamental trade-off in AI:
Remote "Frontier" Models: These are extremely powerful models (like GPT-4) running in cloud datacenters.
They offer high accuracy and complex reasoning but come with significant costs, latency, and privacy concerns since your data must be sent offsite.
Local "On-Device" Models: These run directly on an AI PC, offering low cost, high speed, and complete data privacy..
However, they are less powerful and may struggle with tasks requiring vast context or intricate reasoning.
The Minions framework creates a bridge between these two extremes.
Instead of processing an entire task remotely, the frontier model acts as a manager..
The Minions framework creates a bridge between these two extremes.
Instead of processing an entire task remotely, the frontier model acts as a manager..
It analyses the user's request, breaks it down into smaller, simpler subtasks, and sends only these subtasks—with minimal necessary context—to the local AI for execution.
"Herd Tactics": An Analogy
The "herd tactics" metaphor provides an intuitive way to understand this process.
The Elephant and the Herd: A large, intelligent model (the "elephant") identifies a broad goal (like finding a food source)..
"Herd Tactics": An Analogy
The "herd tactics" metaphor provides an intuitive way to understand this process.
The Elephant and the Herd: A large, intelligent model (the "elephant") identifies a broad goal (like finding a food source)..
It then delegates the actual work of gathering to the local models (the "herd") without needing to share its entire map or knowledge base.
Delegation and Efficiency: Just as wolves might scout a path for the pack to follow, the frontier model does the high-level planning, while the local models handle the on-the-ground execution.
This minimizes data transfer and leverages the strengths of each component.
This approach is designed to reduce the cost and privacy risks of using large models,..
The remote AI never sees the full, sensitive dataset (be it medical records, proprietary code, or financial data).
Practical Applications and Workflow
This hybrid model applies to numerous real-world scenarios:
Field
Local Model Task (The "Herd")
Remote Model Task (The "Elephant")
Document Analysis
Scans gigabytes of local logs, files, or code.
Receives small snippets or summaries to perform high-level analysis or answer complex queries.
Medical Research
Processes sensitive patient records on a secure local machine.
Receives anonymized, distilled sub-inquiries for advanced interpretation or to cross-reference with global research.
Business & Finance
Parses daily transactions and manages accounting data locally.
Is called upon to identify strategic anomalies or generate high-level financial insights from summarized reports.
Academic Research
Scans and indexes a personal library of research papers and drafts.
Helps refine a hypothesis, check citations against a vast external database, or suggest new research directions.
RS
Delegation and Efficiency: Just as wolves might scout a path for the pack to follow, the frontier model does the high-level planning, while the local models handle the on-the-ground execution.
This minimizes data transfer and leverages the strengths of each component.
This approach is designed to reduce the cost and privacy risks of using large models,..
The remote AI never sees the full, sensitive dataset (be it medical records, proprietary code, or financial data).
Practical Applications and Workflow
This hybrid model applies to numerous real-world scenarios:
Field
Local Model Task (The "Herd")
Remote Model Task (The "Elephant")
Document Analysis
Scans gigabytes of local logs, files, or code.
Receives small snippets or summaries to perform high-level analysis or answer complex queries.
Medical Research
Processes sensitive patient records on a secure local machine.
Receives anonymized, distilled sub-inquiries for advanced interpretation or to cross-reference with global research.
Business & Finance
Parses daily transactions and manages accounting data locally.
Is called upon to identify strategic anomalies or generate high-level financial insights from summarized reports.
Academic Research
Scans and indexes a personal library of research papers and drafts.
Helps refine a hypothesis, check citations against a vast external database, or suggest new research directions.
RS
*
Deep Dive into the Minions Framework
1. The Core Trade-Off
Every AI deployment faces a three-way tug-of-war between cost, performance, and privacy:
Cloud “frontier” models (e.g. GPT-4):
Pros: Best reasoning, huge context windows
Cons: High per-token fees, latency, full-data exposure
On-device LLMs (e.g. 7–13B parameter models on NPUs):
Pros: Low cost, instant response, data never leaves your machine
Cons: Limited context, weaker at multi-step reasoning
Minions bridges this gap by letting the frontier model orchestrate and delegate chunks of work to your local LLM,..
So you pay for— and expose to the cloud,.. Only those minimal snippets that truly need a powerhouse brain.
2. How Minions Orchestrates Work
Frontier as Task Manager
Ingests the entire user request.
Breaks it into subtasks: data cleaning, summarization, targeted Q&A.
Local LLM as Executor
Receives each distilled subtask + minimal context.
Processes it entirely on-device.
Returns results to the frontier for any final synthesis.
Iterative Refinement
For very large inputs, both agents trade richer messages—but still only what’s needed.
Accuracy climbs close to frontier-only levels, yet token spend plummets.
3. Nature’s “Herd” Tactics in AI
Minions didn’t borrow metaphors by accident, they mirror efficient, privacy-preserving collaboration found in ecosystems:
Beginning conception:
Elephant & Herd
Elephant (frontier) spots the goal, sends the herd (locals) off without sharing its full map.
Wolves & Ants
Wolves (frontier) chart the route; ants (locals) do the parallel grunt work.
Farmers & Cows
Farmers (remote) plan the harvest; cows (device) graze where directed, reporting yields in tiny batches.
4. Precision & Bit-Depth Considerations
When running local LLMs, model weight precision (4-, 8-, 16-bit) dramatically influences speed, memory, and fidelity:
4-bit Quantization:
Pros: Tiny footprint, ultra-fast inference
Cons: May lose nuance in complex reasoning
8-bit Quantization:
Sweet spot for many applications, balancing size and accuracy
16-bit / FP16:
Nearly full-precision, heavier but excels on tasks needing fine detail
Tuning your local hardware (NPUs/TPUs, memory bandwidth, on-chip caches) around these bit-depths can further push cost and latency toward zero.
6. Beyond Minions: Next Steps & Open Questions
Network Design: How do you architect LAN/WiFi or RDMA links to guarantee sub-100 ms hops?
Security Layers: Can you incorporate TPM-backed enclaves or JIT-verified code to harden the local agent?
Adaptive Delegation: What heuristics decide “local vs. remote”? Real-time performance profiling?
Model Evolution: As frontier models grow, can your local “herd” dynamically upgrade via federated distillation?
Embracing Minions means you no longer cross your fingers hoping an all-cloud or all-local solution suffices..
You choreograph a team that’s cost-smart, fast, and respects your data’s privacy
Rupert S
Deep Dive into the Minions Framework
1. The Core Trade-Off
Every AI deployment faces a three-way tug-of-war between cost, performance, and privacy:
Cloud “frontier” models (e.g. GPT-4):
Pros: Best reasoning, huge context windows
Cons: High per-token fees, latency, full-data exposure
On-device LLMs (e.g. 7–13B parameter models on NPUs):
Pros: Low cost, instant response, data never leaves your machine
Cons: Limited context, weaker at multi-step reasoning
Minions bridges this gap by letting the frontier model orchestrate and delegate chunks of work to your local LLM,..
So you pay for— and expose to the cloud,.. Only those minimal snippets that truly need a powerhouse brain.
2. How Minions Orchestrates Work
Frontier as Task Manager
Ingests the entire user request.
Breaks it into subtasks: data cleaning, summarization, targeted Q&A.
Local LLM as Executor
Receives each distilled subtask + minimal context.
Processes it entirely on-device.
Returns results to the frontier for any final synthesis.
Iterative Refinement
For very large inputs, both agents trade richer messages—but still only what’s needed.
Accuracy climbs close to frontier-only levels, yet token spend plummets.
3. Nature’s “Herd” Tactics in AI
Minions didn’t borrow metaphors by accident, they mirror efficient, privacy-preserving collaboration found in ecosystems:
Beginning conception:
Elephant & Herd
Elephant (frontier) spots the goal, sends the herd (locals) off without sharing its full map.
Wolves & Ants
Wolves (frontier) chart the route; ants (locals) do the parallel grunt work.
Farmers & Cows
Farmers (remote) plan the harvest; cows (device) graze where directed, reporting yields in tiny batches.
4. Precision & Bit-Depth Considerations
When running local LLMs, model weight precision (4-, 8-, 16-bit) dramatically influences speed, memory, and fidelity:
4-bit Quantization:
Pros: Tiny footprint, ultra-fast inference
Cons: May lose nuance in complex reasoning
8-bit Quantization:
Sweet spot for many applications, balancing size and accuracy
16-bit / FP16:
Nearly full-precision, heavier but excels on tasks needing fine detail
Tuning your local hardware (NPUs/TPUs, memory bandwidth, on-chip caches) around these bit-depths can further push cost and latency toward zero.
6. Beyond Minions: Next Steps & Open Questions
Network Design: How do you architect LAN/WiFi or RDMA links to guarantee sub-100 ms hops?
Security Layers: Can you incorporate TPM-backed enclaves or JIT-verified code to harden the local agent?
Adaptive Delegation: What heuristics decide “local vs. remote”? Real-time performance profiling?
Model Evolution: As frontier models grow, can your local “herd” dynamically upgrade via federated distillation?
Embracing Minions means you no longer cross your fingers hoping an all-cloud or all-local solution suffices..
You choreograph a team that’s cost-smart, fast, and respects your data’s privacy
Rupert S
*****
Dual Blend & DSC low Latency Connection Proposal - texture compression formats available (c)RS
https://is.gd/TV_GPU25_6D4
Reference
https://is.gd/SVG_DualBlend https://is.gd/MediaSecurity https://is.gd/JIT_RDMA
https://is.gd/PackedBit https://is.gd/BayerDitherPackBitDOT
https://is.gd/QuantizedFRC https://is.gd/BlendModes https://is.gd/TPM_VM_Sec
https://is.gd/IntegerMathsML https://is.gd/ML_Opt https://is.gd/OPC_ML_Opt https://is.gd/OPC_ML_QuBit https://is.gd/QuBit_GPU https://is.gd/NUMA_Thread
On the subject of how deep a personality of 4Bit, 8Bit, 16Bit is reference:
https://science.n-helix.com/2021/03/brain-bit-precision-int32-fp32-int16.html
https://science.n-helix.com/2022/10/ml.html
