Advanced Compute Strategy Modeling with LlamaIndex
Create a decision support agent using LlamaIndex to model compute infrastructure strategies for AI developers. Inspired by the reported chip supply ambitions of major labs, this agent will evaluate compute procurement options. By utilizing Fireworks AI for model inference and Edge Impulse for performance simulation, the agent will analyze hypothetical supply chain risks and compute scalability, providing data-driven recommendations without relying on internal document databases.
What you are building
The core problem, expected build, and operating context for this challenge.
Create a decision support agent using LlamaIndex to model compute infrastructure strategies for AI developers. Inspired by the reported chip supply ambitions of major labs, this agent will evaluate compute procurement options. By utilizing Fireworks AI for model inference and Edge Impulse for performance simulation, the agent will analyze hypothetical supply chain risks and compute scalability, providing data-driven recommendations without relying on internal document databases.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
Feasibility Check
Ensure proposed strategy fits within input budget
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Resource Efficiency
Score of recommended strategy viability • target: 85 • range: 0-100
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Master LlamaIndex agentic abstractions for complex reasoning tasks
Configure Fireworks AI as an inference backend for model performance
Integrate Edge Impulse APIs for simulated inference latency calculation
Design multi-step planning agents using GPT-5.4 Pro and Claude Sonnet 4.6.6
Orchestrate comparative strategy modeling between compute hardware providers
Build feedback loops between model inference performance and business requirements
How this agent runs
Evaluate the agent's ability to propose optimal infrastructure configurations based on constraints.
Challenge input
Constraint map
Llama Index
Data framework for LLM
Yupp AI
LLM evaluation platform
Fireworks AI
Fast inference and fine-tuning platform.
Evaluated output
Infrastructure plan JSON
- Ensure proposed strategy fits within input budget
- Score of recommended strategy viability • target: 85 • range: 0-100
- Resource Efficiency target: 85
- 1 public reference case
- Python execution harness
View technical recipe
Configured tools
- Llama Index · Required
- Yupp AI · Optional
- Fireworks AI · Optional
- Fireworks AI · Optional
Evaluation contract
- Feasibility Check · Weight 1
- Resource Efficiency · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.
Run this agent on your dataset and AI stack
Bring your dataset, model providers, and success criteria. We will scope the right managed run for your team.
Scope a managed run[ok] Wrote CHALLENGE.md
[ok] Wrote .versalist.json
[ok] Wrote eval/examples.json
Requires VERSALIST_API_KEY. Works with any MCP-aware editor.
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