Technical Collaboration

Building LATAM's Technical AI Future

Technical collaboration with Jhenner Tigreros on CUDA optimization and AI infrastructure development from Colombia, demonstrating that world-class projects are viable in LATAM with committed technical teams.

active Research
CUDAGPU ComputingLATAM AIDeepSeek
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Key Highlights

Building LATAM’s Technical AI Future

LATAM AI collaboration map

At SIMG, we’re committed to demonstrating that world-class AI projects are not only possible, but economically viable from Latin America. Our collaboration with Jhenner Tigreros, a technical expert in CUDA and GPU computing from Colombia, exemplifies this commitment to regional technical excellence.

The Reality of Costs: DeepSeek-R1

In a recent update to the DeepSeek-R1 paper, the time and money spent on complete training was finally confirmed:

Real Cost Analysis

The truth: A trivial amount compared to Silicon Valley projects that typically exceed $100M USD.

Our Response

With these numbers, it’s clear that quality and valuable projects can be developed from LATAM.

With technical teams from universities willing to act not from the ego of being first, but from the excitement of doing it right from the beginning as a region.

Never stop learning 🚀

Collaboration with Jhenner Tigreros

Technical Focus

Our collaboration with Jhenner focuses on critical areas of AI infrastructure:

1. CUDA Optimization

2. Low-Level Architecture

3. Algorithmic Efficiency

Why It Matters

Jhenner’s expertise in CUDA and GPU computing represents the type of deep technical knowledge LATAM needs to develop:

The LATAM Case

Why Now?

The DeepSeek-R1 numbers validate what we’ve been saying:

  1. 💡 It’s not just about scale: Algorithmic efficiency and mathematical rigor surpass simple “more compute”
  2. 🎓 We have the talent: Latin American universities produce world-class researchers and engineers
  3. 💰 It’s financially viable: $6M USD is achievable with mixed funding (academia + industry + government)
  4. 🌎 There’s regional demand: Models adapted to Latin American context have unique value

LATAM’s Competitive Advantages

Technical Talent

Operational Costs

Cultural Context

Vision: LATAM Technical Ecosystem

2026-2027 Goals

  1. 🔬 Demonstrative Projects

    • Train SOTA model in specific domain with limited budget
    • Publish benchmarks and open-source code
    • Document process and real costs

  2. 👥 Community Building

    • Form network of CUDA and GPU computing experts
    • Organize technical workshops and hackathons
    • Create educational resources in Spanish

  3. 🏗️ Shared Infrastructure

    • Negotiate access to compute resources for research
    • Develop open-source tools for optimization
    • Establish regional standards and best practices

  4. 🤝 Strategic Collaborations

    • Connect academia with local tech industry
    • Seek funding from foundations and government
    • Participate in international initiatives from LATAM

Learnings from DeepSeek-R1 Paper

Technical Lessons

# Efficiency principles inspired by DeepSeek
class LatamAIPhilosophy:
    """AI development philosophy from LATAM"""
    
    principles = {
        "efficiency_first": "Algorithmic optimization > brute scale",
        "mathematical_rigor": "Solid mathematical foundations",
        "open_collaboration": "Share knowledge and code",
        "pragmatic_innovation": "Practical and deployable solutions",
        "regional_focus": "Build for local context first"
    }
    
    @staticmethod
    def estimate_project_viability(budget_usd, team_size, timeline_months):
        """
        Estimate AI project viability in LATAM
        
        DeepSeek-R1 as baseline:
        - $294K training
        - ~$6M total (with team)
        - ~12 months
        """
        training_costs = budget_usd * 0.05  # ~5% on compute
        team_costs = budget_usd * 0.95      # ~95% on talent
        
        feasibility = {
            "viable": budget_usd >= 500_000,  # Conservative threshold
            "competitive": budget_usd >= 3_000_000,
            "world_class": budget_usd >= 6_000_000,
            "message": "LATAM can compete with adequate teams"
        }
        
        return feasibility

Success Factors

  1. Elite Technical Team: Prioritize expertise over quantity
  2. Clear Focus: Specific domain vs. general purpose
  3. Efficient Infrastructure: Leverage cloud and optimization
  4. Rapid Iteration: Culture of experimentation and learning
  5. Documentation: Share process so others can replicate

Call to Action

For Researchers

For Universities

For Industry and Government

Conclusion

The DeepSeek-R1 numbers aren’t just a technical curiosity - they’re proof of concept that LATAM can and should participate in the frontier of AI research.

Not as followers, but as builders and innovators who understand that technical excellence doesn’t require budgets in the hundreds of millions.

It requires:

The future of AI in LATAM is built today, with quality technical work and regional collaboration.

Contact

Want to collaborate on this effort?

Let’s keep building the future of AI from LATAM 🚀🌎

Resources

📝

Jhenner Tigreros Article

Technical analysis on real costs of AI model development
💻

DeepSeek-R1 Paper

Updated paper with confirmed training costs

Team & Collaborators

Researchers

  • SIMG Research Group
  • Jhenner Tigreros

Collaborators

Jhenner Tigreros

CUDA Expert - Colombia

SIMG Research Group

Universidad Nacional de Colombia

Supported by

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