Choosing Open-source LLMs

Table of Contents

• Module 1 — Introduction to Open-source LLMs
  • 1.1 The Model Selection Challenge
  • 1.2 Why the Right Model Matters
  • 1.3 Open-source vs. Proprietary Models
  • 1.4 Model Families
• Module 2 — Evaluating Models for Performance and Usability
  • 2.1 The Three Pillars of Evaluation
  • 2.2 Understanding Specifications: Parameter Count
  • 2.3 Quantization: Making Large Models Accessible
  • 2.4 What Is Context Length?
  • 2.5 Architectural Considerations
  • 2.6 Decoding Benchmarks and Metrics
  • 2.7 Reality Check: Benchmarks vs. Real-world Performance
  • 2.8 Step 1: Define Your Requirements
  • 2.9 Step 2: Multi-tier Evaluation Approach
  • 2.10 Step 3: Total Cost of Ownership
  • 2.11 Running Your First Local LLM with Ollama
• Module 3 — Licenses and Practical Constraints
  • 3.1 Understanding Model Licenses
  • 3.2 License Red Flags to Watch For
  • 3.3 Practical Deployment Considerations
  • 3.4 Where to Go from Here?
• Appendix — Resources and References

Module 1 — Introduction to Open-source LLMs

1.1 The Model Selection Challenge

Choosing an open-source LLM (Large Language Model) can feel overwhelming. A startup founder captures the problem well:

“I’m looking at Llama models, various Mistral options, something called Falcon, and then all these parameter counts — 7B, 13B, 70B. One model requires massive compute power, another runs fine on standard machines but struggles with complex queries, and the third has licensing terms that are hard to understand.”

What is an LLM?

An AI system trained to understand and generate human-like text — a highly sophisticated autocomplete system capable of writing, analyzing, summarizing, and even coding.

Some popular models on the market:

1.2 Why the Right Model Matters

Illustrative scenario: An academic researcher analyzes historical literature and picks the most powerful model available — Historian-70B. Reality quickly catches up:

Minimum required configuration:
  - 8 × A100 GPUs
  - 140 GB VRAM

Available resources:
  - Intel MacBook Pro 2019
  - 16 GB RAM

Result: analyses take hours, the cloud budget is exhausted in two weeks. Yet a smaller model would have produced similar results for 19th-century poetry analysis.

Your model choice affects three key factors:

mindmap
  root((Model Choice))
    Performance
      Response quality
      Hardware compatibility
      Avoiding crashes and slowdowns
    Cost
      Hardware and energy
      Team and maintenance
      Commercial licenses
    Feasibility
      Integration into your systems
      Operational reliability
      Managing updates

1.3 Open-source vs. Proprietary Models

The problem with proprietary models:

sequenceDiagram
    participant A as Your application
    participant API as Proprietary API
    A->>API: Request (your data)
    API-->>A: Response
    Note over API: Potential outage
    Note over API: Unexpected price change
    Note over A,API: Operational costs ×3 overnight

Advantages of open-source models:

Fine-tuning: further training the model on your data so it improves on your specific tasks — like turning a generalist writer into a domain expert.

Disadvantages of open-source:

1.4 Model Families

graph TD
    LLM[Open-source LLMs] --> Llama
    LLM --> Mistral
    LLM --> Falcon

    Llama["🦙 Llama Family (Meta)"]
    Llama --> L1["Llama 3.1 / Llama 4"]
    Llama --> L2["Sizes: 8B, 70B parameters"]
    Llama --> L3["Comprehensive documentation"]
    Llama --> L4["Large community support"]

    Mistral["💨 Mistral Family (France)"]
    Mistral --> M1["Mistral 7B"]
    Mistral --> M2["Focused on efficiency"]
    Mistral --> M3["High performance vs size ratio"]

    Falcon["🦅 Falcon Series (UAE — TII)"]
    Falcon --> F1["Excellent multilingual support"]
    Falcon --> F2["Falcon 40B: Apache 2.0 license"]
    Falcon --> F3["International applications"]

Recommended use cases by family:

Module 2 — Evaluating Models for Performance and Usability

2.1 The Three Pillars of Evaluation

The fundamental question breaks down into three practical questions:

flowchart LR
    Q["How do I know if\na model is right\nfor my project?"]
    Q --> H["🖥️ Hardware\nCan my hardware\nhandle this model?"]
    Q --> B["📊 Benchmarks\nWhat do these\nnumbers really mean?"]
    Q --> R["🎯 Real-world Test\nHow does it perform\non my own data?"]

The three pillars of evaluation:

1. Model scale — parameter count, memory requirements, context length
2. Hardware fit — can your system run the model at a usable speed?
3. Usability factors — benchmarks, license terms, ease of fine-tuning

2.2 Understanding Specifications: Parameter Count

Parameters are like the model’s “brain cells” — the more there are, the more patterns it can recognize, but also the heavier it becomes.

xychart-beta
    title "Required VRAM by parameter count"
    x-axis ["7B", "13B", "30B+"]
    y-axis "VRAM (GB)" 0 --> 160
    bar [8, 16, 64]

Required hardware levels:

┌─────────────────────────────────────────────────────────┐
│                  30B+ parameters                        │
│         → Enterprise systems / GPU clusters             │
├─────────────────────────────────────────────────────────┤
│                 13B–15B parameters                      │
│         → Workstation-class hardware                    │
├─────────────────────────────────────────────────────────┤
│                  7B–8B parameters                       │
│         → High-end consumer devices                     │
├─────────────────────────────────────────────────────────┤
│                   3B–5B parameters                      │
│         → Standard consumer devices                     │
└─────────────────────────────────────────────────────────┘

Detail by category:

Comparative example — email classification:

Mistral 7B:
  ✅ Classifies by sentiment
  ✅ Categorizes common issues
  ✅ Flags urgency
  ❌ Struggles with sarcasm or mixed sentiments

Llama 2 13B:
  ✅ Everything Mistral 7B can do
  ✅ Better handling of ambiguous cases (sarcasm, mixed sentiments)
  ❌ Requires more memory

2.3 Quantization: Making Large Models Accessible

Understanding LLM weights:

LLMs are neural networks with billions of connections. Each connection has a weight — a number that represents the strength of that connection:

Example — completing "The sky is..." :
  
  "blue"    ←── weight: +0.42  (strong connection)
  "purple"  ←── weight: -0.003 (weak connection)

By default, these weights are stored as 32-bit floating-point numbers:

32-bit float representation:
┌──────┬──────────┬─────────────────────────┐
│ Sign │ Exponent │       Significand        │
│ 1 bit│  8 bits  │         23 bits          │
└──────┴──────────┴─────────────────────────┘
         = 4 bytes per weight

Calculation for a 7B model:
  7,000,000,000 parameters × 4 bytes = 28 GB of memory
                                        (just for the weights!)

Quantization reduces this precision:

graph LR
    A["7B Model\nFP32\n28 GB"] -->|"Quantization\n32-bit → 8-bit"| B["7B Model\nINT8\n~7 GB"]
    B --> C["Runs on a\nstandard laptop!"]

Pros and cons:

Key principle: Quantization is the reason why many large models can run on consumer hardware — without it, most LLMs would be confined to data centers.

2.4 What Is Context Length?

The context length is the amount of text a model can hold in memory while generating responses. It is measured in tokens.

What is a token?

"apple"                → 1 token
"running"              → "run" | "ning"            → 2 tokens
"The apples are        → "The" | "apples" | "are" |
 running out."           "run" | "ning" | "out" | "."  → 7 tokens
"こんにちは" (Hello)   → 4 tokens
👍🔥                   → 3 tokens

A token is not always a word — it’s a fragment of text that the model processes as a basic unit.

Context length levels:

Illustration — customer support:

Customer: "As I mentioned in my previous email
           regarding the billing issue..."

┌──────────────────────────────────────────────────────┐
│  Short context model        │  Long context model     │
├──────────────────────────────────────────────────────┤
│ "I can't see the previous   │ "I see you're following │
│ billing details.             │ up on the billing       │
│ Could you clarify?"          │ discussed earlier.      │
│                              │ Let me check the        │
│                              │ resolution status."     │
└──────────────────────────────────────────────────────┘

Trade-offs of long context:

graph TD
    Long["Long context length"] --> HW["💰 High hardware costs"]
    Long --> PB["📍 Position bias"]
    Long --> AD["📉 Attention degradation"]

    PB --> PB1["The model gives more\nweight to content\nat the beginning and end"]
    AD --> AD1["Performance decreases\nwith very long contexts"]

Speed vs capacity (execution time):

Context  4K  ████████ (fast)
Context 32K  ████████████████████ (moderate)
Context  1M+ ████████████████████████████████████████████████ (slow)
             0      75     150    225    300 seconds

Choosing the right context length:

Short (4K–8K)    → Chats, simple code tasks, daily emails
                   Works on consumer hardware

Medium (32K–128K) → Research articles, multi-file projects
                   Often requires professional GPUs

Massive (1M+)    → Very large inputs (entire books)
                   High costs, risk of precision degradation

2.5 Architectural Considerations

There are three main architectures for open-source LLMs:

graph TD
    A[LLM Architectures] --> T[Traditional Transformer]
    A --> M[Mixture of Experts MoE]
    A --> S[State Space Models]

    T --> T1["✅ Consistent and reliable quality"]
    T --> T2["❌ Loads the entire model into memory"]
    T --> T3["❌ Full power on every request"]
    T --> T4["⚙️ Ideal: general-purpose applications"]

    M --> M1["✅ Activates only relevant parts"]
    M --> M2["✅ Faster and more memory efficient"]
    M --> M3["❌ Less predictable quality"]
    M --> M4["⚙️ Ideal: real-time chat, customer support"]

    S --> S1["✅ Scales well with long text"]
    S --> S2["✅ Less memory for long inputs"]
    S --> S3["❌ Fewer tools and community resources"]
    S --> S4["⚙️ Ideal: long documents or transcriptions"]

Summary comparison:

2.6 Decoding Benchmarks and Metrics

Benchmarks measure general capabilities — not necessarily your specific use case.

MMLU — Massive Multitask Language Understanding

Introduced in a research paper by Dan Hendrycks et al.

What MMLU measures:

• General knowledge across 57 subjects
• From elementary mathematics to philosophy and law

Example questions:

Elementary Math              High School Biology          Philosophy
─────────────────────────────────────────────────────────────────────────────
What is the value of p       Which best provides          Plato believes that
in 24 = 2p ?                 examples of mitotic          true beauty is ___.
                             cell divisions?
A. p = 4                     A. Muscle cross-section      A. In everyday objects
B. p = 8  ✓                  B. Anther cross-section      B. Non-existent
C. p = 12                    C. Stem section ✓            C. Everywhere in nature
D. p = 24                    D. Leaf cross-section        D. Not of this world ✓

MMLU score calculation:

$$\text{MMLU Score} = \frac{\text{Correct answers}}{\text{Total questions}} \times 100$$

Example: 1 correct answer out of 3 → $\frac{1}{3} \times 100 = 33%$

Score interpretation:

< 60%    ▓░░░░░░░░░  Notable knowledge gaps
60–75%   ▓▓▓▓░░░░░░  Decent general knowledge
75–85%   ▓▓▓▓▓▓░░░░  Strong, capable of reasoning
> 85%    ▓▓▓▓▓▓▓▓▓░  Exceptional, near expert-level
~25%     ▒▒░░░░░░░░  Random guessing (4 choices)

Important: MMLU measures breadth, not depth. A model can score 78% overall but much lower in medicine or finance.

HELM — Holistic Evaluation of Language Models

HELM evaluates models across 7 metrics:

mindmap
  root((HELM))
    1 Accuracy
      Correctness of answers
    2 Calibration
      Model confidence
    3 Robustness
      Performance with noisy inputs
    4 Fairness
      Tests across demographic groups
    5 Bias
      Stereotype detection
    6 Toxicity
      Detection of harmful outputs
    7 Efficiency
      Cost measurement

HELM produces a comprehensive report that tests models in realistic use cases.

HumanEval and MBPP — Code Generation Benchmarks

HumanEval:

• 164 hand-written Python problems
• Tests: language comprehension, algorithms, basic mathematics
• Metric: pass@k

Example HumanEval prompt:

def is_palindrome(text: str) -> bool:
    """
    Checks whether a given string is a palindrome.
    
    >>> is_palindrome('racecar')
    True
    >>> is_palindrome('hello')
    False
    """
    # The model must complete this function

Interpreting pass@k:

Example — OpenAI Codex (2021):

pass@1  :  28.8%   ████████░░░░░░░░░░░░░░░░░░░░░░
pass@100:  72.3%   █████████████████████████████░
                   Source: Chen et al., 2021

Generating multiple solutions significantly improves results!

MBPP: ~1000 beginner-level programming tasks.

2.7 Reality Check: Benchmarks vs. Real-world Performance

Caution about marketing claims:

Case study — Customer feedback analysis:

Two models compared on their general benchmarks:

             MMLU    HumanEval   Ranking
AtlasLM    :  84%      72%          #3
Insight-AI :  76%      68%         #12

Results on real customer feedback data:

                        AtlasLM    Insight-AI
─────────────────────────────────────────────
Issue identification :   72%         89% ✓
Severity category    :   65%         84% ✓
Solution suggestions :   58%         76% ✓
  (often generic)       (specific, actionable)
Processing speed     : 2.3s/item  1.8s/item ✓

Why? Insight-AI was trained on more technical documentation and customer service data.

Domain relevance principle:

graph TD
    UC[Your use case] --> CS[Customer service]
    UC --> CC[Content creation]
    UC --> CG[Code generation]
    UC --> AT[Analysis tasks]
    UC --> FA[Factual applications]

    CS --> B1["Benchmarks: sentiment analysis,\nintent classification"]
    CC --> B2["Benchmarks: writing quality,\ncreativity"]
    CG --> B3["Benchmarks: HumanEval, MBPP"]
    AT --> B4["Benchmarks: reading comprehension,\nreasoning"]
    FA --> B5["Benchmarks: knowledge,\ntrustworthiness (HELM)"]

2.8 Step 1: Define Your Requirements

Before looking at models, be precise about what you actually need:

Task Definition

□ Primary function: summarization, Q&A, generation, analysis?
□ Input types: text, code, JSON, mixed?
□ Typical input size (in tokens): 500? 5,000?
□ Expected output format: short paragraph, structured JSON, step-by-step reasoning?
□ Define "good enough" in one sentence
□ Volume: how many requests per hour or per day?

Performance Requirements

□ Acceptable accuracy threshold (e.g., 80% coverage of key points for summaries,
  < 5% incorrect answers for chat)
□ Required latency (e.g., 95% of requests < 2s for 500-token outputs)
  → P95 metric: 95 out of 100 requests meet this target
□ Need for deterministic results (for compliance or audit)?

Resource Constraints

□ Hardware available today (not what you wish you had)
□ Budget for cloud or hardware upgrades
□ Actual technical skill level of your team

2.9 Step 2: Multi-tier Evaluation Approach

Two-tier strategy:

flowchart TB
    subgraph T1["Tier 1 — Local Testing"]
        A["3B–8B models\n(modern laptops)"]
        B["10B–14B models\n(upper limit)"]
        A --> C["Test on your current hardware"]
        B --> C
    end
    subgraph T2["Tier 2 — Research-based Evaluation"]
        D["Hugging Face Open LLM Leaderboard\nhttps://huggingface.co/"]
        E["Ollama Library\nhttps://ollama.com/"]
        F["Official model documentation"]
    end
    T1 -->|"Identify gaps"| T2
    T2 -->|"Compare cost/benefit"| G["Final decision"]

What to test locally:

Decision framework:

1. Start local         → Test up to 14B on your hardware
2. Identify gaps       → Where do smaller models fail?
3. Research larger     → Do larger models fill those gaps?
4. Cost-benefit analysis → Is the improvement worth the hardware investment?

What to verify in research:

□ Hardware specs used for testing
□ Testing methodology (standardized prompts? quality measurement?)
□ Multiple concordant sources?
□ Results based on current model version?

2.10 Step 3: Total Cost of Ownership

Prices reflect the US market in late 2025. Hardware and cloud prices may fluctuate, but the decision framework remains relevant.

Hardware Paths

graph TD
    subgraph Consumer["💻 Consumer Hardware\n$1,500 – $3,500"]
        C1["MacBook Pro M4, 24 GB RAM\n($1,500–$2,000)"]
        C2["Gaming PC + RTX 4070\n($2,500–$3,500)"]
        C3["→ 3B–7B models run smoothly\n→ Up to 14B with quantization"]
    end
    subgraph Pro["🖥️ Professional Hardware\n$8,000 – $20,000"]
        P1["AI Workstation\nDual RTX 4090 or pro GPU\n48+ GB total VRAM"]
        P2["→ 13B–30B models run smoothly\n→ Up to 70B with quantization"]
    end
    subgraph Cloud["☁️ Cloud Computing"]
        CL1["Budget GPU (T4): ~$0.30–$0.50/h\n→ 3B–7B models"]
        CL2["Mid-range GPU (A100): ~$1.30–$2.50/h\n→ 13B–30B models"]
        CL3["High-end GPU (H100): ~$2.50–$4.00/h\n→ 70B+ models"]
    end

Important concept:

• Open-source = what you run
• Cloud = where you run it

You can run open-source models in the cloud!

Real-world Decision Scenario

flowchart LR
    R["Requirement:\nAnalyze technical\ndocuments"]
    R --> T1["Tier 1:\n7B model on laptop"]
    T1 -->|"Struggles with\ncomplexity"| T2["Research:\n30B model"]
    T2 -->|"Good fit"| C["Rent A100\nin cloud"]
    C -->|"Excellent quality\nbut $2/h is costly"| O["TCO: 13B on\nsingle GPU = $0.40/h"]
    O -->|"Near-identical accuracy\nat lower cost"| D["✅ Deploy 13B\nin production"]

2.11 Running Your First Local LLM with Ollama

Ollama is a tool designed for consumer hardware. It automatically handles quantization, memory management, and optimization.

Installation

# macOS (via Homebrew)
brew install ollama

# Or download the installer from:
# https://ollama.com/download

# Windows: run the official installer
# Linux: follow the instructions on the same page

Starting and Verifying

# Start the service
ollama serve

# In a new terminal — verify installation
ollama --version

# List installed models (empty at first)
ollama list

Install and Use Phi-3 Mini (3B)

# Install Phi-3 Mini (Microsoft, 3B parameters, ~2.2 GB download)
ollama run phi3:mini

# Example prompt
> Explain quantum computing in simple terms for a business audience.

Observations on MacBook M2 (24 GB):

• Response time: 5–10 seconds
• System remains responsive
• Brief GPU spike (2 spikes observed)
• No memory pressure

Install and Use Llama 3.1:8B

# Install Llama 3.1 8B (~4.9 GB download)
ollama run llama3.1:8b

# Same prompt
> Explain quantum computing in simple terms for a business audience.

Observed comparison:

• More verbose and detailed output than Phi-3 Mini
• Slightly slower
• Longer GPU spike (6 spikes vs 2 for Phi-3)
• Computer remains usable

Model Management

# Exit a session
/bye

# Remove a model (free up disk space)
ollama rm phi3:mini
ollama rm llama3.1:8b

# Verify models are removed
ollama list

# Stop the Ollama service (Ctrl+C in terminal)
# Or if started with Homebrew:
brew services stop ollama

# Completely uninstall Ollama
brew uninstall ollama
# Windows/Linux: use the standard uninstaller

Tier 2 research resources:

Module 3 — Licenses and Practical Constraints

3.1 Understanding Model Licenses

Downloading an open-source model means accepting the terms of its license — it’s not just fine print, it directly defines what you can do with the model in practice.

Two main license categories:

graph TD
    L[Open-source LLM Licenses] --> P[Permissive Licenses]
    L --> C[Custom Community Licenses]

    P --> P1["'Do almost anything you want'"]
    P --> P2["Allow research + commercial use"]
    P --> P3["Modify, integrate, build a business"]
    P --> P4["Example: Falcon 40B → Apache 2.0"]

    C --> C1["More restrictive"]
    C --> C2["Commercial use allowed with conditions"]
    C --> C3["Requirements and limitations apply"]
    C --> C4["Example: Llama 4 → custom community license"]

Where to find license information:

1. Official model website
2. Model card on Hugging Face
3. LICENSE file in the model repository

Best practices:

□ Does the license match my intended use?
□ Are there limits on modification, redistribution, or commercial use?
□ Will the license work if the project grows?
→ Always review the license before committing.

3.2 License Red Flags to Watch For

🚩 Red Flag #1 — Research-only Restrictions

Some models are explicitly reserved for research purposes.

Example: Mistral MNPL License
  ✅ Non-commercial use and research
  ❌ Commercial deployment → separate agreement required
  
⚠️ If you're building a revenue-generating product or service,
   research-only licenses won't work.

🚩 Red Flag #2 — User Limits and Revenue Caps

Example: Llama 4 License
  → Cap of 700 million monthly active users (MAU)
  → Beyond that: request a separate license from Meta
  
💡 For most projects, 700M MAU is not a concern,
   but if you're building something that could reach
   global scale, you need to know this from the start.

🚩 Red Flag #3 — Attribution Requirements

Example: Llama 4
  → Must display "Built with Llama" prominently:
     • On your website
     • In your user interface
     • In product documentation
     
📌 Not necessarily a dealbreaker, but plan it into
   your product design.

🚩 Red Flag #4 — Geographic Restrictions

Example: Llama 4 multimodal capabilities
  → Specific restrictions for individuals and companies
    based in the European Union
  → EU users may USE products built with these models,
    but cannot DEPLOY them directly.
    
⚠️ If you operate internationally, check geographic
   restrictions that could affect your deployment.

🚩 Red Flag #5 — Acceptable Use Policies

Most licenses include acceptable use policies that prohibit:
  ❌ Illegal activities (e.g., generating hate speech)
  ❌ Unauthorized professional advice in regulated fields
  ❌ Harmful applications
  
→ Make sure your use case does not violate these policies.

Summary of red flags:

flowchart TD
    Start["Evaluate a license"] --> Q1{"Commercial\nuse planned?"}
    Q1 -->|No| OK1["✅ Research license OK"]
    Q1 -->|Yes| Q2{"Permissive license\n(Apache 2.0 etc.)?"}
    Q2 -->|Yes| OK2["✅ Commercial use OK"]
    Q2 -->|No| Q3{"Community license\nwith conditions?"}
    Q3 -->|No| STOP["🛑 STOP — Do not use\ncommercially"]
    Q3 -->|Yes| Q4{"Planned scale\n> 700M MAU?"}
    Q4 -->|Yes| WARN1["⚠️ Contact Meta\nfor separate license"]
    Q4 -->|No| Q5{"EU\ndeployment?"}
    Q5 -->|Yes| CHECK1["🔍 Check geographic\nrestrictions"]
    Q5 -->|No| Q6{"Attribution\nrequired?"}
    Q6 -->|Yes| PLAN["📝 Plan display\n'Built with X'"]
    Q6 -->|No| FINAL["✅ Ready to deploy"]
    CHECK1 --> Q6
    PLAN --> FINAL
    WARN1 --> Q6

3.3 Practical Deployment Considerations

Community Support

Choosing a model also means joining a community.

Benefits of an active community:

✅ Helpful documentation and tutorials
✅ Answers to common problems
✅ Sample code and integration guides
✅ Regular updates and improvements

Questions to ask before committing:

□ Does the GitHub repository or forum show recent activity?
□ Are others using the model for similar projects?
□ Do maintainers respond promptly to issues?

Format Compatibility

□ Does the model work with my current frameworks?
□ Will a format conversion be necessary?
□ Are the required tools available?

Documentation Quality

Good documentation includes:

✅ Clear installation instructions
✅ Practical usage examples
✅ Performance benchmarks
✅ Detailed troubleshooting guides

⚠️ Sparse or confusing documentation = warning sign
   → You'll spend more time figuring everything out yourself

Avoiding Overkill

Sometimes a smaller, faster model that is “good enough” outperforms a theoretically superior larger model.

Questions to ask:
□ Can I test and fine-tune the model quickly?
□ Will this model drain my cloud budget?
□ Is it so complex that only specialists can manage it?

3.4 Where to Go from Here?

You now have a solid foundation for evaluating and choosing open-source LLMs. You understand:

• ✅ The trade-offs between different models
• ✅ How to evaluate benchmarks and resource requirements
• ✅ How to navigate licenses
• ✅ How to align model capabilities with your specific use cases

Recommended next steps:

mindmap
  root((Next Steps))
    Fine-tuning
      Adaptation techniques
      LoRA adapters
      Parameter-efficient methods
    Production deployment
      Advanced quantization
      Inference optimization
      Model serving
    Practice
      Test models locally
      Choose a use case
      Compare results

The best way to consolidate your learning is through practice. Test a few models locally on your machine, choose a specific use case, and compare the results.

Appendix — Resources and References

Tools

Academic References

Summary — Complete Selection Framework

flowchart TD
    S["🚀 Start: Choose an LLM"] --> D1

    subgraph D1["Step 1 — Define Requirements"]
        R1["Define the task precisely"]
        R2["Set performance thresholds"]
        R3["Inventory available resources"]
    end

    D1 --> D2

    subgraph D2["Step 2 — Multi-tier Evaluation"]
        T1["Tier 1: Test locally\n(3B–14B)"]
        T2["Tier 2: Research larger models\n(benchmarks + docs)"]
        T1 --> T2
    end

    D2 --> D3

    subgraph D3["Step 3 — TCO (Total Cost of Ownership)"]
        C1["Evaluate hardware\n(consumer vs pro vs cloud)"]
        C2["Calculate cost per request"]
        C3["Compare performance vs cost"]
    end

    D3 --> D4

    subgraph D4["Step 4 — License Verification"]
        L1["Identify the model's license"]
        L2["Check the 5 red flags"]
        L3["Confirm legal compliance"]
    end

    D4 --> FINAL["✅ Deploy the selected model"]

Model Comparison Table

* MNPL = Mistral AI Non-Production License: research only, separate agreement required for commercial use

Search Terms

choosing · open-source · llms · llm · application · development · artificial · intelligence · generative · ai · model · red · flag · models · benchmarks · evaluation · performance · requirements · considerations · constraints · install · language · licenses · practical