There are several factors you need to consider before starting to train an AI engine.
One of the most important is the engine itself — including its version and model. While the engine can be updated or changed later, selecting the right one from the start can make your training process smoother and your operations more efficient.
For my setup, I chose Ollama as the open-source engine portal. It’s important to understand that not all AI processes are created equal — your needs for local AI may differ significantly depending on your specific function. For example, data cleanup and data processing consume different amounts of resources.
Having a clear understanding of these differences can save you time, prevent bottlenecks, and help ensure a successful training process.
As 2025-03-15.
Version: LLaMA 3.0
Model Sizes: 8B / 70B
Pros:
Solid baseline performance in text generation.
Efficient and lightweight compared to later versions.
Accessible for many hardware setups.
Cons:
Limited context window (e.g., shorter memory in conversations or documents).
No multimodal capability (text-only).
No advanced reasoning or tool-calling abilities.
Less multilingual coverage.
Note:
Great starting point for experimentation and understanding transformer-based LLMs.
Works well for general use, like summarization, chat, or translation, with low cost.
Short context limits use in legal/academic analysis.
Lacks competitive features like function calling or memory.
Can’t be integrated into multimodal workflows (e.g., images + text).
Version: LLaMA 3.1
Model Sizes: 70B / 405B
Pros:
Extended context window (up to 128K tokens).
Improved multilingual support (trained with 8% multilingual tokens).
Tool-use readiness: Function calling and agent optimization.
Excellent reasoning ability (per benchmark tests like MMLU / Massive Multitask Language Understanding).
Cons:
High resource demand (especially 405B).
Still lacks multimodal capabilities (text-only).
Limited real-world tool integrations out-of-the-box (requires engineering).
Note:
Long context enables better document understanding and continuous conversations.
Tool use (e.g., calling APIs) makes it closer to AI agent frameworks.
Multilingual improvement makes it usable globally.
You need enterprise-level GPUs or clusters for 405B — not suitable for most local deployments.
Despite function calling, it doesn’t yet natively support all agent behaviors like memory chaining or retrieval-augmented generation (RAG).
Marketed for tool use, but actual implementation requires external scaffolding (e.g., LangChain).
Version: LLaMA 3.2
Model Sizes: 1B / 3B / 11B / 90B
Pros:
Multimodal support (text + image input).
Mobile & edge optimized (1B, 3B).
High-resolution image handling (up to 1120×1120).
Lightweight deployment options for phones and IoT.
Cons:
Limited documentation and benchmarks.
Multimodal models still under testing in many platforms.
1B/3B models lack deep reasoning power.
Limited fine-tuning resources available at this point.
Note:
Opens doors to multimodal workflows — chat with images, visual document Q&A, etc.
Makes AI possible on small devices and real-time environments.
Ideal for apps, on-device copilots, or smart cameras.
Edge-ready models compromise deep understanding for speed.
Hard to scale for large business logic unless paired with server-based inference.
Promoted as “mobile ready,” yet the image processing resolution suggests heavier needs in memory and power.
High-res image input but limited memory in small models can lead to failure in vision-based reasoning.
Cross-Version Note.
Smaller is better vs bigger is better Small models (1B–8B) are efficient but often underperform in complex reasoning. Larger models (70B–405B) are better at logic and context but require expensive hardware.
Tool readiness vs real integration 3.1 promotes tool use, but it still needs external frameworks like LangChain or LlamaIndex to fully realize this.
Multilingual improvement vs global usability While multilingual token percentage increased in 3.1, it’s still not fully fluent in low-resource or regional dialects.
Multimodal claims vs hardware limitations 3.2 claims edge-compatibility, yet high-res image support suggests mid-range devices may struggle.
3.0 = Best for learning and basic applications.
3.1 = Most powerful for deep context, multilingual tasks, and agent tooling (if you have the hardware).
3.2 = Cutting-edge for vision + text workflows, mobile apps, and embedded AI.
Choosing the right model depends on your goals, hardware, and level of integration needed.