LLM Landscape Overview

Table of Contents

• B. Orchestration Layers (Switch Between Models Easily)
  • What Is an Orchestration Layer? (Simple Definition)
  • Why Orchestration Matters
  • Popular Orchestration Tools
  • Example: Switching Models Without Orchestration
  • Example: Switching With Orchestration (LiteLLM)
  • Use Cases Where Orchestration Is Essential
• Why Beginners Should Learn Orchestration Early
  • C. Local Models (Ollama, llama.cpp, LM Studio)

Model Types & Breeds

LLMs come in different breeds, each designed for specific purposes.

To navigate this space, we need to understand the difference between Base Models, Instruction-tuned Models, and Reasoning Models, along with the split between Frontier and Open-Source approaches.

What is a Base Model ?

A Base Model is a raw, PreTrained model that has only learned from its training data using the next-token prediction objective. It has not been gone through special training for following instructions or having conversations.

Below are the characteristics of a Base Model.

• More creative but less predictable
• Does not reliably follow instructions
• Requires careful prompting or examples
• Good for research, fine-tuning, and experimentation
• Not ideal for everyday users

What is an Instruction/Chat Model ?

An Instruction Model (also called Chat Model or Instruct Model) is a base model fine-tuned to follow instructions, answer questions, and behave like an assistant.

These models has undergo:

1. Supervised Fine-Tuning (SFT):
   In this approach LLM Engineers write example instructions + ideal responses, used for training LLMs.

2. RLHF (Reinforcement Learning with Human Feedback):
   In RLHF we observe and rank the responses from model and it learns from our feedback, that what “good behavior” looks like.

Base vs. Instruction Models

Example:

• GPT-3 (base) → unpredictable
• InstructGPT / GPT-3.5 → safe, helpful assistant

Reasoning Models

Standard LLMs are good at generating text - stories, summaries, explanations.

Reasoning models, however, are optimized to go beyond surface-level generation. They are designed to actively think through problems, break tasks into steps, and produce more reliable, logically consistent answers.

They often include internal prompting techniques like:

• Chain-of-Thought (CoT):
  • The model is encouraged to explain its reasoning step-by-step.
  • Example prompt: “Explain your reasoning step-by-step before giving the final answer.”
  • This leads to dramatically higher accuracy in: Math, Logic, Data Analysis, Structured Reasoning Tasks.
• Step-by-step reasoning traces
  • These models produce — or internally use — sequences of reasoning such as:
    • Breaking a problem into subproblems
    • Verifying assumptions
    • Evaluating intermediate states
    • Correcting earlier steps
    • This mirrors human problem-solving.
• Internal monologue before producing final answer
  • Modern frontier models (like GPT-4o or Claude 3) do a form of internal self-reflection before answering.
    • These hidden traces aren’t shown to the user.
    • They improve accuracy and stability.
    • They reduce hallucinated reasoning.
    • It’s similar to a person thinking silently before speaking.

Models like OpenAI o-series, Claude 3, or Gemini 1.5 are optimized for:

• Math
• Logic
• Multi-step decision-making
• tool-based automation (agents)
• Planning

Mixture-of-Experts (MoE) Models

As models grow larger, training and running them becomes extremely expensive. A traditional dense LLM uses every parameter for every input. This means: more parameters, more compute, more cost and slower inference.

Mixture-of-Experts (MoE) architecture solves this problem, without increasing compute proportionally. Instead of one giant neural network that handles every task, an MoE model is built from multiple specialized networks called “Experts”.

Think of it like a team of experts - One expert is good at coding, One is good at math, One handles language translation, One handles reasoning.

When you give the model a prompt, a router selects which experts should be activated, and only those experts run - the rest stay idle.

This means:

• More parameters overall
• Less compute per token
• Better cost-efficiency

Which includes some of the models like;

• Mixtral 8x7B (Mistral)
• GroqMoE models
• Gemini 1.5 Pro architecture uses MoE concepts

Insight: That is how an MoE models scale intelligence without proportionally scaling computation.

Frontier Models (Closed-Source)

Frontier models are the most advanced, cutting-edge Large Language Models built by major AI labs such as OpenAI, Google, Microsoft, and Anthropic.

They are called frontier because they sit at the frontier of AI capability - meaning they push the limits of reasoning, safety, multimodality, and performance.

Unlike open-source models, these models are not released to the public. We cannot download them or see their internal architecture. Instead, they are available only through an API (Application Programming Interface).

Below companies are responsible for the most advanced AI capabilities available today - especially reasoning, multi-step problem-solving, multimodality (text + image + audio), and agentic behavior.

Strengths

They outperform open-source models in reliability and accuracy, with their;

• Strong reasoning
• Best-in-class safety
• Multi-modality
• High reliability

Trade-offs

• Not transparent
• API-only
• Can be expensive
• Can’t fine-tune

Open Source Models

Open source LLMs Models give users full control, something closed-source frontier models cannot offer. Thses models are fully downloadable and runnable locally or in the cloud.

They allow teams to build highly customized AI systems without relying on a single provider.

• Common benefits include:
• Privacy (your data stays on your machine)
• Lower cost (you pay only for hardware or use existing)
• Flexibility (fine-tune, modify, or integrate deeply)
• Community-driven improvements

Above features make open models highly attractive for startups, enterprise teams, and hobbyist developers alike.

Trade-offs

• Slightly weaker reasoning
• Requires more engineering effort
• Context windows are usually smaller

Accessing the Power of LLMs

Now that we understand the different kinds of LLMs, the next question comes is: - “How do I actually use these models in my application?”

There are two primary ways developers access LLMs:

1. Call a hosted model directly through a provider’s API
2. Use an orchestration layer to manage multiple models and providers

This section explains both approaches in a way that’s easy to understand, with examples and clear trade-offs.

A. Direct Model Providers (APIs)

Direct providers are companies that host the models on their own servers and expose them through simple APIs. This is the most common route for production systems.

You send a request like:

{ "model": "gpt-4o", "messages": [ ... ] }

Here’s the list of Direct Providers

These providers give you instant access to some of the most advanced AI systems in the world - without needing to setup your own hardware like GPUs.

The provider handles all resources for the Model eg. infrastructure, GPU clusters, scaling, uptime, safety checks.

Benefits

Perfect for beginners and rapid prototyping. As you can integrate a state-of-the-art model with: a single API key, one HTTPS request, minimal setup

• Fastest integration path
• High performance + reliability
• Managed scaling & monitoring
• Extra features like safety classifiers, embeddings, caching

Limitations

Every token processed has a cost. Heavy workloads (RAG systems, agents, chat apps) can become expensive inside large organizations.

• Requires stable internet
• Dependent on provider uptime
• Limited model customization
• Cost grows with usage

B. Orchestration Layers

As you start integrating LLMs into real applications, you’ll quickly discover a need for flexibility:

• What if one provider becomes too expensive?
• What if a model goes down temporarily?
• What if a new model is released that performs better?
• What if you want a fallback model for safety?

This is where orchestration layers come in.

What is an Orchestration Layer ?

An orchestration layer is a tool or framework that sits between your application and the actual LLM providers. It provides a single, unified API for interacting with multiple models from different providers.

So, instead of tying your app to one provider eg. OpenAI or Anthropic or Google, orchestration providers let you switch providers on the fly without rewriting code.

Insights: Think of it as: “One API to use all LLMs.”

Why Orchestration Matters

Avoid Vendor Lock-In, if you build your entire product around only one provider, switching later becomes painful.

Orchastration Providers allows automatic fallback and routing, that is if the requested model - fails, time-outs, reached rate-limit or becomes unavailable the orchestration layer can automatically switch to another model eg. Try GPT-4o → if fails, try Claude 3 → if fails, try Mistral 7B.

This dramatically increases reliability for production apps.

These tools also allows us to:

• Swap models with 1 line of code
• Test multiple models for a task
• Benchmark the performance and cost

Cost Optimization

You can route requests based on:

• cost (use cheap model for easy tasks)
• speed (use Groq for fastest inference)
• accuracy (use frontier models for complex logic)

Example strategy:

• Use Mixtral or Phi-3 for simple Q&A
• Use GPT-4o or Claude Opus for high-stakes reasoning
• Use Groq or Mistral for speed-sensitive tasks

This reduces cost without reducing quality.

Popular Orchestration Tools

These tools let you integrate multiple LLMs without duplicating code or managing separate client libraries.

Following are the Use Cases where orchestration is essential

• Multi-model apps (e.g., reasoning + vision + fast mode)
• Chatbots that need fallback models
• AI products with strict SLAs
• Cost-optimized deployments
• Enterprise systems needing provider redundancy
• RAG pipelines with specialized embedding or summarization models

C. Local LLM Models

Local models are open-source LLMs that can be downloaded and run entirely on your own device locally. It doesn’t sent any information to external servers and you have full, unlimited access that for ree of cost as long as your hardware supports it. Your only cost is your hardware.

While APIs give you access to powerful cloud-hosted models powerful models running on best hardware, Local LLMs offer a level of control and privacy that cloud APIs cannot.

While APIs give you access to powerful cloud-hosted models, Local LLMs are becoming increasingly important due to:

• privacy
• cost control
• offline capability
• experimentation and customization

Thanks to tools like Ollama, llama.cpp and LM Studio, running models locally has become accessible, even for beginners.

Ollama

Ollama is the most developer-friendly tool for running local models.

It provides ready to use features like:

• Simple CLI
• Built-in model downloader
• GPU/CPU-optimized inference
• Local server (HTTP API)
• Plug-and-Play model support

Once you have installed Ollama, you can quickly run a model with below commands.

# Pulls if not available locally and start interactive CLI 
ollama run llama3   # run llama3 model
ollama run mixtral  # runs mistral model

# To run a custom model
ollama create mymodel -f Modelfile

This offers benefits like:

• CPU/GPU-optimized
• Supports quantized models
• Easy to embed in apps (HTTP server)
• Works with LangChain and other frameworks

llama.cpp

llama.cpp is a highly optimized C/C++ inference engine designed to run Llama-family and other open-source models on consumer hardware - even without a GPU.

It was originally developed to make Llama 2 run on a MacBook CPU, but it quickly became the backbone of the entire local AI ecosystem due to its efficiency and portability.

It uses Quantization compresses models (e.g., 7B → 3–4 GB) without large accuracy loss. Which makes the models load faster, require far less RAM/VRAM and run smoothly on everyday hardware.

Insight: This is the reason, how even a Raspberry Pi can run smaller models.

llama.cpp has proven performance across the ecosystem. Hence, many popular tools are built directly on llama.cpp including - Ollama, LM Studio, GPT4All etc.

If you’re running a local model today, chances are llama.cpp is doing the heavy lifting behind the scenes.

When Should Beginners Use llama.cpp ?

For most users, Ollama is friendlier. For advanced developers, llama.cpp offers unmatched control and speed.

You should use llama.cpp if you want:

• Maximum performance on CPU
• To embed models into custom apps
• Fine-grained control (threading, quantization, memory)
• To learn how local inference works internally

LM Studio

LM Studio is a graphical desktop application that makes running local models as simple as using ChatGPT, without touching command-line tools.

Think of it as:
“The VS Code of Local LLMs.”
A clean UI → built on llama.cpp → optimized for local evaluation.

This is great for Beginners as well as Professionals, no need to find the model files, manage formats, configure GGUF or write commands.

Just Search for models (Llama, Mistral, Gemma, Qwen) directly inside the app, click download & start chatting in few clicks. Perfect for quick experimentation.

It is bundled with Local HTTP Server for Developers, one of LM Studio’s most useful features to serve your local model as an API, similar to OpenAI’s API.

POST http://localhost:1234/v1/chat/completions

Summary

Quick Recommendations (For Beginners)