Understanding Inference Models: What they are and why they matter?

Artificial Intelligence powers everything from chatbots to medical imaging, but there's one concept at the heart of most modern AI applications: inference.

While training large models like GPT or LLaMA gets all the attention, it's inference that actually brings AI to life in apps, assistants, and automations. Whether you’re a developer, researcher, or just curious about how tools like ChatGPT or Ollama work under the hood, understanding inference models is essential.

Let’s break it down.

---

Inference Model

An inference model is a trained AI or machine learning model that’s being used to make predictions or generate outputs on new data.

You're not teaching the model anymore — you're using what it already knows to do something useful:

• Predict a word, label an image, summarize a document, or generate code.

Example:

• Training: Feed a model millions of images of cats and dogs.

• Inference: Show it a new image → it tells you “cat”.

In the world of large language models (LLMs), inference means:

Prompt → Model → Text Response

---

How Inference Happens: The Process

1. 🧾 Input is tokenized (e.g., “Hello” → [15496])

2. ⚙️ The model processes the tokens through multiple layers

3. 📤 An output is generated (text, number, label, etc.)

For example, asking an LLM:

“Explain quantum computing in simple terms”
results in a full paragraph written by the model — this is inference.

---

Inference Platforms vs. Running Models Directly

You can run inference in two main ways:

1. Run Models in Code

Use libraries like PyTorch, Transformers, or TensorFlow:

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct")
model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct")

inputs = tokenizer("What is inference?", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))

✅ Pros:

• Full control

• Customisation

• Good for research

❌ Cons:

• Heavy setup

• Requires managing memory, GPUs, dependencies

---

2. Use Inference Platforms

Platforms like Ollama, Groq, LM Studio, and Hugging Face Inference API provide:

• One-command setup

• Efficient model execution

• REST APIs or CLI tools

Example with Ollama:

# Start LLaMA 3 locally
ollama run llama3

✅ Pros:

• Fast and optimised

• Low setup effort

• Can run locally (Ollama) or in cloud (Groq)

❌ Cons:

• Limited customisation

• Depends on platform support

---

Do Inference Models need the Internet?

❌ No, if you're running locally:

• Tools like Ollama, LM Studio, or WebUI work completely offline

• Great for privacy, edge computing, and offline environments

✅ Yes, if you're using cloud inference:

• Platforms like OpenAI, Anthropic, GroqCloud require an internet connection to access their hosted models

---

Popular Inference Tools

Here are common tools for running inference in different ways:

---

Final Thoughts

Inference is the final step in the machine learning pipeline — and arguably the most important. It’s where trained models turn theory into action, powering everything from virtual assistants to real-time translation and intelligent automation.

Further Reading

• Ollama Docs

• GroqCloud

• Hugging Face Transformers

• vLLM GitHub

👋 Enjoyed this blog?

Reach out in the comments below or on LinkedIn to let me know what you think of it.

For more updates, do follow me here :)