Deploy an LLM on a Budget: Quantization, Pruning, and Distillation

By / October 16, 2025

Large Language Models (LLMs) like GPT-4, Claude, and LLaMA have transformed how we build AI-powered applications. They can write, summarise, generate code, and even reason, but this power comes with a cost.

Running or deploying an LLM can be computationally expensive. A single model with billions of parameters often demands tens of gigabytes of GPU memory and high-end cloud servers that cost hundreds (or even thousands) of dollars monthly.

However, with the right optimisation techniques: Quantisation, Pruning, and Distillation, you can drastically cut costs, reduce latency, and run models on affordable hardware, including CPUs or smaller GPUs.

This guide breaks down each method in detail, explaining how they work, the tools you can use, and practical workflows to make your LLM deployment lightweight, scalable, and budget-friendly.

1. Why Optimising LLMs Is Crucial

Before we dive into techniques, let’s understand why optimisation is essential.

Modern LLMs like LLaMA 2 (70B) or GPT-NeoX (20B) have billions of parameters. Each parameter represents a weight, a floating-point number stored with 16- or 32-bit precision.

That means:

  • More parameters → More memory usage
  • More computations → Higher inference latency
  • More resources → Higher cloud bills

Even a 13B model might require ~24 GB VRAM just to load, and significant GPU power for inference. For startups, indie devs, or edge deployments, this isn’t sustainable.

Here’s what optimisation helps with:

  • Run models on CPUs or smaller GPUs
  • Speed up inference and response times
  • Reduce hosting and energy costs
  • Enable on-device AI on mobile or edge systems

2. Quantisation: Making Models Smaller & Faster

What Is Quantisation?

Quantisation is the process of compressing model weights from high precision (e.g., 32-bit floats) into lower-precision integers (e.g., 8-bit or 4-bit). It doesn’t change the architecture. It simply represents numbers with less precision, which:

  • Reduces model size and memory usage
  • Improves inference speed
  • Has minimal accuracy loss

How Quantisation Works

Imagine a weight value like 0.83456. In FP32 (32-bit floating-point), it occupies 4 bytes. If we store it as an INT8 (8-bit integer), we approximate it to something like 0.83 But only use 1 byte.

Now multiply that savings by billions of parameters. That’s massive compression.

Types of Quantisation

  1. Post-Training Quantisation (PTQ):
    Apply quantisation after training. Fast but may reduce accuracy slightly.
  2. Quantisation-Aware Training (QAT):
    The model is trained to anticipate quantisation noise, more accurate but requires retraining.

Popular Quantisation Libraries

  • bitsandbytes (Hugging Face) – INT8 & INT4 quantization for PyTorch models
  • GGUF / GGML – Used for LLaMA, Mistral, and Mixtral models (great for CPU inference)
  • Intel Neural Compressor – Optimises models for Intel hardware
  • TensorRT / ONNX Runtime – For GPU and edge deployment

Example: Load an 8-bit Quantised Model

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "meta-llama/Llama-2-7b-hf"
tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    load_in_8bit=True,
    device_map="auto"
)

Result: Up to 75% memory reduction and faster inference with negligible accuracy loss.

3. Pruning: Trimming the Model Without Hurting It

What Is Pruning?

Pruning removes parts of a model (neurons, layers, or connections) that contribute little to predictions, like trimming dead branches from a tree. It makes the model smaller, faster, and more efficient while maintaining accuracy.

Types of Pruning

  1. Unstructured Pruning:
    Removes individual weights with low importance. Best for research, harder to run efficiently on hardware.
  2. Structured Pruning:
    Removes entire neurons, filters, or attention heads, better suited for production as it simplifies architecture.

How It Works

Each weight in a model contributes differently to outputs. By analysing their “importance” (often using magnitude or gradients), we can safely remove those that don’t matter.

After pruning, the model is retrained (“fine-tuned”) to recover any accuracy lost.

Benefits

  • 20–50% fewer parameters
  • 2–3× faster inference
  • Smaller model size, lower memory requirements

Tools for Pruning

  • PyTorch’s pruning module
  • SparseML (by Neural Magic)
  • Hugging Face Optimum

Example Workflow

  1. Load your pretrained model.
  2. Apply structured pruning (e.g., remove 30% least important weights).
  3. Fine-tune the model briefly to regain lost accuracy.
  4. Save and deploy.

4. Distillation: Training a Smaller Model to Mimic a Larger One

What Is Knowledge Distillation?

Distillation trains a smaller model (the student) to imitate the performance of a larger model (the teacher).

The smaller model learns not just the “correct answers,” but also how confident the teacher is about each prediction, effectively learning how to think like the big one.

Why It Works

Large models generalise knowledge well but are expensive to run. By distilling, you can train a smaller model that retains most of the accuracy but is faster and cheaper.

The Process

  1. Train or load a large teacher model.
  2. Use it to generate “soft labels” (probability distributions) on your dataset.
  3. Train a smaller student model to reproduce those outputs.

Example: Distilling BERT → DistilBERT

  • DistilBERT retains 97% of BERT’s accuracy
  • Uses 40% fewer parameters
  • Runs 60% faster

Popular Tools for Distillation

  • Hugging Face’s Trainer API
  • TinyLlama (distilled from LLaMA 2)
  • DistilGPT-2, DistilWhisper

Example Code Snippet

from transformers import Trainer, TrainingArguments, AutoModelForSequenceClassification

teacher_model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased")
student_model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased")

# The student learns from teacher predictions (soft labels)
training_args = TrainingArguments(
    output_dir="./results",
    learning_rate=5e-5,
    num_train_epochs=3
)

trainer = Trainer(
    model=student_model,
    args=training_args,
    train_dataset=train_dataset
)

trainer.train()

5. Combining Quantisation, Pruning & Distillation

The most effective optimisation often combines all three methods.

Suggested Workflow:

  1. Start with Pruning: Remove redundant parameters.
  2. Then Apply Quantisation: Compress weights for efficiency.
  3. Finish with Distillation: Train a smaller model for deployment.

This multi-step approach can achieve a reduction of up to 90% in model size with minimal loss of accuracy, making it ideal for startups or low-cost cloud environments.

6. Low-Cost Deployment Options

Once your LLM is optimised, you can deploy it using lightweight inference frameworks or cheaper cloud setups.

Best Platforms

  • Ollama / LM Studio: Run quantised models locally (Mac/Windows/Linux).
  • Hugging Face Inference Endpoints: Managed hosting for smaller models.
  • vLLM or Text Generation Web UI: Efficient GPU/CPU serving with batching.
  • ONNX Runtime: Convert and serve models optimised for CPU inference.
  • TensorRT / TFLite: For edge and mobile deployment.

Pro tip: Save your quantised or pruned model in GGUF or ONNX format for universal compatibility.

7. Case Study: From LLaMA-2 to TinyLLaMA

  • Original Model: LLaMA 2 (13B parameters, ~24 GB VRAM needed)
  • Optimised: TinyLLaMA (1.1B parameters, 3 GB VRAM)
  • Techniques Used: Distillation + Quantisation
  • Result:
    • 80% smaller
    • 4× faster inference
    • 95% of original accuracy retained

This shows that you can realistically deploy powerful AI assistants without massive servers.

Conclusion

Deploying an LLM doesn’t have to mean sky-high GPU bills or massive infrastructure. By strategically using quantisation, pruning, and distillation, you can:

  • Compress model size by up to 90%,
  • Retain most of the accuracy,
  • And deploy AI systems efficiently and affordably, even on edge devices.

The future of AI isn’t just about building bigger models. It’s about making them smarter, faster, and more accessible to everyone.

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