Author's): Ramya Ravi
Originally published in Towards Artificial Intelligence.
LLM training and implementation remain costly and resource-intensive as LLMs become more powerful. Recently, a new generation of lightweight AI optimization frameworks have emerged that enable developers to train, compress, and share models more efficiently.
This new stack is built around three core frameworks:
- Unsloth – Speeds up tuning with memory-saving kernels
- AutoAWQ – Automates quantization to shrink models for cheaper inference
- SGLang – Provides high-throughput and structured inference for production
This stack creates a seamless, end-to-end workflow that reduces computation costs, speeds experimentation, and scales better than traditional stacks.
Let's take a look at each framework, why they're important, and how they fit together to provide AI developers with an efficient and profitable workflow.
1. Unsloth – Fast and efficient tuning
Tuning has traditionally been one of the biggest bottlenecks in working with LLM. Even for mid-size models with ~7B parameters, full tuning or LoRA requires massive GPU memory and long training cycles.
Unsloth solves this problem through kernel-level optimizations and efficient LoRA/QLoRA implementations. It also supports popular models such as LLaMA, Mistral, Phi and Gemma.
Key benefits
· 2-3x faster training compared to standard Face Hugging + PEFT configurations
· Memory-saving LoRA/QLoRA implementations – train 7B-13B models on consumer GPUs
· Optimized CUDA kernels for transformer layers to reduce training costs
Example – How to tune a Lamy 3 model
# Install Unsloth
pip install unsloth# Start fine-tuning
unsloth finetune
--model llama-3-8b
--dataset ./data/instructions.json
--output ./finetuned-llama
--lora-r 8 --lora-alpha 16 --bits 4
Unsloth enables developers and startups to train models at a fraction of the usual cost, without the need for large GPU clusters.
2. AutoAWQ – Smarter Quantization, Smaller Models
After tuning, LLM models are still usually too large to infer profitably. This is where AutoAWQ comes into play. AutoAWQ automates the quantization process in popular LLM architectures, based on the AWQ method (activation-aware weight quantization). Automatically applies AWQ, reducing precision while maintaining accuracy.
Key benefits
· Reduce model size by 50-75% with INT4 quantization
· Compatible with fine-tuned Unsloth models and SGLang inference
· Enables large models to run on consumer or edge hardware
· Drastically reduces application costs
Quantization example
# Install AutoAWQ
pip install autoawq# Quantize your model
autoawq quantize
--model ./finetuned-llama
--output ./llama-awq
--wbits 4
By using AutoAWQ after tuning, before deployment, you can shrink your models and reduce inference costs at scale.
3. SGLang – High Performance Structured Inference
Once the model is trained, the next challenge is to operate it efficiently. SGLang is a next-generation inference engine built for structured generation and high throughput. It can act as a replacement for inference frameworks such as vLLM while offering greater control over the structure of generated results, which is ideal for applications such as function calling, JSON generation, or agent structures.
SGLang uses the optimized vLLM runtime but adds a layer of abstraction to facilitate structured and multi-step generation.
Key benefits
· Faster inference with optimized KV cache support and token streaming
· Support for structured results – ensures models produce parsable and predictable formats (no regex hacks)
· High throughput in multi-user environments
· Lightweight and production ready with no custom hacks
Serving example
# Install SGLang
pip install sglang# Serve your Model
sglang serve --model ./llama-awq --port 8080
You can then send structured queries:
from sglang.client import Clientclient = Client("http://localhost:8080")
response = client.generate(
prompt="Return a JSON object with two fields: framework and benefit",
format="json"
)
print(response.text)
With SGLang, developers can scale inference to thousands of concurrent users while maintaining a good response structure for downstream applications.
How do these frameworks fit together?
By combining Unsloth, AutoAWQ and SGLang, developers can build an end-to-end pipeline:
1. Adapt with Unsloth – fast and efficient training even on single GPUs
2. Quantize with AutoAWQ – Shrink models for cheaper and faster inference
3. Serve with SGLang – Deploy structured, high-throughput inference at scale
Together they create a modern, modular optimization process that saves money, accelerates development and scales production.
Summary and next steps
If you are an AI developer, now is the time to experiment with this modular stack. This framework reflects a broader shift in the AI ecosystem:
· Instead of universal tools, developers create stacks tailored to individual needs
· GPU time = money – optimization directly affects lifespan
· These tools allow small teams to do what previously required large research labs
· Tuning, quantizing and sharing become plug-and-play
While Unsloth, AutoAWQ, and SGLang cover the basic stages, the ecosystem is rapidly evolving. Some complementary tools worth considering include vLLM (a great choice for high-throughput inference, especially for cloud-native deployments), Axolotl (a popular precision orchestration tool that can be integrated with Unsloth).
Published via Towards AI
