Managing Large Language Models (LLMs) in Production

📚 Key Learnings

• What makes LLMs different from traditional ML models
• Fine-tuning vs prompt engineering
• Deployment patterns for LLMs (real-time vs batch, on-demand vs always-on)
• Managing GPU infrastructure and resource scheduling
• Logging, monitoring, and scaling LLM inference
• Cost-saving strategies using model distillation, quantization, and caching
• Serve LLM using tools like vLLM, Hugging Face Inference, OpenLLM, and Ray Serve

Inference & Deployment

Inference

• Can be done via APIs (e.g., OpenAI, Hugging Face)
• Requires GPU or specialized inference hardware

Deployment Strategies

• On cloud: using SageMaker, Vertex AI, Azure ML
• On-prem: using NVIDIA Triton, ONNX Runtime
• Edge deployment: using quantized/distilled models

Challenges and Risks

• Bias in training data
• Hallucination (confidently wrong outputs)
• High computational cost
• Privacy & security concerns
• Regulation & ethical use

Tools and Ecosystem

• Hugging Face Transformers
• LangChain
• OpenAI API
• LlamaIndex
• DeepSpeed, vLLM for fast inference
• Weights & Biases / MLflow for experiment tracking

Fine-Tuning Techniques for LLMs – LoRA, QLoRA, PEFT, and Adapters

Fine-tuning Large Language Models (LLMs) can be resource-intensive due to their size. To make this process more efficient, several parameter-efficient techniques have emerged. Let's explore popular fine-tuning strategies: LoRA, QLoRA, PEFT, and Adapters, highlighting how they work and when to use them.

Why Fine-Tuning Matters

• Aligns models with domain-specific tasks or datasets.
• Improves performance on specialized tasks without retraining the entire model.
• Reduces inference latency and improves control over outputs.

Core Techniques

1. 🔧 LoRA (Low-Rank Adaptation)

Aspect	Detail
Purpose	Efficient fine-tuning using low-rank matrix updates
Method	Injects trainable low-rank matrices into frozen layers
Pros	Lightweight, fast training, reduces GPU memory usage
Use Cases	Chatbots, domain-specific LLM tuning
Libraries	peft (Hugging Face), lora (FastChat)

from peft import get_peft_model, LoraConfig
peft_config = LoraConfig(task_type="CAUSAL_LM", r=8, lora_alpha=32, lora_dropout=0.1)
model = get_peft_model(base_model, peft_config)

QLoRA (Quantized LoRA)

Aspect	Detail
Purpose	LoRA + quantization (4-bit or 8-bit) for memory efficiency
Method	Uses 4-bit quantized weights + LoRA adapters
Pros	Run large models on a single GPU, 2-4x memory savings
Use Cases	Fine-tuning 65B models on consumer GPUs
Libraries	bitsandbytes, transformers, peft

from transformers import BitsAndBytesConfig
bnb_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_use_double_quant=True)
model = AutoModelForCausalLM.from_pretrained("model_name", quantization_config=bnb_config)

PEFT (Parameter-Efficient Fine-Tuning)

Aspect	Detail
Purpose	Umbrella framework for techniques like LoRA, Prompt Tuning
Method	Train only a small subset of model parameters
Pros	Unified API, scalable for many models
Use Cases	Low-resource environments, multi-task setups
Libraries	Hugging Face peft

from peft import prepare_model_for_kbit_training
model = prepare_model_for_kbit_training(model)

Adapters

Aspect	Detail
Purpose	Plug-in modules injected into transformer layers
Method	Train small adapter layers; leave backbone frozen
Pros	Modular, reusable across tasks, fast training
Use Cases	Multi-task fine-tuning, federated setups
Libraries	AdapterHub, Hugging Face Adapters

from transformers import AdapterConfig
adapter_config = AdapterConfig(mh_adapter=True, output_adapter=True)
model.add_adapter("my_task", config=adapter_config)

Comparison Table

Technique	Memory Efficient	Modular	Suitable for Low-resource	Easy to Implement
LoRA	✅	⚠️	✅	✅
QLoRA	✅✅	⚠️	✅✅	⚠️
PEFT	✅	✅	✅✅	✅✅
Adapters	✅	✅✅	✅	⚠️

When to Use What?

Scenario	Suggested Method
Limited GPU memory	QLoRA
Training same model on multiple tasks	Adapters
Need fastest training and inference	LoRA
Want general-purpose framework	PEFT

Fine-Tuning vs Prompt Engineering

Aspect	Fine-Tuning	Prompt Engineering
Definition	Modifying model weights using labeled data	Crafting inputs to guide model behavior
Effort Required	High (data prep, training, infra)	Low to moderate (text input crafting)
Cost	Expensive (compute, storage)	Inexpensive, especially with hosted models
Latency	Slightly higher due to larger models	Same as base model
Customization	Deep, task-specific customization	Shallow, context-based adjustments
Repeatability	Consistent once trained	May vary with prompt changes
Use Cases	Domain adaptation, enterprise apps	Quick prototyping, dynamic interactions
Tooling	Requires training frameworks (LoRA, PEFT, etc.)	No-code or simple API-based
Deployment	Host fine-tuned model manually	Use SaaS APIs like OpenAI, Claude, etc.
Best For	High-accuracy & large-scale production tasks	Fast iteration, experimentation, few-shot tasks

What Makes LLMs Different from Traditional ML Models

Large Language Models (LLMs) like GPT, BERT, LLaMA, and Claude represent a new generation of AI systems capable of understanding and generating human-like language at scale. Let's explore how LLMs differ from traditional machine learning (ML) models, especially in terms of architecture, training, capabilities, and use cases.

Key Differences

Aspect	Traditional ML Models	Large Language Models (LLMs)
Purpose	Solve narrow tasks (classification, etc.)	General-purpose language understanding & generation
Input	Structured/tabular/numerical data	Natural language text
Architecture	Decision Trees, SVMs, Logistic Regression	Transformers with attention mechanisms
Training Data	Task-specific datasets (often small)	Massive corpus of unstructured text (web, books)
Model Size	Thousands to millions of parameters	Billions to trillions of parameters
Generalization	Limited to trained tasks	Few-shot, zero-shot, and transfer learning capable
Pre-training	Rare; models trained from scratch	Pre-trained on large corpus, fine-tuned for tasks
Compute Requirement	Low to moderate	Extremely high (requires GPU/TPU clusters)
Inference	Fast and lightweight	Slower and resource-intensive
Explainability	Easier to interpret (e.g., decision paths)	Harder to interpret; black-box behavior

Examples

Traditional ML Model:

from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.fit(X_train, y_train)
predictions = model.predict(X_test)

LLM (Using OpenAI's GPT):

import openai
openai.api_key = "YOUR_API_KEY"
response = openai.ChatCompletion.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Explain quantum physics in simple terms"}]
)
print(response['choices'][0]['message']['content'])

Use Cases Comparison

Task	Traditional ML Model	LLM
Sentiment Analysis	Logistic Regression on TF-IDF	Fine-tuned GPT or BERT
Price Prediction	Linear Regression	Not a strong fit
Text Summarization	Not ideal	GPT, T5, LLaMA
Fraud Detection	Random Forest / XGBoost	Can assist in explanation/report generation
Chatbot	Rule-based/NLU + ML classifiers	Fully powered conversational LLMs
Code Generation	Not applicable	Codex, Code LLaMA, DeepSeek-Coder

Why LLMs Matter

• Enable natural human-computer interaction.
• Can perform multiple tasks without retraining.
• Power modern AI assistants, search engines, and copilots.
• Capable of creative tasks like writing, coding, and design.

Challenges of LLMs

• High cost of training and inference
• Hallucinations and factual inaccuracies
• Difficulty in fine-tuning and alignment
• Data privacy and ethical concerns

When to Use LLMs vs Traditional ML

Situation	Recommended Model Type
Structured data tasks	Traditional ML
Resource-constrained environments	Traditional ML
Open-ended or text-based tasks	LLM
Need for generalization	LLM

Deployment Patterns for Large Language Models (LLMs)

Deploying LLMs efficiently and cost-effectively requires choosing the right deployment pattern based on use case, latency requirements, resource constraints, and user interaction patterns. Let's compare common deployment patterns like real-time vs batch and on-demand vs always-on, highlighting their trade-offs and applications.

Core Deployment Dimensions

1. Real-Time vs Batch

Dimension	Real-Time Inference	Batch Inference
Latency	Low-latency (<1s), instant response	High-latency, processed on schedule
Use Cases	Chatbots, search, live support, autocomplete	Report generation, summarization, analytics
Trigger	User/API request	Scheduled jobs, data triggers
Compute Cost	Higher (scaled per request)	Lower per request, but possibly higher peak
Scalability	Needs autoscaling	Can batch process at off-peak times
Example	GPT-powered assistant in web app	Weekly email summarizer for CRM records

2. On-Demand vs Always-On

Dimension	On-Demand (Cold Start)	Always-On (Warm Start)
Startup Time	Slower (seconds to minutes)	Fast (sub-second latency)
Cost	Cost-efficient for infrequent usage	Costly but high-performance
Use Cases	DevOps tools, infrequent endpoints	High-traffic chatbots, customer service
Infra Style	Serverless, function-as-a-service	Dedicated GPU/TPU-backed containers
Trade-offs	Latency during startup	Idle costs, scaling complexity
Example	Lambda function calling OpenAI API	Kubernetes pod serving LLM via FastAPI

Hybrid Patterns

Many production systems use a combination of these patterns:

• Batch + Always-On: LLM processes batch requests continuously on dedicated infra.
• Real-Time + On-Demand: Cost-saving pattern for dev/test environments.
• Real-Time + Always-On with Auto-Scaling: Ideal for SaaS/production chatbots.

Infrastructure & Tooling Examples

Tool/Platform	Use Case	Deployment Style
AWS SageMaker	Batch & real-time	Batch Transform / Endpoint
Vertex AI	Real-time APIs	AutoML / Deployed Model
OpenAI API	Real-time, on-demand	Fully managed SaaS
BentoML	Real-time LLM serving	On Docker / Kubernetes
VLLM / TGI	High-throughput inference	Always-on with GPU optimization
FastAPI + HuggingFace	Custom REST API	On-Demand / Always-On

Choosing the Right Pattern

Criteria	Recommended Pattern
High throughput, low latency	Real-Time + Always-On
Cost-sensitive and infrequent	On-Demand + Batch
Daily scheduled text processing	Batch
Multi-user customer support	Always-On + Auto-Scaling
Dev/test & experimentation	On-Demand / Serverless

Managing GPU Infrastructure and Resource Scheduling

Efficiently managing GPU infrastructure is critical for high-performance AI workloads like training and deploying LLMs.

Core Components of GPU Infrastructure

Component	Description
GPU Hardware	NVIDIA A100, V100, H100, L4, AMD Instinct, etc.
Server Nodes	On-prem servers, AWS EC2, GCP GPU VMs, Azure NC/ND-series VMs
Orchestration	Kubernetes (with NVIDIA device plugin), Slurm, Ray, Nomad
Storage	High-speed parallel file systems (NVMe, EFS, FSx, Ceph)
Networking	High-bandwidth interconnects (InfiniBand, 100Gbps Ethernet)

Resource Scheduling Models

1. Static Allocation
   Assign specific GPUs to workloads.
   Simpler but underutilizes resources.
2. Dynamic Scheduling
   Pool of GPUs shared across workloads.
   Requires schedulers (K8s, Slurm, Ray).
3. Multi-Tenancy & Quotas
   Enforce GPU limits per user/team.
   Namespace-based GPU quota management in Kubernetes.
4. Priority & Preemption
   Schedule critical jobs with higher priority.
   Evict lower-priority workloads if needed.

Kubernetes GPU Scheduling

Prerequisites:

• NVIDIA GPU Drivers installed
• nvidia-device-plugin DaemonSet

Sample YAML for GPU Request:

apiVersion: v1
kind: Pod
metadata:
  name: gpu-pod
spec:
  containers:
  - name: cuda-container
    image: nvidia/cuda:11.0-base
    resources:
      limits:
        nvidia.com/gpu: 1 # request 1 GPU

Key Tools:

• Kueue – workload queueing for batch workloads
• Volcano – batch job scheduler
• Kubeflow – ML workflows with GPU support

Monitoring & Optimization

Tool	Purpose
NVIDIA DCGM	GPU telemetry and health
Prometheus + Grafana	Resource metrics dashboard
nvidia-smi	CLI for real-time GPU usage
Kubecost	GPU cost tracking and forecasting
MIG (Multi-Instance GPU)	Partition A100s into logical GPUs

💡 Best Practices

• Use MIG for fine-grained isolation.
• Auto-scale GPU nodes with Karpenter or Cluster Autoscaler.
• Use mixed precision (FP16/BF16) to reduce compute time.
• Separate training and inference nodes for cost efficiency.
• Implement GPU quota policies per team/project.

Cloud Provider Considerations

Cloud	Key Features
AWS	EC2 P4/P5/A10G, SageMaker, FSx Lustre
GCP	T4/V100/A100 support, Vertex AI
Azure	NC/ND-series, AKS + GPU Pools

Experiment Management

• Use MLFlow or Weights & Biases for logging GPU-based experiments.
• Label GPU-intensive jobs for visibility (job-type=gpu)
• Leverage node affinity/taints to isolate GPU workloads.

🧠 Learn here

What is an LLM, a.k.a Large Language Model?

A Large Language Model is a transformer-based neural network trained on massive corpora of textual data to perform various NLP tasks. These models predict the next word in a sentence, generate responses, summarize content, translate languages, and more.

In Simple words:

• Large Language Models (LLMs) are advanced deep learning models trained on vast amounts of text data
• They can understand, generate, and manipulate human-like language

Core Concepts

1. Transformers
   Introduced by Vaswani et al. in 2017
   Uses self-attention mechanisms
   Enables parallelization during training
2. Tokenization
   Text is broken down into smaller units (tokens)
   Byte Pair Encoding (BPE), WordPiece, SentencePiece used
3. Pretraining and Fine-Tuning
   Pretraining: Unsupervised learning on large corpora
   Fine-tuning: Supervised training on specific tasks
4. Embeddings
   Converts tokens to dense vectors
   Captures syntactic and semantic meaning
5. Attention Mechanism
   Allows model to focus on relevant parts of input
   Scaled dot-product attention is key

Architecture

• Embedding Layer: Converts token IDs into vectors
• Transformer Blocks: Stack of attention + feedforward layers
• Language Head: Outputs probabilities over vocabulary

Training Process

• Collect Large Text Corpus
• Tokenize Input
• Train with Masked Language Modeling or Causal LM
• Apply Optimization Techniques (Adam, Learning Rate Schedulers)
• Fine-tune on downstream tasks

Popular LLMs

Model	Developer	Parameters	Notable Feature
GPT-3	OpenAI	175B	Few-shot learning
PaLM 2	Google	540B	Multilingual, reasoning
LLaMA 2	Meta	7B-65B	Open-weight LLMs
Claude	Anthropic	Proprietary	Constitutional AI
Mistral	Mistral AI	7B	Efficient, open-weight
Falcon	TII	7B-180B	High performance in open LLMs

Model Sizes, Training Requirements, and Memory Needs

Model Size	Parameters	Training Data Size	GPU Requirements	Memory (VRAM) Needed
Small	~125M	~10GB	Single GPU (e.g., RTX 3090)	~8-16 GB
Medium	~1.3B	~50GB	2-4 GPUs (A100/3090)	~24-40 GB
Large	~6-13B	~300GB	8+ A100 GPUs	80+ GB
XL	30B+	500GB+	16-32 A100 GPUs	160+ GB
XXL	65B-540B	1TB+	TPU Pods / Supercomputers	500 GB+

Use Cases

• Text summarization
• Chatbots & conversational agents
• Code generation
• Sentiment analysis
• Translation
• Content creation
• Legal/medical document analysis

Logging, Monitoring, and Scaling LLM Inference

Deploying LLMs in production environments demands robust observability and elastic scalability.

Logging LLM Inference

Key Aspects to Log

Metric	Description
Request Metadata	Timestamp, user ID, session ID, request ID
Input Prompts	(Masked/anonymized) user prompts
Model Version	Track changes over deployments
Response Latency	Time from request to model output
Tokens Used	Prompt + completion token count
Errors/Failures	Timeout, 5xx, invalid prompt, token limit exceeded

Logging Tools

• OpenTelemetry + Fluent Bit: Unified telemetry and log pipelines
• ELK Stack (Elasticsearch, Logstash, Kibana)
• Cloud-native solutions: CloudWatch, GCP Logging, Azure Monitor

{
  "timestamp": "2025-08-02T10:32:00Z",
  "model": "gpt-4",
  "prompt": "Translate to French: 'Hello'",
  "response_time_ms": 218,
  "tokens": 15,
  "status": "success"
}

Monitoring LLM Inference

Metrics to Track

Category	Key Metrics
Performance	Inference latency, throughput (req/s), queue time
System Health	GPU/CPU utilization, memory usage, disk I/O
Model Behavior	Token counts, prompt lengths, temperature/output stats
Error Monitoring	4xx/5xx status codes, timeout rate, fallbacks

Monitoring Stack

• Prometheus + Grafana
• NVIDIA DCGM Exporter (GPU metrics)
• OpenTelemetry Metrics Collector
• Sentry / Datadog (for alerting & error tracking)

Scaling LLM Inference

1. Horizontal Scaling

• Add more inference pods/containers
• Use Kubernetes Horizontal Pod Autoscaler (HPA)
• Metrics: CPU/GPU load, QPS, latency

2. Vertical Scaling

• Allocate more GPU/CPU resources per replica
• Upgrade to high-memory instances or A100/H100

3. Model Optimization

Technique	Benefit
Quantization	Reduce memory and speed up inference
vLLM/TGI	Efficient transformer inference engine
Batch Inference	Serve multiple requests in one pass
Async Pipelines	Reduce bottlenecks using asyncio/queues

4. Load Balancing

• Use service mesh (Istio/Linkerd) or K8s Ingress + HPA
• Ensure intelligent request routing to least-loaded replica

Cost-Saving Strategies for LLM Inference – Distillation, Quantization, and Caching

Running large language models (LLMs) in production can be expensive due to compute, storage, and latency demands. Let's explore three effective strategies to reduce cost while maintaining acceptable performance: Model Distillation, Quantization, and Caching.

1. Model Distillation

Train a smaller "student" model to replicate the behavior of a larger "teacher" model by mimicking its outputs.

Benefits

• Reduced model size (2x–20x smaller)
• Faster inference
• Lower compute/GPU requirements

Use Cases

• On-device applications
• Low-latency chatbots
• Edge deployments

Tools

• Hugging Face Transformers (distilBERT, TinyBERT)
• Knowledge Distillation libraries (e.g., DistilLLM, TinyStories)

# Example: Teacher-student setup with HuggingFace
from transformers import AutoModelForSequenceClassification
teacher_model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased")
student_model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased")

2. Model Quantization

Reduce the precision of model weights from 32-bit floating point (FP32) to 8-bit or 4-bit integers (INT8, INT4).

Benefits

• Smaller memory footprint (up to 4x reduction)
• Faster inference speed
• Lower power and storage usage

Trade-offs

• Slight reduction in accuracy
• Quantization-aware training improves results

Tools

• bitsandbytes, Optimum, TensorRT, ONNX Runtime
• Hugging Face Transformers integration

from transformers import BitsAndBytesConfig
bnb_config = BitsAndBytesConfig(load_in_8bit=True)
model = AutoModelForCausalLM.from_pretrained("gpt2", quantization_config=bnb_config)

3. Caching Inference Results

Store and reuse responses to frequent or identical queries to avoid redundant inference.

Benefits

• Zero computation for cache hits
• Reduces token usage and latency

Use Cases

• Chatbots answering FAQs
• Repeated prompt inputs
• Search/autocomplete services

Types of Caching

Type	Example
Prompt-Response	Full response caching for known prompts
Output Tokens	Cache token sequences during generation
Embedding Cache	Cache vector results in RAG pipelines

Tools

• Redis, Memcached
• Faiss/Weaviate for embedding caches
• Custom prompt-hash cache layers

Combined Strategy Table

Strategy	Savings Potential	Accuracy Impact	Infra Benefit	Best For
Distillation	✅✅✅	Moderate	Smaller model sizes	Edge/real-time inference
Quantization	✅✅	Low/none (w/ Aware)	Faster, smaller	Consumer GPUs, memory-constrained
Caching	✅✅✅	None	Bypasses model call	FAQs, autocomplete, search, RAG

🧠 Best Practices

• Use quantized models + caching for high-volume endpoints.
• Perform benchmarking after distillation/quantization.
• Regularly update caches and avoid stale content.
• Track hit/miss ratio in caching layer.

Serving LLMs with vLLM, Hugging Face Inference, OpenLLM, and Ray Serve

Efficient LLM deployment requires the right tooling for performance, scalability, and ease of integration. Let me explain a practical comparison and usage guide for four popular tools: vLLM, Hugging Face Inference Endpoints, OpenLLM, and Ray Serve.

Tool Comparison Table

Tool	Strengths	Use Cases	Infra Needs
vLLM	Fast throughput, paged attention, multi-models	High-load inference APIs	GPU, Kubernetes
Hugging Face Inference	Fully managed, easy to use	Hosted inference, zero ops	HuggingFace Infra
OpenLLM	Open-source, REST/gRPC, LoRA adapter support	On-prem, containerized deployments	Docker/K8s
Ray Serve	Scalable actor-based deployment	Python-first multi-model microservices	Ray Cluster / K8s

vLLM (via vllm.ai)

Features

• Transformer engine optimized for LLMs
• Efficient batch serving
• Paged attention for long context support

Installation

pip install vllm

Launch Example

python -m vllm.entrypoints.openai.api_server \
  --model facebook/opt-6.7b \
  --port 8000

Hugging Face Inference Endpoints

Features

• Fully managed GPU endpoints
• Deploy from HF UI or CLI
• Auto-scaling and observability included

Example Deployment

# From CLI
huggingface-cli endpoint create \
  --model-id meta-llama/Llama-2-7b-chat-hf \
  --instance-type a10g.large \
  --name llama2-api

Integration

from huggingface_hub import InferenceClient
client = InferenceClient(model="meta-llama/Llama-2-7b-chat-hf")
response = client.text_generation("What is Ray Serve?")

OpenLLM (by BentoML)

Features

• REST and gRPC API support
• LoRA adapter integration
• Model server CLI with YAML config

Install and Serve

pip install openllm
openllm start dolly-v2 --model-id databricks/dolly-v2-3b

Deploy with BentoML

bentoml build && bentoml containerize openllm/dolly-v2

Ray Serve

Features

• Actor-based architecture for distributed serving
• Built-in scaling and fault-tolerance
• Python-first deployment via decorators

Basic Example

from ray import serve
from fastapi import FastAPI

app = FastAPI()

@serve.deployment(route_prefix="/generate")
@serve.ingress(app)
class LLMService:
    async def __call__(self, request):
        prompt = await request.json()
        return {"output": "Generated: " + prompt["text"]}

serve.run(LLMService.bind())

When to Use What?

Scenario	Recommended Tool
Fully managed, hosted solution	Hugging Face Inference
Local inference with multi-GPU efficiency	vLLM
Containerized, open-source API deployment	OpenLLM
Pythonic microservice model deployment	Ray Serve

📊 Summary

Today you've learned how to professionally manage large language models (LLMs) in production environments, covering the complete lifecycle from deployment to optimization.

🎯 Key Takeaways

• LLMs have unique requirements for GPU memory, batch processing, and long-context handling
• Inference optimization involves quantization, caching, and efficient serving frameworks
• Deployment strategies range from cloud APIs to containerized on-premise solutions
• Fine-tuning with LoRA/PEFT enables cost-effective model customization
• Comprehensive monitoring includes latency, token usage, and GPU utilization metrics
• Cost optimization through distillation, quantization, and intelligent caching
• Production-ready serving with vLLM, Ray Serve, OpenLLM, or Hugging Face

🔧 Production Considerations

Aspect	Key Focus Areas
Performance	Batch inference, GPU utilization, response latency
Scalability	Horizontal/vertical scaling, load balancing, auto-scaling
Cost Management	Model compression, caching, efficient hardware utilization
Observability	Comprehensive logging, monitoring, alerting, and error tracking
Reliability	Fault tolerance, graceful degradation, backup strategies

💡 Best Practices Recap

• Start small: Begin with smaller models and scale up based on requirements
• Optimize first: Apply quantization and caching before scaling hardware
• Monitor everything: Track both technical metrics and business outcomes
• Plan for costs: LLM inference can be expensive—optimize early and often
• Security focus: Implement proper authentication, rate limiting, and data privacy

🔥 Challenges

• Use a Hugging Face model like distilGPT2 or flan-t5-small to generate output
• Run quantized LLM using bitsandbytes or AutoGPTQ
• Deploy the model as a container using vLLM or OpenLLM on local Docker
• Enable batching for inference to handle multiple requests
• Fine-tune a small LLM using LoRA + PEFT with your own dataset
• Set up CI/CD pipeline to auto-update a deployed LLM model (GitHub Actions + Hugging Face repo or S3)
• Add observability: track latency, input length, token count per request
• Deploy LLM inference to GPU-backed Kubernetes pod (EKS/GKE) using Ray Serve or KServe
• Write and test 5 different prompts and compare responses
• Log prompt + response to file or JSON store