After experimenting with LLMs for chatbots, data analytics, and content summarization across environments, Here’s what I gathered for an appropriate LLM setup:
1) Local LLMs (Experimented with Python using GPT4All with Orca mini on mac M3)
Use For:
Experimentation, learning and POCs
Working offline or in secure environments
Handling sensitive data (e.g., finance, healthcare)
A no-cost, self-contained setup on M1/M2/M3//M4
Full control over where data lives for HIPAA, GDPR
Limitations:
Slower inference on local
Manual model updates, no built-in scaling
Obviously not designed for multiple concurrent users or serving over the web without additional orchestration
Tips:
Use 4-bit quantized models (GGUF) for faster inference on CPU even for larger models like Llama 3
Automate model updates with scripts to avoid manual downloads
Add validation layers with schema checking/fact checking/rule filters/secondary model validation/RAG (Retrieval-Augmented Generation)
to reduce hallucinations and error scenarios.
While CPUs can run LLMs, dedicated GPUs (or Apple Silicon's unified memory) provide a substantial speedup.
2) EC2 + LLMs (Experimented with a Dockerized Python app using Ollama with Mistral 7B, Llama 3 running on a spot instance)
Use for:
More compute (GPU/RAM) than laptop
Running larger models privately (e.g., LLaMA 3 13B)
Building custom tools or secure internal pipelines
Full control over where data lives for HIPAA, GDPR
Limitations:
Infrastructure setup, scaling, and cost management
Tips:
Use Spot Instances to cut costs by up to 90%
Use Reserved Instances for predictable, long-running workloads, and Savings Plans for flexible cost savings.
Package with Docker or vLLM for easier setup & reproducibility
Store models in EFS/S3 to share across instances, avoid redownloads
Can scale horizontally with auto-scaling groups and load balancers for larger internal teams.
3) Amazon Bedrock (Experimented with Python running on Lambda and using AWS bedrock models -
amazon.titan-embed-text-v2:0 - for generating embeddings, amazon.titan-text-express-v1 - for text generation)
Use for:
Developing apps with managed model agnostic APIs (Claude, Mistral, Titan)
Enterprise-grade security, IAM, and uptime
Scalable for customer-facing or API-heavy workloads - no need to manage concurrency limits or infrastructure.
Limitations:
Usage-based costs, limited access to model internals means less control over quantization, direct access to weights, etc.
Fine-tuning is limited but possible for Titan models.
Tips:
Use VPC endpoints to restrict data to the network
Apply RAG to reduce hallucinations, add context
Use Guardrails to enforce safe outputs and compliance
Monitor via CloudWatch to track usage and avoid cost spikes
Supports regional compliance when paired with VPC and IAM boundaries.
Provides Data encryption at rest (KMS) and in transit, and ensures least-privilege access for Bedrock-related roles.
Handles rate limiting (e.g., exponential backoff, request queuing) for production applications
SageMaker supports fine-tuning and hosting of open-source LLMs. Can use it when full control over training loops,
advanced MLOps, or cost-efficient multi-model endpoints is needed. Ideal for custom enterprise LLM pipelines beyond Bedrock’s prebuilt APIs.
4) OpenAI APIs (GPT-4o, GPT-3.5) (Experimented with Java and Python apps calling OpenAI APIs for text and image generation)
Use for:
Best-in-class reasoning and multimodal support for complex problem solving, code generation, logical deduction and strategic thinking
Zero infrastructure preference
Rapid prototyping and app development
Building stateful, conversational applications with persistent threads, function calling, and knowledge retrieval,
the Assistants API simplifies much of the orchestration.
Limitations:
Per-token pricing, privacy, and rate limits
Fine-tuning is only available for GPT-3.5 and smaller models - not GPT-4
Tips:
Use caching (e.g., Redis) to avoid repeated prompts and save cost
Prefer GPT-3.5 for simple tasks for cheaper, good enough for many use cases
Add RAG to supply facts instead of relying on memory
Opt out of data logging or sign a DPA to enhance privacy
OpenAI has region-specific data controls, but full residency control is limited.
Can use function calling to interact with external tools and APIs for building intelligent agents and automations.
Handles rate limiting (e.g., exponential backoff, request queuing) for production applications
Summary
No internet or high privacy => Local LLM (e.g., Ollama, GPT4All)
Private high-performance inference => EC2 + LLMs
Scalable managed APIs with security => Amazon Bedrock
Best reasoning, no infra needed => OpenAI APIs
Custom training & hosting => SageMaker
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