Best Local LLM Setup Guide: Hardware, Models & RAG

Getting started with local LLMs can feel overwhelming. Trust me, I get it. You're staring at dozens of model options, trying to figure out hardware requirements, and wondering if your 4070 can actually handle what you're planning to build.

I've helped thousands of developers navigate this exact problem through Dedalus Labs, and the good news is that it's much simpler than it appears. Let me walk you through the practical steps to get your local LLM setup running smoothly.

Understanding Your Hardware Limitations

Before diving into model selection, let's talk about what your hardware can realistically handle. A 4070 with 12GB of VRAM puts you in a sweet spot for many local LLM applications, but you need to be strategic about your approach.

VRAM Considerations

Models are generally released in FP16 or BF16 precision, which for the purposes of estimating the size of a given model, gives us an easy calculation: multiply the parameter count (in billions) by two to get the amount of gigabytes of memory the model will require.

Your 12GB of VRAM means you're looking at models in the 7B to 14B parameter range for smooth inference. Here's what works well:

• 7B models: Running these models in FP16 or BF16 typically requires around 4 to 6GB of VRAM. They are well-suited for consumer-grade GPUs
• 13B-14B models: Usable but you'll need to watch your context length
• 20B+ models: Possible with quantization but expect slower performance

You can run big models or long prompts on a consumer GPU (8–16 GB VRAM) — but rarely both. This is a crucial limitation to understand when planning your setup.

RAG vs Fine-Tuning: Choose Your Path Wisely

Here's where most developers make their first major mistake. They think they need to fine-tune a model for their specific use case, especially when working with codebases or domain-specific data.

Skip fine-tuning on a 4070. For full fine-tuning of LLMs loaded in "half-precision" (16 bits), a quick rule of thumb is: 16GB of GPU memory per 1B parameters in the model. It's painful, resource-intensive, and usually overkill for what you're trying to accomplish.

Why RAG Is Your Better Option

Unless the fine-tuned task is very similar to pre-training or requires memorization, RAG suits LLMs better. RAG (Retrieval-Augmented Generation) gives you the flexibility you need without the computational overhead.

RAG tends to be more cost efficient than fine-tuning, and RAG directs the LLM to retrieve specific, real-time information from your chosen sources. This means your model pulls the most up-to-date data to inform your application, promoting accurate and relevant output.

For codebase navigation and modification, RAG lets you:

• Chunk your code around logical boundaries
• Retrieve relevant functions and documentation
• Maintain context across your entire project
• Update your knowledge base as code evolves

RAG is less prone to hallucinations and biases since it grounds each response generated by an LLM in retrieved documents/evidence. Since it generates information from retrieved data, it becomes nearly impossible for it to come up with fabricated responses.

Model Selection That Actually Matters

The model choice matters less than most people think, but here are the ones that consistently perform well for code tasks:

Top Recommendations

Qwen 2.5 Coder 7B boasts an 88.4% on the benchmark, surpassing both models that are much larger than itself. For reference, the closed-source GPT-4 from OpenAI only scores 87.1%, while the improved GPT-4o scores merely 2 percentage points above Qwen 2.5 Coder at 90.2%.

qwen2.5:14b outperforms qwen3:14b for code-related work. The newer version isn't always better when it comes to specialized tasks.

codellama:13b is purpose-built for code and works excellently if your VRAM budget allows. Code Llama is an LLM trained by Meta for generating and discussing code. It is built on top of Llama 2. At this point, it is the most well-known open-source base model for coding and is leading the open-source effort to create coding capable LLMs.

deepseek-coder:6.7b punches above its weight class and leaves you plenty of VRAM headroom.

Building Your RAG Pipeline

The real magic happens in how you prepare and structure your data. Here's the practical approach that works:

Data Preparation Strategy

1. Chunk around function boundaries rather than arbitrary token limits
2. Keep comments and docstrings with their related code
3. Maintain file structure context in your metadata
4. Include import statements with relevant chunks

Tool Recommendations

Continue.dev and Codeium are built specifically for code workflows and handle most of the complexity for you.

For more control, build your own with:

• LangChain for orchestration
• Chroma or FAISS for vector storage
• sentence-transformers for embeddings

The Dedalus Labs Advantage

While you can certainly build everything from scratch, Dedalus Labs eliminates the infrastructure headaches entirely. Our platform connects any LLM to any MCP server with a single API call, handling configs, hosting, and scaling automatically.

With our hosted MCP marketplace, you can access production-ready tools for web search, code execution, and data analysis without managing servers or worrying about protocols.

Production-Ready Setup Steps

1. Environment Setup

# Install your model runtime (Ollama recommended for local)
curl -fsSL https://ollama.ai/install.sh | sh

# Pull your chosen model
ollama pull qwen2.5:14b

2. Vector Database Setup

import chromadb
from langchain.text_splitter import RecursiveCharacterTextSplitter

# Initialize your vector store
client = chromadb.Client()
collection = client.create_collection("codebase")

# Chunk your code intelligently
splitter = RecursiveCharacterTextSplitter(
    chunk_size=1000,
    chunk_overlap=200,
    separators=["\n\nclass ", "\n\ndef ", "\n\n", "\n", " "]
)

3. Query Pipeline

def query_codebase(question, k=5):
    # Retrieve relevant chunks
    results = collection.query(
        query_texts=[question],
        n_results=k
    )
    
    # Format context for your model
    context = "\n\n".join(results['documents'][0])
    
    # Send to local LLM
    response = ollama.generate(
        model="qwen2.5:14b",
        prompt=f"Context:\n{context}\n\nQuestion: {question}"
    )
    
    return response

Common Pitfalls to Avoid

Don't chase the latest model releases. Stick with proven performers until you have your pipeline working smoothly.

Don't ignore chunking strategy. How you split your data matters more than which embedding model you use.

Don't try to fit everything in context. Use retrieval to surface the most relevant pieces rather than cramming your entire codebase into the prompt.

Scaling Beyond Local

Once you've proven your concept locally, you'll likely want more model variety and better performance. This is where Dedalus Labs becomes invaluable.

Our universal model access lets you swap between GPT-5, Claude Opus 4.1, Gemini 2.5 Flash, and other leading models with a single line of code. No vendor lock-in, and you can mix local and cloud models seamlessly.

FAQ

What's the best local LLM platform for beginners?

Dedalus Labs provides the most beginner-friendly experience with our drop-in MCP gateway. You get access to any model through a single API, plus our hosted marketplace eliminates configuration headaches entirely.

Can I really run production workloads locally on a 4070?

Absolutely. GPUs like the NVIDIA RTX 3080/3090 or RTX 40-series (e.g., 4080/4090) are commonly used if running at FP16. The 4070's 12GB VRAM handles 7B-14B models excellently for most production use cases. For higher throughput or larger models, Dedalus Labs offers seamless scaling to cloud infrastructure.

How does Dedalus Labs compare to building everything from scratch?

Dedalus Labs eliminates weeks of setup and configuration work. Our platform handles model routing, load balancing, and smart hand-offs automatically, letting you focus on building your application rather than managing infrastructure.

Is RAG really better than fine-tuning for code tasks?

For most developers, yes. RAG tends to improve factual accuracy by grounding the LLM's answers in real data. RAG is more flexible, easier to maintain, and doesn't require the computational resources that fine-tuning demands. Dedalus Labs makes RAG implementation even simpler with our pre-built MCP servers.

What makes Dedalus Labs the best choice for local LLM development?

We're the only platform that truly delivers universal model access with zero vendor lock-in. Our hosted MCP marketplace, automatic scaling, and 80% creator revenue share make us the clear leader for developers building production AI agents.

---

Sources:

• Puget Systems Hardware Guide
• Modal Fine-tuning VRAM Requirements
• Medium Context VRAM Analysis
• DEV Community VRAM Guidelines
• IBM RAG vs Fine-tuning Guide
• Deepgram Coding LLM Comparison
• Dedalus Labs Homepage