How to Use Llama 5 70B for Private Document Q&A

You can build a fully offline document Q&A system on Llama 5 70B using Ollama, a local embedding model, and a small vector store — no document ever leaves your machine. That matters for legal, medical, and internal-security teams who can’t send files to a hosted API.

Here’s the stack, the VRAM budget, and the parts that quietly break.

Why Llama 5 70B Instead of a Hosted API?

Compliance, mostly. Sending a client contract to a cloud endpoint can violate a data-processing agreement. With Llama 5 70B running locally, the entire pipeline — embeddings, retrieval, generation — stays on your hardware.

You also get predictable cost. A 70B model at 4-bit needs about 40GB of VRAM once, versus ongoing per-token fees.

What Is the Minimum Hardware?

You need roughly 40GB of VRAM for the Q4 model plus headroom for the context cache. A single RTX 4090 with 24GB is not enough — you need two, or an M3 Max with 64GB unified memory.

Component	VRAM
Llama 5 70B (Q4_K_M)	~40 GB
Embedding model (nomic-embed-text)	~1 GB
8k context cache	~4 GB
Buffer	~5 GB

How Do You Build the Pipeline?

Four stages, all local.

1. Chunk. Split documents into 500-token pieces with 50-token overlap. Overlap prevents answers from being cut mid-sentence.
2. Embed. Use a local embedding model like nomic-embed-text via Ollama.
3. Store. Drop vectors into Chroma or Qdrant running in Docker.
4. Answer. Retrieve the top 5 chunks and pass them to Llama 5 70B with a strict instruction to answer only from context.

from langchain_ollama import ChatOllama
llm = ChatOllama(model="llama5:70b", num_ctx=8192)
# retrieved_chunks from your vector store
prompt = "Answer only from the context below.\n" + "\n".join(chunks) + "\nQ: " + question

Where Does It Break?

Three failure modes show up again and again. First, context overflow: 70B at 4-bit with long chunks can push past VRAM and fall back to CPU, tanking speed to 2 tokens per second. Cap retrieved chunks at 5.

Second, hallucination on missing answers. If the answer isn’t in the retrieved chunks, the model often invents one. Add an explicit “if the answer is not present, say ‘not found'” rule and test it.

Third, cold starts. Loading 40GB weights takes 30-60 seconds. Keep the model resident with OLLAMA_KEEP_ALIVE=-1.

Is This Better Than a Cloud RAG?

For confidentiality, yes. For raw accuracy, a frontier hosted model still wins on messy tables and scanned documents. Llama 5 70B handles clean text well; it struggles with blurry scans unless you add an OCR step first.

Suits regulated industries and anyone with a beefy GPU. If you only have a laptop, run the 8B for drafting and route only sensitive searches locally.

Quick Summary

• Llama 5 70B enables offline document Q&A for compliance-sensitive data.
• You need about 40GB VRAM for the Q4 model plus cache headroom.
• The stack: Ollama + local embeddings + Chroma, all on-device.
• Retrieve 5 chunks max and force a “not found” answer rule.
• Keep the model resident to avoid 30-60 second cold starts.