Optimize Document Processing and RAG Pipelines
Find the best models for document analysis, summarization, and retrieval-augmented generation (RAG). Compare context lengths, embedding models, and GPU requirements for document processing at scale.
Key Considerations
- ‣Long context windows (128K+) are essential for processing full documents without chunking.
- ‣Pair a large LLM with a fast embedding model for efficient RAG pipelines.
- ‣KV-cache memory grows linearly with context length — budget extra VRAM for long documents.
- ‣Consider quantized models (FP8/INT4) to fit longer context windows in available VRAM.
Recommended Models
| Model | Parameters | Context | VRAM (BF16) | Cheapest $/M Out | Est. Monthly Cost |
|---|---|---|---|---|---|
| DeepSeek R1 Distill 70B DeepSeek | 70.6B | 131K | 141 GB | $0.88 | $176via together |
| Llama 3 70B 1M Context Gradient | 70.6B | 1049K | 141 GB | $1.50 | $300via gradient |
| Llama 3.1 70B Meta | 70.6B | 131K | 141 GB | $0.79 | $146via groq |
| Llama 3.3 70B Meta | 70.6B | 131K | 141 GB | $0.79 | $146via groq |
| Hermes 3 70B Nous Research | 70.6B | 131K | 141 GB | $0.88 | $176via together |
| HelpSteer2 Llama 3.1 70B NVIDIA | 70.6B | 131K | 141 GB | $0.50 | $100via nvidia-nim |
| Llama 3.1 Nemotron 70B Instruct NVIDIA | 70.6B | 131K | 141 GB | $0.88 | $176via together |
| Llama 3.1 Nemotron 70B Reward NVIDIA | 70.6B | 131K | 141 GB | $0.50 | $100via nvidia-nim |
| Nemotron 70B NVIDIA | 70.6B | 131K | 141 GB | $0.88 | $176via nvidia |
| Llama 3.1 70B Turbo Together AI | 70.6B | 131K | 141 GB | $0.88 | $176via together |
| Claude Sonnet 4 Anthropic | 70B | 200K | 140 GB | $15.00 | $2280via anthropic |
| o1-mini OpenAI | 70B | 128K | 140 GB | $12.00 | $1860via openai |
* Monthly cost estimated at 200M tokens/month (30% input, 70% output split) using cheapest available provider.
Recommended GPUs
Cost Estimation
Low Volume
$10/mo
20M tokens via API
Medium Volume
$100/mo
200M tokens via API
High Volume
$500/mo
1000M tokens via API
Estimates based on average output token pricing across providers. Use the calculator for precise estimates →
Frequently Asked Questions
What model is best for document analysis?
Llama 3.1 70B (128K context), Qwen 2.5 72B, and DeepSeek V3 are excellent for document analysis. For RAG, pair with an embedding model like E5-Mistral-7B for retrieval. Choose models with 128K+ context windows for full-document processing.
How much does document processing cost with LLMs?
Processing a 50-page document (~25K tokens) costs $0.01-0.25 per document depending on the model. At scale (10K documents/month), budget $100-2,500/month via API, or self-host for unlimited processing at fixed GPU cost.
What is RAG and why does it matter?
RAG (Retrieval-Augmented Generation) combines document search with LLM generation. Instead of processing entire documents, RAG retrieves relevant chunks and feeds them to the LLM. This reduces cost, improves accuracy, and works with any document corpus size.
How much VRAM do I need for long-context processing?
Long context dramatically increases KV-cache memory. A 70B model at 128K context needs 80-160 GB VRAM (BF16). Using FP8 quantization and PagedAttention (via vLLM) can reduce this by 40-50%. An H100 80GB or multi-GPU A100 setup is recommended.