Can you recommend a self-hosted model for air-gapped inference in a government contractor environment?

Yes — for an air-gapped, self-hosted government-contractor environment, I’d recommend choosing a model based on three things:

1. No dependency on external APIs
2. Permissive licensing / clear provenance
3. Strong performance on general enterprise tasks like summarization, drafting, extraction, and internal Q&A

Good model recommendations

1) Llama 3.1 Instruct

• Best all-around choice for many private deployments
• Strong general reasoning and instruction following
• Available in sizes like 8B, 70B, and larger
• Works well for:
  • document summarization
  • policy QA
  • drafting
  • code assistance
  • internal knowledge assistants

Why it’s a good fit: broad tooling support, good quality, widely deployed, and can run fully offline once weights are installed.

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2) Mistral / Mixtral Instruct

• Mistral 7B Instruct is efficient and easy to deploy
• Mixtral 8x7B Instruct gives much better capability if you have the hardware
• Good for:
  • enterprise chat
  • retrieval-augmented generation (RAG)
  • lightweight agent workflows

Why it’s a good fit: strong performance-to-cost ratio, often easier to serve in constrained environments.

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3) Qwen2.5 Instruct

• Very strong quality across reasoning, multilingual tasks, and coding
• Comes in many sizes, including smaller ones suitable for on-prem inference
• Good for:
  • structured extraction
  • multilingual document handling
  • coding and technical support
  • general assistant usage

Why it’s a good fit: excellent capability for the size, especially if you need flexibility.

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If you need a smaller footprint

If hardware is limited or you need higher throughput:

• Llama 3.1 8B Instruct
• Mistral 7B Instruct
• Qwen2.5 7B Instruct

These are often the best starting points for air-gapped environments because they can be quantized and run efficiently on a single or small number of GPUs.

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If you need higher-quality reasoning

If you have the compute budget:

• Llama 3.1 70B Instruct
• Mixtral 8x7B Instruct
• Qwen2.5 32B / larger variants

These are better for more complex analysis, longer document synthesis, and higher-stakes internal workflows.

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Deployment suggestions for air-gapped use

For offline serving, common choices are:

• vLLM — high throughput on GPUs
• Text Generation Inference (TGI) — production-grade serving
• Ollama or llama.cpp — easier setup, especially for smaller models or quantized deployments

Practical recommendation

If you want a single default recommendation:

Start with Llama 3.1 8B Instruct for pilot deployment, and scale to Llama 3.1 70B Instruct or Mixtral 8x7B if you need better quality and have the hardware.

Important government-contractor considerations

Before deployment, review:

• model license terms
• export/control restrictions
• data retention and logging policy
• whether the model weights are approved for your environment
• supply-chain verification of the downloaded artifacts

If you want, I can also recommend:

• the best model for specific hardware you have,
• a FedRAMP-style deployment pattern,
• or a comparison table of Llama vs Mistral vs Qwen for air-gapped use.