09 Apr Pick the Right Open LLM for Private Data Exploration in Omniscope

Speed, Cost, Quality: How to pick the Best Open Source LLM for Private, Self-Hosted Visual Data Exploration in Omniscope.

At Visokio, we’ve been exploring how large language models (LLMs) can power more intelligent, conversational data exploration in Omniscope. The result? Report Ninja — a natural language assistant that helps users explore data and create visual reports just by asking.

We wanted to ensure Report Ninja could also run in privacy-sensitive environments, where sending data to external APIs isn’t an option. That led us to test a range of open-source LLMs that can be self-hosted on cloud or on-premise infrastructure — giving organisations full control over data, performance, and cost.

If you’re a CTO, data scientist, or ML engineer looking to integrate LLMs into your analytics product — or just curious how to run them efficiently — this post summarises our findings.

Note: We’re not against commercial APIs. In fact, OpenAI’s new GPT-4o model delivers outstanding results — it’s the best commercial model we tested. But open-source options offer more flexibility for private deployments, and that’s what we focused on here.

⚡ TL;DR — What We Learnt

✅ You can run open-source LLMs privately inside Omniscope — no cloud API needed.

💾 Quantised 4-bit models strike the best balance between speed, accuracy, and cost.

🧠 Structured outputs are faster and more reliable when using grammar constraints.

🎮 A single 80GB H100 GPU can run even 70B models when quantised.

🧵 Multiple GPUs help with throughput, not with single prompt speed.

🛠️ The Goal: Structured, Fast, Private Visual Exploration

Omniscope’s Report Ninja translates user queries into structured JSON instructions to generate visual reports. So we prioritised:

⚡ Speed — under 10 seconds is ideal

✅ Accuracy — no hallucinated columns or logic

🧱 Structure — valid JSON output is essential

💰 Cost — needs to scale without breaking the budget

To meet these, we tested a range of open-source models and GPU setups using both Llama.cpp and vLLM.

🧮 Model Size, Quantisation, and Hardware

We focused on DeepSeek’s 70B LLaMA-distilled model, in both full precision (bf16) and 4-bit quantised formats (GGUF, AWQ).

Key Observations:

• 4-bit quantised models are dramatically more efficient and still high quality for structured tasks.
• A single H100 (80GB) is enough for excellent performance.
• Multi-GPU setups didn’t improve latency — they help with concurrent requests and longer contexts.

🎭 Thinking vs Grammar — Speed vs Intelligence

We explored how model “thinking” (reasoning out loud) affects speed and quality, and how grammar constraints (like JSON schema or GBNF) help.

• Grammar constraints ensure valid JSON, reduce hallucinations, and work well without thinking.
• “Thinking” mode improves reasoning but slows down responses — sometimes by 2×.
• Combining both would be ideal, but currently broken in vLLM.

🔧 Frameworks Compared: Llama.cpp vs vLLM

Llama.cpp (70B, 4-bit GGUF)

• ✅ GBNF grammar works with no slowdown.
• ⚡ “Split mode row” gave ~30% faster responses.
• Multi-GPU behaviour was mixed — unclear improvements for single queries.
• Good option for private, stable, structured inference.

vLLM (70B, full + 4-bit AWQ)

• ✅ JSON schema output is now fast and reliable (with thinking disabled).
• ❌ GBNF grammar mode is currently broken (major slowdown).
• 🧠 Minor hallucinations in thinking mode without grammar (~13–17s responses).
• ⚖️ No speed boost with 4 GPUs vs 1 for single prompts.

🧪 Other Models We Tried

💸 Cost and Setup Notes

• 1× H100: ~$2/hr
• 4× H100: ~$8/hr
• Setup time: 10–20 mins (some automatable)

Best value: 70B 4-bit quantised model on 1× H100 — fast, private, and production-ready.

🎯 Final Thoughts

Open-source LLMs are now viable, fast, and affordable for self-hosted, privacy-conscious deployments. In Omniscope, we’ve successfully integrated support for local models, giving customers control over their data and infrastructure.

Still, when privacy isn’t a concern and you want peak reasoning and fluency, OpenAI’s GPT-4o remains the most impressive commercial model we’ve tested — especially for natural language fluency and multi-modal tasks.

But if you want to keep everything in-house, open-source LLMs are ready. With the right setup, you can have speed, control, and accuracy — all running privately on your own hardware.

💬 Curious about LLM-powered workflows in Omniscope? Thinking of going private?
We’d love to chat.