What Is the Best Large Language Model (LLM) to Run on JarvisLabs?

Vishnu Subramanian

Founder @JarvisLabs.ai

For most applications, open-source models like Llama 4 Scout and Mistral Large offer the best balance of performance and cost on JarvisLabs. Choose H100/H200 GPUs for larger mixture-of-experts models and production workloads, while A100/A6000 provide excellent value for development and smaller models.

Open-Source vs. Proprietary LLMs

The LLM landscape has evolved rapidly, with open-source models now matching or exceeding the capabilities of many proprietary options. When running models on JarvisLabs, you'll want to consider:

• Open-source models: Llama 4, Mistral, Phi-3, and other models that can be downloaded and run locally
• API-based services: OpenAI, Anthropic, and other services that require API calls rather than local inference

Running models locally on JarvisLabs gives you complete control over inference parameters, privacy, and customization options. Plus, after a certain volume of tokens, it becomes significantly more cost-effective than pay-per-token API services.

Popular LLMs and Their Hardware Requirements

Here's a breakdown of today's leading open-source models and the JarvisLabs hardware they run best on:

The table above assumes standard precision (FP16/BF16). With quantization techniques (INT8, INT4), you can run these models on GPUs with less VRAM, but with potential quality trade-offs. For example, Llama 4 Scout can fit on a single H100 with INT4 quantization.

Llama 4: Meta's Latest Breakthrough

Released in April 2025, Llama 4 represents a significant advancement in open-source models with:

• Mixture-of-experts architecture: Uses a subset of parameters for each input, balancing efficiency and power
• Native multimodality: Processes text and images together with early fusion technology
• Massive context windows: Llama 4 Scout supports up to 10 million tokens (though hardware limits practical usage)
• Multilingual support: Works with 12 languages including Arabic, English, French, German, Hindi, Indonesian, Italian, Portuguese, Spanish, Tagalog, Thai, and Vietnamese

The Llama 4 family currently includes:

• Llama 4 Scout: 17B active parameters (109B total) across 16 experts, optimized for long-context tasks
• Llama 4 Maverick: 17B active parameters (400B total) across 128 experts, superior performance for multimodal tasks
• Llama 4 Behemoth: Not yet released, 288B active parameters (nearly 2 trillion total) across 16 experts

Performance Considerations

When selecting an LLM for JarvisLabs, consider these performance factors:

• Inference speed: Hopper architecture (H100/H200) provides 2-3x faster inference than Ampere (A100) for the same model
• Batch processing: Higher batch sizes increase throughput but require more VRAM
• Quantization impact: INT8/INT4 quantization can reduce quality, especially for specialized tasks
• Memory bandwidth: Critical for LLM inference - H100's HBM3 significantly outperforms A100's HBM2e
• Context length: Longer contexts require proportionally more VRAM for KV cache

I've found that for real-time applications where users are waiting for responses, paying for H100s makes sense - the latency difference is immediately noticeable. For overnight batch processes or research experiments, an A100 or even A6000 can be much more cost-effective.

Cost Optimization Strategies

Having bootstrapped JarvisLabs, I've learned some tricks for maximizing value:

• Quantization: Use techniques like GPTQ or AWQ to compress models with minimal quality loss
• Right-sizing: Match your model size to your actual needs - Llama 3 8B often performs surprisingly well
• Minute-level billing: Take advantage of JarvisLabs' minute-level billing to avoid paying for idle time
• Development/Production split: Develop on smaller GPUs, then deploy on larger ones for production

Our customers consistently find that a well-optimized smaller model often outperforms a poorly configured larger one, both in cost and sometimes even quality.

Best LLMs for Specific Use Cases

For Budget-Conscious Users

• Phi-3 Mini (3.8B) on RTX5000 ($0.39/hr)
  • Remarkably capable despite small size
  • Excellent for development, prototyping, and educational purposes

For Balanced Performance/Cost

• Llama 3 8B or Mistral 7B on A5000 ($0.49/hr)
  • Strong general performance without breaking the bank
  • Works well for most common applications and fine-tuning experiments

For Long Context Processing

• Llama 4 Scout on A100 ($1.29/hr)
  • Exceptional 10M token context window capability
  • Ideal for document processing, code analysis, and multi-document reasoning

For Multimodal Applications

• Llama 4 Maverick on H100 SXM ($2.99/hr)
  • Superior text and image understanding
  • Excellent for applications requiring visual reasoning

For Maximum Quality

• Llama 3 405B on H200 SXM ($3.80/hr)
  • Top-tier performance approaching proprietary models
  • Best for applications where quality is critical

My Recommendation

After running thousands of experiments across different GPUs and models at JarvisLabs, here's my practical advice:

For general-purpose applications, Llama 4 Scout on an H100 SXM ($2.99/hr) offers an excellent balance of performance, context length, and cost. The mixture-of-experts architecture delivers quality comparable to much larger models, and while it requires more powerful hardware, the performance benefits often justify the investment.

For multimodal applications requiring image understanding, Llama 4 Maverick on an H100 ($2.99/hr) provides state-of-the-art capabilities that rival proprietary models like GPT-4o.

For development and testing, Llama 3 8B on an RTX5000 ($0.39/hr) still gives you tremendous bang for your buck at a fraction of the cost.

The beauty of JarvisLabs' minute-level billing is that you can experiment across different hardware configurations to find your own sweet spot without long-term commitments.

What's your specific use case? Your optimal combination will depend on your latency requirements, budget constraints, and quality thresholds.