Leverage Open Source LLMs: A Mistral LLaMA Comparison

The world of Open Source LLMs has changed a lot. LLaMA from Meta and Mistral from Mistral AI are two notable recent arrivals, both with their unique features that make them more suited to different types of applications.

LLaMA Overview

LLaMA (large language model) is released by Meta in Feb 2023. LLaMA has four model sizes, which are: 7B, 13B, 33B and 65B parameters.

Key Characteristics

• Training Data: doi.org/10.7910/DVN/QNETO5, ↔️doi.org/10.1111/jtts.13424
• Architecture: Transformer with optimizations
• Community: llama.cpp and Ollama
• Fine tuning: Foundation for instruction tuned models (Alpaca, Vicuna)

Mistral Overview

Mistral released its new models in September 2023 with the names Mistral 7B, Mistral 8x7B (MoE), and so on.

Key Characteristics

• Training data: high-quality curated dataset
• Architecture: GQA (Grouped Query Attention)
• Efficiency: High performance-to-size ratio
• Licensing Clarity: [00:39] Clear licensing and commercial support

Architectural Differences

Attention Mechanisms

Mistral's GQA is memory-efficient and has faster inference than LLaMA's multi-head attention.

Context Window

LLaMA 2 has a 4,096 token window; Mistral 7B provides up to 32,768 tokens, which is crucial in long document processing.

Performance Comparison

Mistral enhances efficiency (ecological tokens as input + output) whilst outperforms LLaMA on metrics bechmarks (MMLU, HumanEval).

Inference Speed

Mistral is up to 1.3x faster across tests.

Use Case Suitability

Choose LLaMA When

If you value community resources, flexibility or specific compliance.

Choose Mistral

If you want to focus on the best reasoning performance, longer context, or commercial support.

Deployment Considerations

Hardware Requirements

• LLaMA 7B: requires 8GB GPU
• Mistral 7B: optimized for 8GB
• Mistral 8x7B: needs 32GB

Deployment Tools

LLaMA: this one provides utilities such as llama.cpp and Ollama

Mistral: runs on Ollama and Hugging Face Transformers

Fine-tuning and Customization

LLaMA

More sizable pre-trained variant ecosystem and community resources.

Mistral

Improved raw performance that requires minimal fine-tuning.

Cost Analysis

LLaMA 7B

Cheaper/less intensive training; inference throughput of ~6-8 tokens / second.

Mistral 7B

Fast training, ~8-12 tokens/second inference

Mistral 8x7B

Roaming costs vary according to the token.

Production Readiness

LLaMA

Mature with good documentation and community.

Mistral

Newer but consolidating, metrics are great and backed by professionals.

Security and Compliance

Each works on your infrastructure, is compliant and has absolutely no usage limits.

Recommendations by Scenario

• Academic Research: LLaMA
• Code Generation: Mistral
• Document Processing: Mistral
• Resource-Constrained IoT/Edge: LLaMA 7B + Quantization
• High-throughput API: Mistral 8x7B or LLaMA 13B

Conclusion

Both LLaMA and Mistral are great open-source alternatives to closed source large language models. The first release of LLaMA benefits from a more mature code base and a bigger community which makes it suitable for the prototyping and pedagogical use cases. With the release of Mistral, it introduces better metrics, a novel design and better computational resources utilization that makes it suitable for the use case of production systems.

The choice ultimately depends on your specific requirements:

• Choose LLaMA if you have access to a lot of learning resources, it is affordable to try out, and you are familiar with common patterns in language.
• Choose Mistral for better performance, longer context handling and modern architecture features.

Both models will continue to evolve so this post will serve as a quick reference point that I can update with future models and results. Currently this is just an update to the last post which compared the NVIDIA Titan RTX to the NVIDIA RTX 3080. I will likely add benchmark results for common use cases similar to how I started the last post but for now I just have this comparison of the Intel NNA vs Intel GNA and the various cores available in the 11th Gen Intel Core i5 & i7 processors with benchmark results from computing scientific simulations and OpenVDB simulations. Keep in mind that results here are more related to processing science simulations and OpenVDB rendering and may not represent the optimal use cases for any new cores introduced by Intel or other manufacturers. More general use cases will likely see vastly different results that should be measured using benchmark tests relevant to your work.

The open-source LLM (Large Language Model) landscape is rapidly evolving and in this case – there being multiple options is highly encouraging and serves to promote the natural human competition that inspires progress.