Best Open Source LLMs for Production

Best Open Source LLMs for Production

LLMs (Large Language Models) are rapidly gaining traction in the corporate world, channelizing a revolution across sectors by enhancing creativity and efficiency. They help businesses save on human resources and concentrate on high-priority initiatives. 

Leading companies like IBM, Google, and Microsoft use LLMs for chatbots, content creation, code generation, and summarization, among other complex tasks. Today, LLMs are integral to data analysis in tech, product recommendations and customer service in retail, drug discovery in healthcare, and more.

There are many open-source choices available when it comes to LLMs. However, factual correctness, minimal hallucinations, pinpoint accuracy and security are some of the most crucial requirements that need to be considered before picking on of the available options, especially in production environments. 

Model size, of course, has a huge impact on these criteria– smaller LLMs, while appealing for computational efficiency, are more prone to hallucinations, which makes meeting production standards difficult. 

As a result, striking a fine balance between model size, performance, and computational affordability becomes an important aspect when navigating LLM deployment for production.

In this article we'll examine some of the best open-source LLMs on the market right now and discuss their features and shortcomings.


Llama 3-70B, the flagship open-source LLM from Meta AI, is a perfect fit for NLP for its core usage. It has proven to be a popular and adaptable tool that can not only generate creative and persuasive prose but also is able to do so in a variety of situations with enhanced contextual understanding. It has a vast pre-trained knowledge, which makes it a contextually relevant and coherent conversation generator. Llama3-70B is a significant upgrade over Llama2 in terms of scalability and performance, with the ability to handle multi-step tasks efficiently.

The benchmarks shared at Meta’s press release validate that Llama3-70B is comparable in performance to advanced language models such as GPT-4, Google Gemini Pro 1.5 and Mixtral-8x22B– some of the most popular flagship LLMs– at a fraction of the cost. Developers across the globe took to platforms like X and LinkedIn, sharing their initial feedback, confirming the same.

A 400 billion parameter version of Llama3 is also in the pipeline, which is speculated to have multimodal and multilingual capabilities with a much longer context window. Meta stated that the model is still being trained, but as of 15 April, it had earned an MMLU benchmark score of 86.1; just slightly lower than GPT-4, which has a score of 86.4.

What We Like about Llama3-70B

Increased Pre-Trained Knowledge: Llama3 has been pre-trained on over 15T tokens– 7x the training dataset used for Llama2, with 4x more code examples, all collected from publicly available sources. 

Improved Vocabulary: Llama 3 uses a tokenizer with a vocabulary of 128K tokens as compared to the ~32K token vocabulary size of the Llama2 tokenizer. This results in improved language encoding, and hence improved model performance.

Enhanced Security: Llama3 comes with Llama Guard 2, a safeguard model capable of predicting safety labels on the LLM input and response. It’s a significant improvement over the previous iteration of Llama Guard, aimed at responsible use of AI.

Model Flexibility: The scale and the variety of data it was trained on, incorporating a series of data filtering pipelines and extensive experimentation allows the model to handle complex language processing and generation tasks.

What Llama 3 Can Improve 

Resource Intensive: The 70B parameter model is quite compute-intensive when it comes to inference, requiring at least around 160GB of VRAM (2xA100s), which implies increased costs during high-workload scenarios. 

Smaller Context Window: The official Llama3-70B has only an 8K context window, which is 1/4th of the GPT-4’s 32K token context length, and much smaller than other open source models in this category too.


One of the most used open-source LLMs currently, Mixtral 8x7B-Instruct is a highly performant, sparse mixture of experts (MoE) model by the French AI startup MistralAI. 

The model’s strength is its unique architecture– a total of roughly 46.7B parameters, out of which only around 12.9B are active for a single inference call– resulting in reduced computation and response time. The model is an ensemble of 8 “expert” 7B-parameter submodels, with built-in MoE quantization, LRU caching and speculative expert loading, that routes the request to the appropriate experts.

For context, Mixtral-8x7B, despite being almost half the size of the Llama2-70B, is approximately 6x faster than the previous flagship open-source model by Meta AI, and better in almost all benchmarks in terms of performance.

The model boasts strong multilingual and advanced code generation capabilities, and a much larger context window of 32K tokens, making it one of the best open source LLMs in production for text generation, understanding, and language translation and code generation.

The Advantages of Mixtral 8x7B Instruct 

Adaptability: Mixtral-8x7B-Instruct works well for a variety of tasks, such as generating code, language translation, and generating comprehensive texts.  

Large Context Window: A 32K-token context window enables nearly any use case, including retrieval-augmented generation. As a result, the model is capable of handling and processing long text-sequences, allowing for engineering detailed prompts, and embedding context for more factually correct responses. 

Performance Efficiency: The efficiency of the Mixtral 8x7B Instruct is another benefit. For inference, it just needs one A100, which makes it an affordable option in many situations. It uses a maximum of 2 experts at a time, resulting in faster response times.

Shortcomings of Mixtral 8x7B Instruct

Inefficiency with Batch Processing: One of its major limitations is that batching model queries reduces the performance– The routing mechanism used to select experts adds overhead. In batched inference requests, this overhead increases linearly.

Lack of Moderation Mechanisms: As a base model, Mixtral-8x7B lacks built-in content moderation mechanisms, which can be a concern for applications requiring robust safety measures. For context, Llama3 comes with Llama Guard 2 as an additional security layer for both the LLM input and the generated response. 

Cost Efficiency: While inference uses only two sub-models at a time, you still need to load the entire model into memory. As a result, the MoE model isn’t cost efficient.

Command R+

Command R+ is Cohere's newest open-sourced LLM. The model has been designed and fine-tuned on an instruction-following conversational pattern, with the ability to generate high quality responses with superior accuracy.

Command R+’s strength is its usability in complex RAG applications and multi-step agentig workflows, due to the long context length.

Why Choose Command R+

Long Context Length: Command R+ has a context window of 128K input tokens, making it ideal for RAG-based and chain-of-thought applications.

Multilingual Flexibility: The model is pre–trained to perform well in English, French, Italian, Spanish, Japanese, German, Korean, Portuguese, Chinese and Arabic. Additionally, the foundational training dataset also included corpuses from 13 additional languages– Russian, Polish, Turkish, Vietnamese, Dutch, Czech, Indonesian, Ukrainian, Romanian, Greek, Hindi, Hebrew, and Persian. The model efficiently responds in the language of the user. 

Accuracy and Efficiency: Command R+ excels in tasks like translation, text summarization, and sophisticated question answering, with reduced hallucinations thanks to a larger context window and higher parameter count.

Multi-Step Tool/Agent Use: Command R+ comes with native agent support, that too multi-step– which means the model is capable of invoking another agent via the output of the previous agent on its own. This chain-of-command capability allows generation of complex workflows using the model.

Some Downsides: 

Resource Intensive: With a parameter count of 104B parameters, Command R+ requires an instance with a minimum of 4 x A100-80GB configuration for inference, making it a very expensive model for production.

Context Window Issue: The model’s official page on Cohere’s website states a known issue with the model that the team at Cohere is actively trying to resolve– the model tends to generate poor quality responses with prompts between 112K - 128K in length.


Grok-1, developed by xAI, is a state-of-the-art Mixture-of-Experts (MoE) model boasting 314 billion parameters. Designed to optimize computational efficiency, Grok-1 activates only two out of its eight expert networks per input token, significantly reducing the required computational resources. This autoregressive transformer-based model excels in next-token prediction and supports a wide range of applications, including advanced mathematical reasoning, coding tasks, and natural language understanding. Released under an open-source Apache 2.0 license, Grok-1 aims to democratize access to cutting-edge AI technology for researchers and developers worldwide.

Grok has proved to be a great alternative for creative content generation, owing to its training data corpus that was greatly derived from X (formerly Twitter).

xAI has also announced the next generation of Grok, Grok-1.5, with a larger context length of 128K and multi-modality, which is speculated to be open-sourced as well.

Advantages of Grok-1

Versatility: Grok-1 excels in a wide range of applications, including advanced mathematical reasoning, coding tasks, and natural language understanding. This versatility makes it suitable for diverse use cases across different industries.

Truly Open-Source: Released under the Apache 2.0 license, Grok-1 is open-source, promoting transparency and collaboration within the AI community.

What to watch out about Grok-1

High Resource Requirements: Despite its efficiency, Grok-1 still requires substantial computational resources due to its 314B parameters, making it very expensive to fine-tune or run.

Latency Issues: Although the model is designed to be efficient, the complexity of its architecture can introduce latency, particularly in real-time applications.


The demand for advanced language models is continuously growing. Businesses are looking to integrate genAI functionalities for process automations within the organization. All of the models covered in this article are great options for specific use cases, but before choosing one, it's important to consider the advantages and disadvantages of each model. 

When choosing an LLM, take into account aspects like output quality, speed, cost, factual correctness, minimal hallucinations, and licensing. Deploying and fine-tuning LLMs require ongoing effort and resource allocation. 

At ScaleGenAI, we help deploy and fine-tune LLMs for various business needs at a fraction of the price. We go beyond basic implementation, customizing LLMs to handle complex scenarios and making sure they align perfectly with your business objectives, with E2E LLMOps automations and security solutions.

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