AI is now a core productivity tool, but building a custom assistant is expensive and slow. Open-source LLMs are an alternative — we break down what they are, the upsides, and the trade-offs.
Introduction
AI is now an essential tool in workflows. Neural networks answer questions, draft reports, and generate content. But not every company can build its own AI agent — it's expensive and slow. Open-source LLMs are the alternative. This article covers what an open-source LLM is, plus the trade-offs.
What is a large language model (LLM)
A Large Language Model (LLM) is trained on enormous volumes of diverse data.
This type of AI is purpose-built for text. It reads, analyzes, writes, and answers questions. Leading LLM developers today:
OpenAI — GPT-4, Grok-2
Anthropic — Claude 3
Google DeepMind — Gemini 1.5 Pro
Alibaba Cloud — Qwen 2.5
DeepSeek — R1
An LLM is a specialized solution for text and natural-language processing across English and other languages. A neural network is broader — it covers many architectures for text, images, or voice. ChatGPT is an LLM. ResNet is a neural network for computer vision.
What LLMs can do
LLMs handle a wide range of tasks:
Chatbots. Virtual consultants that answer standard user questions.
Content creation. Social-media posts, interview questions, articles, even books — produced fast.
Multilingual translation. LLMs translate between languages and write code.
Analytics and summarization. LLMs analyze large volumes of data and deliver clear, structured conclusions.
Smart search. LLMs find what a user needs online and return comprehensive answers.
AI agents are also a form of LLM.
How LLMs are trained
Training happens in four stages. Developers:
Collect data. Sources include articles, books, and websites. The model learns from diverse examples at scale.
Process the data. Data is split into small chunks so the LLM can learn language structure.
Train the model. Specialized algorithms drive training. The model predicts the next word in a sentence based on prior context. Wrong predictions get corrected, and accuracy improves over time. The cycle repeats thousands of times — slow, compute-heavy work.
Test and refine. Developers measure performance on unseen data. Issues found, model refined.
What happens after training?
The model loads into an application and goes to work. Different LLMs give different answers to the same question — they were trained on different data.
Training an LLM is resource-intensive. It demands time and compute. The exact cost depends on the model and on how much training data is used.
A small model like BERT trains in weeks on a handful of GPUs. A large model like GPT-4 takes months or years and requires supercomputers. Thousands of GPUs or TPUs run in parallel, training consumes millions of terabytes of text, and the process burns huge amounts of electricity plus a sizable team. That's why only a few companies can afford to develop and train an LLM. More on resources next.
What compute resources LLMs require
Building and operating LLMs takes substantial resources. Without them, models cannot train or run effectively.
Compute
Training relies on GPUs. They excel at the parallel computation needed to process large data volumes. A mid-sized LLM needs 8–64 GPUs; a large one needs tens of thousands.
Memory
Models need huge amounts of RAM for fast operations and intermediate results. A mid-sized LLM runs on 64–128 GB of RAM; a large one can require several terabytes.
Storage
Training data takes enormous space. Mid-sized LLMs require up to 30 TB; large ones require hundreds. NVMe drives deliver the data-access speeds needed.
Networking and distributed processing
To speed up training, teams use clusters — thousands of machines linked by high-bandwidth networks. This infrastructure distributes work and processes massive data volumes fast.
Building your own model is costly, so most companies integrate ready-made LLMs into their services, applications, and products. This avoids the high cost of buying and maintaining hardware.
What is an open-source LLM
Building an LLM takes enormous resources, so only the largest market leaders develop them.
Most ready-made LLMs ship under a paid, closed license. Some are free under an open license. Those are open-source LLMs, typically downloaded from hubs like HuggingFace.
Open-source LLMs train on smaller data volumes, so they trail closed models in raw capability. But you can fine-tune them on your own data for specific tasks — generating reports, talking to customers. Fine-tuning is far cheaper than training from scratch, which is why small and mid-sized businesses and startups rely on it.
Any base LLM can be tailored to a project's needs. Difficulty depends on the task. Building a Telegram chatbot and plugging in an LLM via API uses the model's standard capabilities — no modification needed. Adapting an open-source model to a complex specific task requires time, specialists, and infrastructure. At that point it's a standard ML problem any experienced specialist can solve, given the right resources.
Open-source LLMs: pros and cons
Advantages:
Accessible. Anyone can download them. No license fees.
Flexible. Adapt them, modify the architecture, train on your own data.
Transparent. Open code. Extend or modify it any time.
Independent. No single-vendor lock-in. Minimal risk of losing access.
Privacy-preserving. Self-hosted on your infrastructure. No data sent to third-party servers.
Downsides:
Output quality varies. Errors happen — the models train on general-purpose data.
No official support. Problems are yours to solve.
Implementation and maintenance demand resources and expertise. Closed systems work out of the box.
You manage the data pipeline yourself — collection, processing, storage for training.
Regulators may question training-data legality or model bias.
Open code is exposed to bad actors who can weaponize it for malware or attacks.
Open-source LLMs share many issues with closed models: hallucinations and limited context windows.
The core advantages of open-source models: free to use, convenient, adaptable to many tasks without deep retraining. You can build a chatbot for a narrow job — cutting objects out of images and filling in backgrounds, or analyzing texts. Most models still need fine-tuning for specialized work.
Popular LLM models
Several open-source LLMs lead the field today.
GPT-J
Built by EleutherAI. More powerful and efficient than GPT-Neo. Six billion parameters, strong at NLP. Trained on a large data set, generates quality content.
DeepSeek
Built by DeepSeek on the transformer architecture. Processes huge volumes of text. Knowledge base draws on books, articles, and scientific papers. Understands context, holds dialogue, generates natural text.
BERT
BERT (Bidirectional Encoder Representations from Transformers) — a landmark model released by Google in 2018. BERT analyzes text in both directions, giving it a stronger grasp of context and word meaning. Strong at question answering, text classification, and machine translation. BERT became the foundation for many modern AI solutions.
T5
Another Google model, released in 2020. One framework, many NLP tasks: translation, summarization, classification, and text generation. Every task is framed as transforming one text into another, which simplifies training.
Mistral
Built by Mistral AI. Strong at text. Multiple configurations, including high-parameter versions. Trained on a wide range of sources — varied, high-quality output.
Yandex YaLM
An LLM from Yandex, released in 2023 for Russian-language NLP. Trained on Russian and English sources. Several versions vary by parameter count.
Hermes 3 — Llama-3.1 8B
Hermes-3 Llama-3.1-8B is built for chat applications. Strong capabilities, good efficiency, solid choice for conversational use.
Yi-1.5-9B-Chat
Yi-1.5-9B-Chat handles more sophisticated conversations. Higher parameter count lets the AI agent cover a wide range of topics.
InternLM2 5-7B Chat
InternLM2 5-7B Chat is a lightweight LLM with serious potential. Smaller size makes it ideal for resource-constrained applications where conversational depth is the priority.
Humanish-Roleplay-Llama-3.1-8B
Humanish-Roleplay-Llama-3.1-8B targets roleplay use cases — built for AI agents that take on different personas. Handles casual conversation and complex interactions equally well.
OpenChat-3.5-1210
OpenChat-3.5-1210 is built for natural, interactive dialogue. Based on the GPT architecture, optimized for conversation, customer support, and text generation. It maintains long, substantive conversations, adapts to a user's style, and delivers accurate answers. Widely used in chatbots, AI agents, and education platforms.
An LLM's parameters work like neural connections — more parameters, smarter the model. High-parameter LLMs tackle complex tasks and identify diverse patterns. For simpler jobs like text classification or chatbots, simpler models suffice. Don't chase the most capable model by default. If an LLM solves your task at the level you need, that's enough.
How to choose the right LLM
Picking an open-source LLM means weighing several criteria.
Define the goal
Start with the use case. Text generation, translation, question answering — different models perform differently on different tasks.
Read the documentation
Confirm the LLM ships with detailed installation and usage docs. That's how you ramp up fast.
Check the requirements
Some LLMs demand significant compute, especially for fine-tuning. Confirm you have the hardware or cloud access.
Evaluate community and support
Active forums and support groups are a strong signal. When configuration breaks, you'll have somewhere to turn.
Try the LLM
Test open-source models on hard tasks before scaling deployment. That's how you confirm fit.
Use leaderboards
Public leaderboards like the Open LLM Leaderboard let you compare models head-to-head on multiple metrics. Take several LLMs, run them on the same task — say, translation — and pick the winner. Some testing arenas accept your own task or use a standard one.
Where LLMs are applied
LLMs are deployed across many industries.
IT
LLMs automate routine work, analyze large data volumes, generate reports, and write code. They cut specialist time and reduce human error.
Education
LLMs build learning materials, tests, assignments, and courses. They personalize programs based on student progress and save educators time.
Business
LLMs power customer support — fast, accurate responses to user requests. Response speed lifts service quality and reduces staff load. Language models also analyze target audiences and produce articles, posts, and ad copy.
Healthcare
LLMs power diagnostic systems. They analyze patient histories and diagnostics and help clinicians make better-informed decisions.
Legal
LLMs analyze documents and draft contracts. The technology still requires fine-tuning, but the field has significant potential.
Limitations and risks
LLMs come with limitations and risks worth accounting for.
Inaccurate answers
LLMs produce information that looks plausible but is false or fabricated. This is called an AI hallucination. Models invent facts, references, or entire concepts. Verification matters.
Transparency problems
LLMs are complex and work with vast data. It's hard to explain why a model produced a given answer. That creates risk in critical fields like law and healthcare.
Ethical concerns
Models inherit errors and biases from training data. That brings the risk of incorrect or discriminatory output. Models may also leak confidential information from the training set.
High costs
LLMs require enormous compute and consume significant electricity. Development and operations are expensive.
Vulnerability to manipulation
Bad actors weaponize models for phishing. An LLM trained on bank-staff communication style can generate emails that mislead users.
The LLM is a high-leverage tool, but not one to deploy carelessly. Weigh the risks. Put controls in place to minimize them.
How to keep data secure
Every prompt sent to a model can carry confidential information. To keep it from third parties, take a comprehensive approach to data protection.
Encryption
Start here. Every byte transmitted or stored should be encrypted. AES or comparable standards work.
Access control
Limit sensitive data to authorized employees. Multi-factor authentication and action logging help prevent breaches.
Compliance with standards
This cuts legal and reputational risk. Companies handling personal data must meet regulatory requirements in their jurisdiction (GDPR in the EU). Companies handling payment data must use PCI DSS.
Continuous monitoring
Automated threat detection, regular audits, and security testing prevent risks before they hit production.
Open-source LLMs: the bottom line
Open-source Large Language Models (LLMs) opened access to cutting-edge AI for a broad audience. Developers, researchers, and enthusiasts can experiment, improve, and adapt the technology without huge financial outlays.
The core strength of open-source LLMs: transparency and flexibility. Users study the source code, modify it, and build their own solutions — that fuels innovation. The limits are real. Quality and performance can lag commercial counterparts. Deployment requires technical knowledge and resources. Ethics and security questions remain pressing — open access cuts both ways.
Open-source LLMs are still a major step toward democratizing AI. They put leading-edge technology in everyone's hands and accelerate collaboration.
Lia is built on top of an LLM. Lia handles language with precision and answers user questions in depth, even on complex topics. It draws from the company's knowledge base and adapts the answer for clarity. Want to see what Lia can do? Book a demo.