Best AI Fine-Tuning Platforms for Gemma
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Compare the Top AI Fine-Tuning Platforms that integrate with Gemma as of January 2026
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This a list of AI Fine-Tuning platforms that integrate with Gemma. Use the filters on the left to add additional filters for products that have integrations with Gemma. View the products that work with Gemma in the table below.
What are AI Fine-Tuning Platforms for Gemma?
AI fine-tuning platforms are tools used to improve the performance of artificial intelligence models. These platforms provide a framework for training and optimizing AI algorithms, allowing them to better understand and respond to data. They offer a variety of features such as automated hyperparameter tuning and data augmentation techniques. Users can also visualize the training process and monitor the model's accuracy over time. Overall, these platforms aim to streamline the process of fine-tuning AI models for various applications and industries. Compare and read user reviews of the best AI Fine-Tuning platforms for Gemma currently available using the table below. This list is updated regularly.
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1
Google AI Studio
Google
Google AI Studio offers robust fine-tuning capabilities, allowing users to tailor pre-trained models to meet their specific needs. Fine-tuning involves adjusting the weights and parameters of an existing AI model based on domain-specific data, resulting in improved accuracy and performance. This is particularly useful for businesses that require customized AI solutions to address unique challenges, such as specialized language processing or industry-specific insights. The platform provides an easy-to-use interface for fine-tuning, enabling users to quickly adapt models to new data and refine their AI systems to better align with their objectives.Starting Price: Free -
2
Vertex AI
Google
AI Fine-Tuning in Vertex AI allows businesses to take pre-trained models and adapt them to their specific requirements by modifying model parameters or retraining with specialized datasets. This fine-tuning process helps companies improve model accuracy, ensuring that AI applications deliver the best possible results in real-world scenarios. With this functionality, businesses can take advantage of state-of-the-art models without needing to start from scratch. New customers receive $300 in free credits, offering them the opportunity to test fine-tuning techniques and enhance model performance with their own data. As businesses refine their AI models, they can achieve a higher level of personalization and precision, boosting the effectiveness of their solutions.Starting Price: Free ($300 in free credits) -
3
Unsloth
Unsloth
Unsloth is an open source platform designed to accelerate and optimize the fine-tuning and training of Large Language Models (LLMs). It enables users to train custom models, such as ChatGPT, in just 24 hours instead of the typical 30 days, achieving speeds up to 30 times faster than Flash Attention 2 (FA2) while using 90% less memory. Unsloth supports both LoRA and QLoRA fine-tuning techniques, allowing for efficient customization of models like Mistral, Gemma, and Llama versions 1, 2, and 3. Unsloth's efficiency stems from manually deriving computationally intensive mathematical steps and handwriting GPU kernels, resulting in significant performance gains without requiring hardware modifications. Unsloth delivers a 10x speed increase on a single GPU and up to 32x on multi-GPU systems compared to FA2, with compatibility across NVIDIA GPUs from Tesla T4 to H100, and portability to AMD and Intel GPUs.Starting Price: Free -
4
Axolotl
Axolotl
Axolotl is an open source tool designed to streamline the fine-tuning of various AI models, offering support for multiple configurations and architectures. It enables users to train models, supporting methods like full fine-tuning, LoRA, QLoRA, ReLoRA, and GPTQ. Users can customize configurations using simple YAML files or command-line interface overrides, and load different dataset formats, including custom or pre-tokenized datasets. Axolotl integrates with technologies like xFormers, Flash Attention, Liger kernel, RoPE scaling, and multipacking, and works with single or multiple GPUs via Fully Sharded Data Parallel (FSDP) or DeepSpeed. It can be run locally or on the cloud using Docker and supports logging results and checkpoints to several platforms. It is designed to make fine-tuning AI models friendly, fast, and fun, without sacrificing functionality or scale.Starting Price: Free -
5
Cake AI
Cake AI
Cake AI is a comprehensive AI infrastructure platform that enables teams to build and deploy AI applications using hundreds of pre-integrated open source components, offering complete visibility and control. It provides a curated, end-to-end selection of fully managed, best-in-class commercial and open source AI tools, with pre-built integrations across the full breadth of components needed to move an AI application into production. Cake supports dynamic autoscaling, comprehensive security measures including role-based access control and encryption, advanced monitoring, and infrastructure flexibility across various environments, including Kubernetes clusters and cloud services such as AWS. Its data layer equips teams with tools for data ingestion, transformation, and analytics, leveraging tools like Airflow, DBT, Prefect, Metabase, and Superset. For AI operations, Cake integrates with model catalogs like Hugging Face and supports modular workflows using LangChain, LlamaIndex, and more.
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