Converting any open source large model to DeepSeek-R1 inference model only requires 7G video memory

Written by

Audrey Miles

Updated on:July-17th-2025

The Unsloth team recently launched a major technological breakthrough. Through the innovative GRPO (Group Relative Policy Optimization) technology, the hardware threshold for AI model training has been significantly reduced, allowing developers to convert any open source large language model into a model with autonomous reasoning capabilities with only 7GB of video memory. This breakthrough has opened up a new path for the popularization and application of AI technology.

Core Advantages

Through innovative technologies, the threshold for AI model training is significantly lowered, and developers only need 7GB of video memory to train large language models with autonomous reasoning capabilities. Compared with traditional solutions (such as dual A100 GPU configurations), it saves 80% of hardware resources, making localized training of high-performance AI models possible.

Technological breakthrough

Revolutionary GRPO algorithm: Based on DeepSeek’s R1 research results, it introduces Group Relative Policy Optimization technology.

Its working principle includes:

Multiple groups of answers generation and scoring mechanism
Optimization of average score comparison between groups
The "aha moment" phenomenon of voluntarily extending thinking time
Reinforcement learning architecture without value function support

Compared with the traditional PPO method, GRPO enables the model to autonomously build reasoning chains through group response generation and reward function optimization, showing significant advantages in tasks such as mathematical problem solving and logical reasoning.

Hardware resource optimization

1. VRAM management innovation:

Combining Unsloth with the vLLM inference library, it achieves: up to 20 times throughput improvement; 50% VRAM usage optimization; a single card (such as Tesla T4 16GB) can complete complex inference; support QLoRA/LoRA hybrid training mode.

2. Cross-platform compatibility:

It supports mainstream model architectures such as Llama3.1, Phi-4, Mistral, and Qwen2.5, and implements customized reasoning training in vertical fields such as law and medicine.

Autonomous Reasoning Evolution

1. Thought chain automation:

Through GRPO's reward function mechanism, the model can autonomously generate reasoning processes from basic arithmetic (1+1=2) to complex logic, breaking away from the data dependence of traditional manual annotation thinking chains.

2. Dynamic optimization system:

It integrates online DPO, PPO, RLOO and other algorithms, and supports real-time strategy adjustment during training. With gradient accumulation and memory optimization technology, it can achieve a processing speed of 4000 tokens/s on the A100 40GB GPU.

Improved training efficiency

Short-term results: Preliminary reasoning capabilities can be acquired after one hour of training. A training cycle of more than 12 hours is recommended for precise optimization.

Resource saving: Compared with traditional solutions, it reduces video memory consumption by 80%, and the cost of single-card training is reduced to the level of consumer-grade hardware.

Process simplification: Automated reward function configuration and strategy optimization mechanism greatly reduce the need for manual intervention.

This technological breakthrough enables small and medium-sized enterprises, research institutions and even individual developers to train professional-level reasoning models in a local environment. It has shown its application potential in scenarios such as medical diagnosis assistance, legal document analysis, and engineering problem deduction, marking an important progress in the democratization of AI.

Unsloth technology significantly reduces the hardware requirements for AI model training through the GRPO algorithm and vLLM integration, while improving training efficiency and reasoning capabilities. This technology not only enables more developers to train customized reasoning models locally, but also provides new possibilities for the popularization and application of AI technology.