RTX 3060 12GB vs RTX 5060 8GB: Best Budget GPU for AI

For budget AI work between the RTX 3060 12GB and RTX 5060 8GB, the optimal choice for a beginner specialist will be the RTX 3060 12GB, especially when flexibility, video memory capacity, and future capabilities are important. Although the RTX 5060 offers higher base performance, the additional 4GB of VRAM on the RTX 3060 significantly expand possibilities for AI tasks, especially when working with large language models.

Table of Contents

• Performance Comparison When Fine-tuning Models
• Inference Result Output Speed
• Out-of-the-box Support and Beginner Friendliness
• Cost and Price-to-Performance Ratio
• Long-term Investment Prospects
• Recommendations for a Beginner Specialist

Performance Comparison When Fine-tuning Models

RTX 3060 12GB demonstrates significant advantages when working with AI models due to its larger video memory capacity. As experts point out, “if you want higher quality output and reasonable generation speed with large context windows, you should consider cards with VRAM capacity between 12GB and 32GB” [source].

When fine-tuning models in Ollama:

• RTX 3060 12GB allows working with larger models without the need for quantization
• 8GB VRAM on the RTX 5060 quickly becomes a limiting factor when working with more complex models
• For 7B parameter models, RTX 3060 provides higher training accuracy

In particular, as noted in research, “12GB VRAM (RTX 3060 12GB, RTX 4070) offer much greater flexibility, allowing 7B models to be run with higher accuracy” [source].

Inference Result Output Speed

When working with inference results, both cards show decent performance, but with different advantages:

RTX 3060 12GB:

• Llama 3.2 7B: 40-50 tokens per second
• Llama 3.2 13B: 20-25 tokens per second [source]

RTX 5060 8GB:

• Higher base performance due to improved tensor cores
• Processes operations 15% faster than RTX 3060 [source]
• However, VRAM limitations may reduce performance when working with larger models

In real AI scenarios, the RTX 5060’s speed advantage is often negated by the need to use more aggressive model quantization to fit within the limited VRAM.

Out-of-the-box Support and Beginner Friendliness

Both cards are well-supported in the NVIDIA ecosystem, but there are differences:

RTX 3060 12GB:

• Wider compatibility with existing tools and frameworks
• More tutorials and documentation focused on this model
• Stable driver support for several years

RTX 5060 8GB:

• Support for the latest NVIDIA technologies, including DLSS 3 and enhanced AI capabilities
• Uses PCI-Express 5.0 standard for better bandwidth
• More modern Blackwell architecture

For a beginner specialist, the RTX 3060 may be preferable due to the larger amount of available tutorials and support communities.

Cost and Price-to-Performance Ratio

When comparing price and performance, there’s an interesting dynamic:

As experts note, “if price efficiency and price-to-performance ratio are important, then choose GeForce RTX 5060. But if performance and future prospects are important – choose RTX 5060. If you plan to play at high resolution or use applications that are demanding on VRAM, then the GeForce RTX 3060 with 12GB VRAM provides more possibilities” [source].

For AI tasks, the price-to-performance ratio often favors the RTX 3060 due to its larger VRAM at an accessible price.

Long-term Investment Prospects

When evaluating long-term prospects, it’s important to consider:

RTX 3060 12GB:

• Large VRAM capacity will remain relevant for AI tasks in the coming years
• Models are getting larger, and 12GB VRAM will provide comfortable operation longer
• Excellent performance-to-memory ratio for AI

RTX 5060 8GB:

• Newer architecture with better energy efficiency
• Faster updated support for new technologies
• However, 8GB VRAM may quickly become outdated for growing AI models

As experts warn, “for local LLMs in 2025, you should consider cards with VRAM capacity between 12GB and 32GB” [source]. This indicates that 12GB VRAM on the RTX 3060 will provide better long-term prospects for AI tasks.

Recommendations for a Beginner Specialist

Based on the analysis conducted, for a beginner in the AI field, choosing the RTX 3060 12GB is recommended for the following reasons:

1. Model Flexibility: The additional 4GB of VRAM allows working with larger models without the need for constant quantization, which is critical for training and experimentation.

2. Better Price-to-Memory Ratio: At a similar or comparable price, the RTX 3060 offers 50% more video memory, which directly impacts AI capabilities.

3. Long-term Prospects: As models grow in size, 12GB VRAM will provide comfortable operation longer than 8GB.

4. Wide Support: The large number of tutorials and communities for beginners will simplify setup and operation.

The RTX 5060 8GB may be interesting if:

• You only care about output speed, not training
• Your budget is strictly limited
• You plan to use only small models

For serious AI work, especially with large language models and fine-tuning tasks, the RTX 3060 12GB will provide a significantly better experience and room for growth.

Sources

1. Best GPU for Local LLM[2025]: Complete Hardware Guide for Running Language Models Locally
2. Run AI Models Locally: GPU Guide & Setup (2025)
3. GeForce RTX 3060 vs GeForce RTX 5060 2025 | Which is Better?
4. GeForce x60: History, Benchmarks, Image Quality - GeForce RTX 3060 … RTX 5060 | TechPowerUp
5. Best GPUs For Local LLMs In 2025 (My Top Picks - Updated) - Tech Tactician
6. NVIDIA RTX 3060 Performance Test 2025: Can It Handle Modern Games? | NoobFeed
7. NVIDIA GeForce RTX 3060 in 2025: Still Worth Buying? Performance & Value – Orange Hardwares