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RTX2060 Ollama Benchmark: Best GPU for 3B LLMs Inference

With the rise of local large language model (LLM) inference, many AI enthusiasts and developers are looking for cost-effective solutions. One such popular choice is running models using Ollama on an Nvidia RTX2060 GPU. In this benchmark, we evaluate the performance of various LLMs on a dedicated RTX2060 server, analyzing their inference speed, GPU utilization, and overall feasibility for small-scale deployment.

This RTX2060 Ollama benchmark aims to answer the question: Can an Nvidia RTX2060 effectively handle LLMs like DeepSeek, Llama 3, Mistral, and Qwen? And if so, which models provide the best trade-off between performance and resource consumption?

Test Server Configuration

Before diving into the Ollama RTX 2060 benchmark, let's take a look at the server specs:

Server Configuration:

Price: $199/month
CPU: Intel Dual 10-Core E5-2660 v2
RAM: 128GB
Storage: 120GB + 960GB SSD
Network: 100Mbps Unmetered
OS: Windows 11 Pro

GPU Details:

GPU: Nvidia GeForce RTX 2060
Compute Capability: 7.5
Microarchitecture: Ampere
CUDA Cores: 1920
Tensor Cores: 240
Memory: 6GB GDDR6
FP32 Performance: 5.0 TFLOPS

This setup allows us to explore RTX2060 for small LLM inference, focusing on models up to 3B parameters due to the 6GB VRAM limitation.

Benchmark Results: Ollama on Nvidia RTX2060

For testing, we utilized Ollama 0.5.11 to benchmark a variety of LLMs on the Nvidia RTX 2060 GPU. The results provide valuable insight into the GPU's performance when tasked with smaller language models.

Models	deepseek-r1	deepseek-r1	deepseek-r1	deepseek-coder	llama3.2	llama3.1	codellama	mistral	gemma	codegemma	qwen2.5	qwen2.5
Parameters	1.5b	7b	8b	6.7b	3b	8b	7b	7b	7b	7b	3b	7b
Size(GB)	1.1	4.7	4.9	3.8	2.0	4.9	3.8	4.1	5.0	5.0	1.9	4.7
Quantization	4	4	4	4	4	4	4	4	4	4	4	4
Running on	Ollama0.5.11	Ollama0.5.11	Ollama0.5.11	Ollama0.5.11	Ollama0.5.11	Ollama0.5.11	Ollama0.5.11	Ollama0.5.11	Ollama0.5.11	Ollama0.5.11	Ollama0.5.11	Ollama0.5.11
Downloading Speed(mb/s)	12	12	12	12	12	12	12	12	12	12	12	12
CPU Rate	7%	46%	46%	42%	7%	51%	41%	7%	51%	53%	7%	45%
RAM Rate	5%	6%	6%	5%	5%	6%	5%	5%	7%	7%	5%	6%
GPU UTL	39%	35%	32%	35%	56%	31%	35%	21%	12%	11%	43%	36%
Eval Rate(tokens/s)	43.12	8.84	7.52	13.62	50.41	7.39	13.21	48.57	3.70	3.69	36.02	8.98

A video to record real-time RTX 2060 GPU Server resource consumption data:

Screen Shoots for Bechmarking LLMs on Ollama with Nvidia RTX2060 GPU Server

Key Findings from the Benchmark

1️⃣. RTX2060 performs well with 3B parameter models)

Llama3.2 (3B) delivered the fastest inference speed (50.41 tokens/s), making it the best choice for RTX2060 small LLM inference. Qwen2.5 (3B) also ran well at 36.02 tokens/s, though slightly slower than Llama3.2.

2️⃣. RTX2060 struggles with 7B+ models

Models like Mistral 7B, DeepSeek 7B, and Llama 3.1 (8B) experienced low inference speeds (7-9 tokens/s) and near 80% VRAM usage.While technically runnable, the performance is too slow for real-time applications.

3️⃣. Efficient utilization at 3B models

GPU utilization stayed within 50-60% for models ≤3B. RAM and CPU usage remained low (<10% CPU, <6% RAM).

Get Started with RTX2060 Hosting for Small LLMs

For those deploying LLMs on Ollama, choosing the right NVIDIA RTX2060 hosting solution can significantly impact performance and costs. If you're working with 0.5b-3b model, the RTX2060 is a solid choice for AI inference at an affordable price.

Professional GPU Dedicated Server - RTX 2060

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128GB RAM
Dual 10-Core E5-2660v2
120GB + 960GB SSD
100Mbps-1Gbps

OS: Windows / Linux
GPU: Nvidia GeForce RTX 2060
Microarchitecture: Ampere
CUDA Cores: 1920
Tensor Cores: 240
GPU Memory: 6GB GDDR6
FP32 Performance: 6.5 TFLOPS

Powerful for Gaming, OBS Streaming, Video Editing, Android Emulators, 3D Rendering, etc

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64GB RAM
Eight-Core E5-2690
120GB SSD + 960GB SSD
100Mbps-1Gbps

OS: Windows / Linux
GPU: Nvidia GeForce RTX 4060
Microarchitecture: Ada Lovelace
CUDA Cores: 3072
Tensor Cores: 96
GPU Memory: 8GB GDDR6
FP32 Performance: 15.11 TFLOPS

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Advanced GPU Dedicated Server - RTX 3060 Ti

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128GB RAM
Dual 12-Core E5-2697v2
240GB SSD + 2TB SSD
100Mbps-1Gbps

OS: Windows / Linux
GPU: GeForce RTX 3060 Ti
Microarchitecture: Ampere
CUDA Cores: 4864
Tensor Cores: 152
GPU Memory: 8GB GDDR6
FP32 Performance: 16.2 TFLOPS

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OS: Linux / Windows 10/ Windows 11
Dedicated GPU: Quadro RTX A4000
CUDA Cores: 6,144
Tensor Cores: 192
GPU Memory: 16GB GDDR6
FP32 Performance: 19.2 TFLOPS

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Conclusion: RTX2060 is Best for 3B Models

If you're looking for a budget-friendly LLM server using Ollama on an RTX2060, your best bet is 3B parameter models like Llama3.2 and Qwen2.5.

Final Recommendations

For fast inference → Llama3.2 (3B)
For alternative choices → Qwen2.5 (3B)
Avoid models over 7B due to low speed and high VRAM use

This RTX2060 Ollama benchmark shows that while Nvidia RTX2060 hosting is viable for small LLM inference, it is not suitable for models larger than 3B parameters. If you require 7B+ model performance, consider a higher-end GPU like RTX 3060/A4000 servers.

Tags:

RTX2060 Ollama benchmark, RTX2060 AI inference, best LLM for RTX2060, Nvidia RTX2060 hosting, Llama 3 RTX2060, Qwen RTX2060, Mistral AI benchmark, DeepSeek AI, small LLM inference, budget AI GPU