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Benchmarking LLMs on Ollama with Nvidia GTX 1660 GPU Server

Introduction to GTX 1660 GPU Hosting: The Nvidia GeForce GTX 1660, a mid-tier gaming GPU, is now being employed for running LLMs (Large Language Models) in server environments. With 6GB of GDDR6 memory, 1408 CUDA cores, and a FP32 performance of 5.0 TFLOPS, this GPU is an affordable option for smaller-scale language model inference tasks. Let's dive into the performance analysis of LLMs running on the GTX 1660 GPU server.

Test Server Configuration

Before diving into the Ollama GTX 1660 benchmark, let's take a look at the server specs:

Server Configuration:

Price: $159/month
CPU: Dual 10-Core Xeon E5-2660v2
RAM: 64GB
Storage: 120GB + 960GB SSD
Network: 100Mbps Unmetered
OS: Windows 11 Pro

GPU Details:

GPU: Nvidia GeForce GTX 1660
Compute Capability: 7.5
Microarchitecture: Turing
CUDA Cores: 1408
Memory: 6GB GDDR6
FP32 Performance: 5.0 TFLOPS

This setup makes it a viable option for Nvidia GTX 1660 hosting to run small LLM inference workloads efficiently while keeping costs in check.

Ollama Benchmark: Testing LLMs on GTX 1660 Server

For testing, we utilized Ollama 0.5.11 to benchmark a variety of LLMs on the Nvidia GTX 1660 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	6%	20%	28%	18%	6%	30%	17%	4%	45%	30%	6%	18%
RAM Rate	8%	9%	10%	9%	8%	10%	8%	8%	12%	12%	8%	9%
GPU UTL	38%	37%	37%	42%	50%	35%	42%	20%	30%	36%	36%	37%
Eval Rate(tokens/s)	30.16	18.29	16.26	21.31	38.36	16.67	21.42	40.2	9.69	9.39	26.28	18.24

A video to record real-time GTX 1660 GPU Server resource consumption data:

Screen Shoots for Bechmarking LLMs on Ollama with Nvidia GTX1660 GPU Server

Key Findings from the Benchmark

1️⃣. Best for Small Models (7B and below)

The GTX 1660 GPU shines when running small models like DeepSeek-r1 (1.5B) and LLama 2 (7B). These models run smoothly with high GPU utilization and reasonable inference speeds (around 30-40 tokens/s).

2️⃣. CPU Load Increases with Larger Models

For models above 7B, such as DeepSeek-r1 (8B) and LLama 3.1 (8B), CPU utilization increases, signaling that GPU memory (6GB) becomes a bottleneck, limiting performance.

3️⃣. Suboptimal for 8B+ Models

The GTX 1660 struggles with models in the 8B+ range, where it experiences performance drops, and CPU usage increases significantly. It’s clear that larger models are not ideal for this GPU.

4️⃣. Performance Drops for Larger 7B+ Models

Models such as CodeGemma, Gemma, and Mistral (7B) perform significantly better when kept within the 6GB VRAM limit, but as the model size increases, performance drops, particularly when the model exceeds 6GB VRAM.

Get Started with GTX1660 Hosting for Small LLMs

For those deploying LLMs on Ollama, choosing the right NVIDIA GTX1660 hosting solution can significantly impact performance and costs. If you're working with 0.5b-7b model, the GTX1660 is a solid choice for AI inference at an affordable price.

Basic GPU Dedicated Server - GTX 1660

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64GB RAM
Dual 10-Core Xeon E5-2660v2
120GB + 960GB SSD
100Mbps-1Gbps

OS: Windows / Linux
GPU: Nvidia GeForce GTX 1660
Microarchitecture: Turing
CUDA Cores: 1408
GPU Memory: 6GB GDDR6
FP32 Performance: 5.0 TFLOPS

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120GB + 960GB SSD
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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

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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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CUDA Cores: 6,144
Tensor Cores: 192
GPU Memory: 16GB GDDR6
FP32 Performance: 19.2 TFLOPS

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Conclusion

The Nvidia GTX 1660 GPU is a cost-effective solution for running small-scale LLMs (1.5B - 7B) with good inference speeds (30-40 tokens/s) and low-cost hosting options like $159/month. For larger models, such as 8B and above, consider scaling up to GPUs with more VRAM for optimal performance. This GTX 1660 VPS is excellent for developers working with smaller language models, LLMs inference, and budget-conscious projects.

Tags:

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