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Benchmarking Large Language Models (LLMs) on Ollama with an NVIDIA V100 GPU Server

With the rising demand for LLM inference and AI model deployment, finding the best GPU hosting solution is crucial. The NVIDIA V100 server is a popular choice for LLM reasoning due to its balance of compute power, affordability, and availability. In this benchmark, we test various LLMs on Ollama running on an NVIDIA V100 (16GB) GPU server, analyzing performance metrics such as token evaluation rate, GPU utilization, and resource consumption.

Test Server Configuration

Before diving into the V100 Ollama benchmark, here’s a quick look at our test server specs:

Server Configuration:

Price: $229.00/month
CPU: Dual 12-Core E5-2690v3 (24 cores & 48 threads)
RAM: 128GB
Storage: 240GB SSD + 2TB SSD
Network: 100Mbps
OS: Windows

GPU Details:

GPU: NVIDIA V100 16GB
Compute Capability 7.0
Microarchitecture: Volta
CUDA Cores: 5,120
Tensor Cores: 640
GPU Memory: 16GB HBM2
FP32 Performance: 14 TFLOPS

This NVIDIA V100 hosting setup ensures an optimal balance between cost and performance, making it a great option for AI hosting, deep learning, and LLM deployment.

Benchmarking LLMs on Ollama with NVIDIA V100 Server

Using Ollama 0.5.11, we tested multiple LLMs, including DeepSeek, LLaMA 2, Mistral, and Qwen. Below are the key results of our benchmarking tests.

Models	deepseek-r1	deepseek-r1	deepseek-r1	deepseek-coder-v2	llama2	llama2	llama3.1	mistral	gemma2	gemma2	qwen2.5	qwen2.5
Parameters	7b	8b	14b	16b	7b	13b	8b	7b	9b	27b	7b	14b
Size(GB)	4.7	4.9	9	8.9	3.8	7.4	4.9	4.1	5.4	16	4.7	9.0
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)	11	11	11	11	11	11	11	11	11	11	11	11
CPU Rate	2%	2%	3%	3%	2%	3%	3%	3%	3%	42%	3%	3
RAM Rate	5%	6%	5%	5%	5%	5%	5%	6%	6%	7%	5%	6%
GPU UTL	71%	78%	80%	70%	85%	87%	76%	84%	69%	13~24%	73%	80%
Eval Rate(tokens/s)	87.10	83.03	48.63	69.16	107.49	67.51	84.07	107.31	59.90	8.37	86.00	49.38

A video to record real-time V100 gpu server resource consumption data:

Screen Shoots for Bechmarking LLMs on Ollama with Nvidia V100 GPU

Key Takeaways from the NVIDIA V100 Ollama Benchmark

1. Best LLM for Performance: LLaMA 2 (7B) & Mistral (7B)

Both LLaMA 2 (7B) and Mistral (7B) deliver the highest token evaluation rates (107+ tokens/s).
Ideal for real-time inference, chatbots, and AI applications requiring fast response times.

2. Medium Models (13-14B) Have Slightly Reduced Performance

DeepSeek 14B, Qwen 14B, and LLaMA 2 13B have reduced evaluation rates (~50 tokens/s).
Higher GPU utilization (~80-87%) results in increased latency.
NVIDIA V100 hosting remains the best choice for 13B-14B models.

3. Not suitable for running large models (27B+)

gemma2:27B drops to 8.37 tokens/s, indicating that v100 will not be able to reason models larger than 27B.

4. GPU Utilization & Resource Efficiency

GPU utilization varies from 70% to 87%, indicating Ollama on V100 efficiently manages workload.
CPU & RAM usage remain low (~2-6%), allowing for potential multi-instance deployments.

Is NVIDIA V100 Hosting a Good Choice for Ollama LLM Inference?

1. Pros of Using NVIDIA V100 for Ollama

Affordable & widely available compared to newer A100/H100 servers.
Good balance of memory (16GB) and compute power for models up to 7B-24B.
Strong inference speed for optimized models like LLaMA 2 and Mistral.
Can run multiple smaller models efficiently due to moderate CPU/RAM requirements.

2. Limitations of NVIDIA V100 for Ollama

Struggles with larger models (27B+), leading to slower evalu,/liation rates.
16GB VRAM is limiting for multi-GPU scaling.
Not ideal for training—only suitable for inference workloads.

Get Started with V100 Server Hosting for LLMs

For those deploying LLMs on Ollama, choosing the right NVIDIA V100 hosting solution can significantly impact performance and costs. If you're working with 7B-24B models, the V100 is a solid choice for AI inference at an affordable price.

Advanced Dedicated GPU Server - V100

$ 229.00/mo

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GPU: V100
CPU: 2 x 12-Core E5-2690v3
Memory: 128GB RAM
Disk: 240GB SSD+2TB SSD
Bandwidth: 100Mbps Unmetered
GPU Memory: 16 GB HBM2

IP: 1 Dedicated IPv4
Location: USA

Professional GPU VPS - RTX A4000

$ 119.00/mo

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GPU: RTX A4000
CPU: 24 CPU Cores
Memory: 28GB RAM
Disk: 320GB SSD
Bandwidth: 300Mbps Unmetered
GPU Memory: 16 GB GDDR6

IP: 1 Dedicated IPv4
Location: USA
Backup: Once per 2 Weeks

Advanced Dedicated GPU Server - RTX 3060 Ti

$ 179.00/mo

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GPU: RTX 3060 Ti
CPU: 2 x 12-Core E5-2697v2
Memory: 128GB RAM
Disk: 240GB SSD+2TB SSD
Bandwidth: 100Mbps Unmetered
GPU Memory: 8 GB GDDR6

IP: 1 Dedicated IPv4
Location: USA

Professional Dedicated GPU Server - P100

$ 159.00/mo

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GPU: P100
CPU: 2 x 8-Core E5-2660
Memory: 128GB RAM
Disk: 120GB SSD + 960GB SSD
Bandwidth: 100Mbps Unmetered
GPU Memory: 16 GB HBM2

IP: 1 Dedicated IPv4
Location: USA

Final Verdict: Is V100 Hosting Worth It for Ollama?

For those looking for an affordable LLM hosting solution, NVIDIA V100 rental services offer a cost-effective option for deploying models like LLaMA 2, Mistral, and DeepSeek-R1. With Ollama’s efficient inference engine, the V100 performs well on models up to 7-24B parameters, making it a great choice for chatbots, AI assistants, and other real-time NLP applications.

However, for larger models (24B+), upgrading to an RTX4090(24GB) or A100(40GB) would be necessary. What LLMs are you running on your NVIDIA V100 server? Let us know in the comments!

Tags:

Ollama, LLM, NVIDIA V100, AI, Deep Learning, Mistral, LLaMA2, DeepSeek, GPU, Machine Learning, AI Inference

Last Updated: 10/21/2026

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