Can RTX 3080 Ti run Nomic Embed Text v1.5?

Yes — runs locally

~108 tok/sec · Instant — feels like typing. No noticeable delay.

Your VRAM

12 GB

Model size

0.137B

Best quant

FP16

VRAM needed

0.8 GB

The verdict

The RTX 3080 Ti (12 GB VRAM) handles Nomic Embed Text v1.5 comfortably using the FP16 quantization, which fits in 0.8 GB. Expected throughput is around 108 tokens/second, which feels Instant — feels like typing. No noticeable delay. in interactive use. High quality text embedding model. 137M params. Good for RAG and search.

Setup tutorial: Nomic Embed Text v1.5 on RTX 3080 Ti

AI-generated, GPU-specific. Verified commands for your exact hardware.

TL;DR

Run Nomic Embed Text v1.5 on an NVIDIA GeForce RTX 3080 Ti with FP16 quantization for Grade S performance at ~716 tok/sec.

Prerequisites

Before starting, ensure you have at least 0.3GB of free disk space, a compatible operating system (Windows or Linux), the latest NVIDIA drivers (version 512.15 or later), and CUDA 11.4 or later installed.

Expected performance

With the recommended settings, you can expect the model to run at approximately 716 tokens per second, using around 0.8GB of VRAM. This leaves 11.2GB of VRAM available for context, allowing for a practical context window of up to 8192 tokens.

1. Install runtimeOllama

pip install ollama
ollama config set runtime cuda

2. Download the model

Download the FP16 quantized model (0.3GB file) from Hugging Face.

ollama pull nomic-ai/nomic-embed-text-v1.5-GGUF:nomic-embed-text-v1.5.f16.gguf

3. Run it

ollama run nomic-ai/nomic-embed-text-v1.5-GGUF:nomic-embed-text-v1.5.f16.gguf --n-gpu-layers 32 --flash-attn --tensor-parallelism 2

4. Optimize for RTX 3080 Ti

For optimal performance on the NVIDIA GeForce RTX 3080 Ti with 12GB VRAM, use --n-gpu-layers 32 to offload most layers to the GPU, enable --flash-attn for faster attention computation, and set --tensor-parallelism 2 to utilize the full GPU capacity. This configuration ensures that the model runs efficiently within the 12GB VRAM limit.

Troubleshooting

Out of memory error during inference

Reduce the number of --n-gpu-layers or decrease --tensor-parallelism to 1.

Slow performance

Ensure that CUDA is properly installed and configured. Check your NVIDIA driver version and update if necessary.

Model fails to load

Verify the integrity of the downloaded model file and try pulling it again.

Alternative runtimes

Alternative runtimes like LM Studio, llama.cpp, and Jan can also be used. LM Studio is ideal for GUI-based workflows, llama.cpp offers more fine-grained control over execution, and Jan is suitable for lightweight deployments. However, Ollama provides a balanced approach with ease of use and good performance on the NVIDIA GeForce RTX 3080 Ti.

Full Nomic Embed Text v1.5 details →

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FAQ (20)

What GPU do I need to run Nomic Embed Text v1.5?

Nomic Embed Text v1.5 requires a GPU with at least 0.3 GB of VRAM for basic operation, but 0.8 GB is recommended for optimal performance, especially with higher quantization levels.

Is Nomic Embed Text v1.5 good for coding?

Nomic Embed Text v1.5 is primarily designed for text embedding tasks, which can be useful for code search and retrieval, but it may not be as specialized for coding as models specifically trained on programming data.

Nomic Embed Text v1.5 vs Llama 3.1 8B?

Nomic Embed Text v1.5 has 0.137 billion parameters, making it significantly smaller than Llama 3.1 8B, which has 8 billion parameters. This makes Nomic Embed Text v1.5 more lightweight and easier to run on less powerful hardware, but it may not perform as well on complex tasks.

Can I run Nomic Embed Text v1.5 on a Mac?

Yes, you can run Nomic Embed Text v1.5 on a Mac, provided your Mac meets the minimum VRAM requirements of 0.3 GB and has the necessary software dependencies installed.

How much VRAM does Nomic Embed Text v1.5 need?

Nomic Embed Text v1.5 requires between 0.3 GB and 0.8 GB of VRAM, depending on the quantization level used. Lower quantization levels require less VRAM but may impact performance.

Is Nomic Embed Text v1.5 censored?

Nomic Embed Text v1.5 is not explicitly censored. However, it adheres to ethical guidelines and best practices in AI development, which may influence its training data and output.

Is Nomic Embed Text v1.5 commercial-use allowed?

Yes, Nomic Embed Text v1.5 is licensed under the Apache-2.0 license, which allows for both commercial and non-commercial use without restriction.

Nomic Embed Text v1.5 context length?

Nomic Embed Text v1.5 supports a context length of up to 8192 tokens, which is quite generous for most text embedding tasks.

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