Can RTX 5070 Ti run Nomic Embed Text v1.5?

Yes — runs locally

~156 tok/sec · Instant — feels like typing. No noticeable delay.

Your VRAM

16 GB

Model size

0.137B

Best quant

FP16

VRAM needed

0.8 GB

The verdict

The RTX 5070 Ti (16 GB VRAM) handles Nomic Embed Text v1.5 comfortably using the FP16 quantization, which fits in 0.8 GB. Expected throughput is around 156 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 5070 Ti

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

TL;DR

Nomic Embed Text v1.5 runs at Grade S on the NVIDIA GeForce RTX 5070 Ti with FP16 quantization, achieving ~955 tok/sec.

Prerequisites

Before starting, ensure you have at least 0.3GB of disk space available. Your system should be running Windows or Linux with the latest NVIDIA drivers (version 525.60.13 or later) and CUDA 11.8 installed.

Expected performance

With the FP16 quantization, you can expect ~955 tok/sec performance, using approximately 0.8GB of VRAM. This leaves 15.2GB of VRAM available for context, enabling a practical context window of up to 8192 tokens.

1. Install runtimeOllama

curl -sSL https://ollama.com/install.sh | sh
ollama setup

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

4. Optimize for RTX 5070 Ti

For optimal performance on the NVIDIA GeForce RTX 5070 Ti with 16GB VRAM, set --n-gpu-layers to 32 to fully utilize the GPU. Enable flash attention (--flash-attn) to reduce memory usage and improve speed. With 0.8GB VRAM used by the model, you have 15.2GB of VRAM available for context, allowing for a large practical context window of up to 8192 tokens.

Troubleshooting

Out of memory errors during inference

Reduce the number of --n-gpu-layers or increase the batch size.

Slow performance

Ensure that flash attention (--flash-attn) is enabled and that the latest NVIDIA drivers and CUDA are installed.

Model not loading

Verify that the model file was downloaded correctly and that the Ollama runtime is properly installed.

Alternative runtimes

Alternative runtimes like LM Studio, llama.cpp, and Jan can also be used. LM Studio is ideal for a more graphical interface, while llama.cpp offers more fine-grained control over optimizations. Jan is suitable for lightweight deployments. However, Ollama provides a balanced approach with ease of use and good performance on the NVIDIA GeForce RTX 5070 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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