Can RTX 4070 SUPER run Nomic Embed Text v1.5?

Yes — runs locally

~132 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 4070 SUPER (12 GB VRAM) handles Nomic Embed Text v1.5 comfortably using the FP16 quantization, which fits in 0.8 GB. Expected throughput is around 132 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 4070 SUPER

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

TL;DR

Run Nomic Embed Text v1.5 on your NVIDIA GeForce RTX 4070 SUPER 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), and the latest NVIDIA drivers (version 525.60.13 or later) with CUDA 11.8 installed.

Expected performance

With the FP16 quantization, you can expect ~716 tok/sec performance, using approximately 0.8GB of VRAM. This leaves 11.2GB of VRAM available for context, enabling a practical context window of up to 8192 tokens.

1. Install runtimeOllama

pip install ollama
ollama init

2. Download the model

Download the FP16 quantized model (0.3GB) 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 --device cuda
ollama chat --model nomic-ai/nomic-embed-text-v1.5-GGUF:nomic-embed-text-v1.5.f16.gguf

4. Optimize for RTX 4070 SUPER

For optimal performance on the NVIDIA GeForce RTX 4070 SUPER with 12GB VRAM, set --n-gpu-layers to 32 to fully utilize the GPU. Enable flash attention (--flash-attn) to speed up computation and reduce memory usage. With 0.8GB VRAM used by the model, you have 11.2GB of VRAM available for context, allowing for a practical context window of up to 8192 tokens.

Troubleshooting

Out of memory error during inference

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

Slow inference speed

Ensure that flash attention is enabled with --flash-attn and that the CUDA toolkit is correctly installed.

Model not found

Verify that the model was successfully downloaded and is available in the Ollama model directory.

Alternative runtimes

For users preferring different runtimes, consider LM Studio for a more user-friendly interface, llama.cpp for lightweight deployment, or Jan for advanced customization options. Each runtime has its own strengths, but Ollama provides a balanced approach for ease of use and performance on the NVIDIA GeForce RTX 4070 SUPER.

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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