Can RTX 4060 run Nomic Embed Text v1.5?

Yes — runs locally

~102 tok/sec · Instant — feels like typing. No noticeable delay.

Your VRAM

8 GB

Model size

0.137B

Best quant

FP16

VRAM needed

0.8 GB

The verdict

The RTX 4060 (8 GB VRAM) handles Nomic Embed Text v1.5 comfortably using the FP16 quantization, which fits in 0.8 GB. Expected throughput is around 102 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 4060

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

TL;DR

Run Nomic Embed Text v1.5 on an NVIDIA GeForce RTX 4060 with FP16 quantization for Grade S performance at ~477 tok/sec.

Prerequisites

Before starting, ensure you have at least 1GB of free disk space, a 64-bit version of Windows or Linux, NVIDIA driver version 525.60 or later, and CUDA 11.8 or later installed.

Expected performance

With the FP16 quantization, you can expect ~477 tok/sec performance while using approximately 0.8GB of VRAM. This leaves 7.2GB 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 12 --flash-attn true --tensor-parallelism 1

4. Optimize for RTX 4060

For optimal performance on the NVIDIA GeForce RTX 4060 with 8GB VRAM, use --n-gpu-layers 12 to offload layers to the GPU, enable --flash-attn for efficient attention computation, and set --tensor-parallelism 1 to utilize the full GPU memory effectively.

Troubleshooting

Out of memory error during inference

Reduce the number of layers offloaded to the GPU by setting --n-gpu-layers to a lower value, such as 8 or 4.

Slow performance

Ensure that CUDA is properly installed and configured. Verify the driver version and CUDA version meet the prerequisites.

Model not found

Check the model name and path in the ollama pull command. Ensure the model is correctly downloaded and accessible.

Alternative runtimes

Alternative runtimes like LM Studio, llama.cpp, and Jan can be used if you prefer a different workflow. LM Studio offers a graphical interface, llama.cpp provides more fine-grained control over optimizations, and Jan is lightweight and easy to integrate into existing projects. Choose based on your specific needs and preferences.

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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