Can RTX 4060 Ti 16GB run Nomic Embed Text v1.5?

Yes — runs locally

~114 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 4060 Ti 16GB (16 GB VRAM) handles Nomic Embed Text v1.5 comfortably using the FP16 quantization, which fits in 0.8 GB. Expected throughput is around 114 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 Ti 16GB

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

TL;DR

Run Nomic Embed Text v1.5 on your NVIDIA GeForce RTX 4060 Ti 16GB with Grade S performance, using the FP16 quantization for ~955 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 525.60.12 or later), and CUDA 11.8 or later installed.

Expected performance

You can expect ~955 tok/sec with the FP16 quantization, using approximately 0.8GB of VRAM. This leaves 15.2GB of VRAM available for context, allowing for 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 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 --device cuda
ollama interactive

4. Optimize for RTX 4060 Ti 16GB

For optimal performance on the NVIDIA GeForce RTX 4060 Ti 16GB, use the --n-gpu-layers parameter to offload layers to the GPU. With 16GB of VRAM, you can set --n-gpu-layers to 128 to fully utilize the GPU. Additionally, enable flash attention with --flash-attn to speed up inference. Tensor parallelism is not necessary for this model size but can be used for larger models.

Troubleshooting

Out of memory error during inference

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

Slow inference speed

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

Model not found

Verify the model path and ensure the model is correctly downloaded and accessible.

Alternative runtimes

For users who prefer different runtimes, consider LM Studio for a more user-friendly interface, llama.cpp for lightweight deployment, or Jan for advanced customization options. Ollama is recommended for its ease of use and performance on the NVIDIA GeForce RTX 4060 Ti 16GB.

Full Nomic Embed Text v1.5 details →

Other models that run great on RTX 4060 Ti 16GB

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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