Can RTX 3090 run Nomic Embed Text v1.5?
Yes — runs locally
~132 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 3090 (24 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 3090
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Nomic Embed Text v1.5 on an NVIDIA GeForce RTX 3090 with FP16 quantization for Grade S performance at ~1432 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 ~1432 tok/sec performance, utilizing approximately 0.8GB of VRAM. This leaves 23.2GB of VRAM available for context, allowing you to process large documents or maintain a wide context window without performance degradation.
1. Install runtimeOllama
curl -fsSL https://ollama.com/install.sh | sh
ollama config set runtime cuda2. 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.gguf3. 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.gguf4. Optimize for RTX 3090
For optimal performance on the NVIDIA GeForce RTX 3090 with 24GB VRAM, use the --n-gpu-layers parameter to offload layers to the GPU. Set --n-gpu-layers to 128 to utilize the ample VRAM efficiently. Additionally, enable flash attention (--flash-attn) to speed up inference. With 24GB VRAM, you can achieve a practical context window of up to 8192 tokens while maintaining high throughput.
Troubleshooting
Insufficient VRAM for context window
Reduce the context window size using --context-length <tokens> to fit within the available VRAM.
Slow inference speed
Ensure that the CUDA runtime is correctly configured and that flash attention is enabled with --flash-attn.
Model not found
Verify the model name and path, and ensure the model is correctly downloaded and accessible.
Alternative runtimes
Alternative runtimes like LM Studio, llama.cpp, and Jan can be used for more advanced customization or specific use cases. LM Studio offers a graphical interface for easier management, llama.cpp provides a lightweight and highly optimized runtime, and Jan is suitable for distributed inference across multiple GPUs. However, Ollama is recommended for its ease of use and strong performance on the NVIDIA GeForce RTX 3090.
Other models that run great on RTX 3090
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.
Want personalized recommendations for your exact setup? Detect my hardware →