Can RTX 3080 run Nomic Embed Text v1.5?
Yes — runs locally
~108 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 3080 (10 GB VRAM) handles Nomic Embed Text v1.5 comfortably using the FP16 quantization, which fits in 0.8 GB. Expected throughput is around 108 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 3080
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Nomic Embed Text v1.5 on an NVIDIA GeForce RTX 3080 with FP16 quantization for Grade S performance at ~597 tok/sec.
Prerequisites
Before starting, ensure you have at least 0.3GB of free disk space, a compatible OS (Windows or Linux), the latest NVIDIA driver (version 470.82.01 or later), and CUDA 11.2 or later installed.
Expected performance
You can expect the model to run at approximately 597 tokens per second with 0.8GB of VRAM in use, leaving 9.2GB of VRAM available for context. This provides ample headroom to achieve the maximum context window of 8192 tokens.
1. Install runtimeOllama
pip install ollama
ollama init2. 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 interact nomic-ai/nomic-embed-text-v1.5-GGUF:nomic-embed-text-v1.5.f16.gguf4. Optimize for RTX 3080
For optimal performance on the NVIDIA GeForce RTX 3080 with 10GB VRAM, set --n-gpu-layers to 12 to utilize the full GPU capacity. Enable flash attention (--flash-attn) to speed up inference and reduce memory usage. With 0.8GB VRAM used by the model, you have 9.2GB of VRAM left for context, allowing for a practical context window of up to 8192 tokens.
Troubleshooting
Out of memory error during inference
Reduce the number of layers offloaded to the GPU using --n-gpu-layers <num_layers> or decrease the context window size.
Low token throughput
Ensure that flash attention is enabled with --flash-attn and that the latest NVIDIA drivers and CUDA are installed.
Model fails to load
Verify that the model file is correctly downloaded and not corrupted. Try re-downloading the model.
Alternative runtimes
Alternative runtimes like LM Studio, llama.cpp, and Jan can be used if you need more control over the execution environment or specific features. LM Studio is ideal for a GUI-based workflow, llama.cpp offers more fine-grained control over optimizations, and Jan is suitable for lightweight deployments. However, Ollama provides a streamlined and easy-to-use interface for most users on the NVIDIA GeForce RTX 3080.
Other models that run great on RTX 3080
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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