Can RTX 5080 run Nomic Embed Text v1.5?
Yes — runs locally
~156 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 5080 (16 GB VRAM) handles Nomic Embed Text v1.5 comfortably using the FP16 quantization, which fits in 0.8 GB. Expected throughput is around 156 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 5080
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Nomic Embed Text v1.5 on an NVIDIA GeForce RTX 5080 with FP16 quantization for Grade S performance at ~955 tok/sec.
Prerequisites
Before starting, ensure you have at least 1GB of free disk space, a compatible operating system (Windows or Linux), and the latest NVIDIA drivers (version 525.60.11 or later) with CUDA 11.8 installed.
Expected performance
With the recommended FP16 quantization, you can expect the model to run at approximately 955 tokens per second, using around 0.8GB of VRAM. This leaves you with 15.2GB of VRAM headroom, allowing for a practical context window of up to 8192 tokens without running into memory constraints.
1. Install runtimeOllama
pip install ollama
ollama config set runtime cuda2. Download the model
Download the FP16 quantized version of Nomic Embed Text v1.5 (0.3GB file size).
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 5080
For optimal performance on the NVIDIA GeForce RTX 5080 with 16GB VRAM, use the --n-gpu-layers flag to offload layers to the GPU, enable flash attention (--flash-attn) to speed up computations, and consider using tensor parallelism (--tensor-parallel-size 2) to further distribute the workload across the GPU cores. This configuration will help you achieve the target ~955 tok/sec while keeping VRAM usage efficient.
Troubleshooting
Out of memory errors during inference.
Reduce the batch size or context length, or increase the --n-gpu-layers value to offload more layers to the GPU.
Slow inference speeds.
Ensure that flash attention is enabled with the --flash-attn flag and that the CUDA backend is correctly configured.
Model fails to load.
Verify that the model file has been downloaded correctly and that the Ollama runtime is properly installed and configured.
Alternative runtimes
While Ollama is the recommended runtime for this setup, you can also use LM Studio for a more user-friendly interface, llama.cpp for lower-level control, or Jan for distributed inference. Choose an alternative runtime if you need specific features or better integration with existing workflows.
Other models that run great on RTX 5080
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 →