Can RTX 5090 run Nomic Embed Text v1.5?
Yes — runs locally
~216 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 5090 (32 GB VRAM) handles Nomic Embed Text v1.5 comfortably using the FP16 quantization, which fits in 0.8 GB. Expected throughput is around 216 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 5090
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Nomic Embed Text v1.5 on an NVIDIA GeForce RTX 5090 with Grade S performance at ~1910 tok/sec using the FP16 quantization.
Prerequisites
Before starting, ensure you have at least 0.3GB of free disk space, a 64-bit version of Windows or Linux, and the latest NVIDIA drivers (version 525.60.12 or later) with CUDA 11.8 installed.
Expected performance
With the FP16 quantization, you can expect ~1910 tok/sec performance, utilizing approximately 0.8GB of VRAM. Given the 32GB VRAM, you will have 31.2GB of headroom for context, allowing for a practical context window of up to 8192 tokens without running into memory constraints.
1. Install runtimeOllama
curl -L https://ollama.io/install.sh | bash
ollama install2. 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.gguf3. Run it
ollama run nomic-ai/nomic-embed-text-v1.5-GGUF:nomic-embed-text-v1.5.f16.gguf --context-length 8192
ollama interactive nomic-ai/nomic-embed-text-v1.5-GGUF:nomic-embed-text-v1.5.f16.gguf4. Optimize for RTX 5090
For optimal performance on the NVIDIA GeForce RTX 5090 with 32GB VRAM, use the --n-gpu-layers flag to offload layers to the GPU, enable flash attention with --flash-attn, and consider tensor parallelism with --tensor-parallel-size 2 to fully utilize the GPU's capabilities. This setup will allow you to achieve the best possible token throughput while maintaining a large context window.
Troubleshooting
Insufficient VRAM during inference
Reduce the context length or decrease the number of GPU layers with --n-gpu-layers <num_layers>.
Low token throughput
Ensure that flash attention is enabled with --flash-attn and that the tensor parallelism is set appropriately with --tensor-parallel-size 2.
Model not found
Verify that the model was successfully downloaded and is available in the Ollama model directory with ollama list.
Alternative runtimes
Alternative runtimes like LM Studio, llama.cpp, and Jan can be used for more advanced customization or specific use cases. LM Studio is ideal for a graphical interface, llama.cpp offers more control over low-level optimizations, and Jan is suitable for lightweight deployments. However, Ollama provides a balanced approach with ease of use and good performance on the NVIDIA GeForce RTX 5090.
Other models that run great on RTX 5090
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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