Nomic Embed Text v1.5 needs ~2.8 GB VRAM. RTX 2000 Ada Laptop 8GB has 8.0 GB. With F16 quantization, expect ~2 tok/s.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Fit status
Runs well
Decode
2.0 tok/s
TTFT
96800 ms
Safe context
8K
Memory
2.8 GB / 8.0 GB
This model fits, but memory bandwidth is the part holding decode speed back.
Throughput will feel slow
Estimated decode speed is only 2.0 tok/s, so this is more of a technical fit than a comfortable daily-driver setup.
Prioritize bandwidth, not only capacity
If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 2.0 tok/s | 52800 ms | 8K |
| Coding | A | Runs well | 2.0 tok/s | 96800 ms | 8K |
| Agentic Coding | A | Runs well | 2.0 tok/s | 140800 ms | 8K |
| Reasoning | A | Runs well | 2.0 tok/s | 114400 ms | 8K |
| RAG | A | Runs well | 2.0 tok/s | 176000 ms | 8K |
How Nomic Embed Text v1.5 (0.13699999451637268B params) fits at each quantization level on RTX 2000 Ada Laptop 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.1 GB | Low | A81 |
Q3_K_S | 3 | 0.1 GB | Low | A81 |
NVFP4 | 4 |
Copy-paste commands to run Nomic Embed Text v1.5 on your machine.
Run
ollama run nomic-embed-textYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen 3.5 4B | 4B | S | 56 tok/s | |
| Qwen 3 8B | 8B | A | 23.7 tok/s |
Yes, RTX 2000 Ada Laptop 8GB can run Nomic Embed Text v1.5 with a A grade (Runs well). Expected decode speed: 2.0 tok/s.
Nomic Embed Text v1.5 (0.13699999451637268B parameters) requires approximately 2.8 GB of memory with F16 quantization.
The recommended quantization for Nomic Embed Text v1.5 is F16, which balances quality and memory efficiency.
On RTX 2000 Ada Laptop 8GB, Nomic Embed Text v1.5 achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 96800ms using F16 quantization.
For coding workloads, Nomic Embed Text v1.5 on RTX 2000 Ada Laptop 8GB receives a A grade with 2.0 tok/s and 8K context.
On RTX 2000 Ada Laptop 8GB, Nomic Embed Text v1.5 can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/nomic-embed-text-v1.5-on-rtx-2000-ada-laptop-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
0.1 GB |
| Medium |
| A81 |
Q4_K_M | 4 | 0.1 GB | Medium | A81 |
Q5_K_M | 5 | 0.1 GB | High | A81 |
Q6_K | 6 | 0.1 GB | High | A81 |
Q8_0 | 8 | 0.1 GB | Very High | A81 |
F16Best for your GPU | 16 | 0.3 GB | Maximum | A81 |
 Phi-4 Mini Reasoning 4B | 3.8B | S | 53.2 tok/s |
 Jina Embeddings v3 | 0.57B | A | 8 tok/s |
 BGE M3 | 0.57B | A | 8 tok/s |
Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.