Mac mini M4 64GBBudget pick
64 GB Unified (+32)
B
Adds memory headroom for longer context windows and future model growth.2 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Nomic Embed Text v1.5 needs ~5.2 GB VRAM. NVIDIA V100 32GB has 32.0 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
5.2 GB / 32.0 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 | B | Runs well | 2.0 tok/s | 96800 ms | 8K |
| Agentic Coding | B | Runs well | 2.0 tok/s | 140800 ms | 8K |
| Reasoning | B | Runs well | 2.0 tok/s | 114400 ms | 8K |
| RAG | B | Runs well | 2.0 tok/s | 176000 ms | 8K |
How Nomic Embed Text v1.5 (0.13699999451637268B params) fits at each quantization level on NVIDIA V100 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.1 GB | Low | A75 |
Q3_K_S | 3 | 0.1 GB | Low | A75 |
NVFP4 | 4 |
Copy-paste commands to run Nomic Embed Text v1.5 on your machine.
Run
ollama run nomic-embed-textUpgrade options
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP
Yes, NVIDIA V100 32GB can run Nomic Embed Text v1.5 with a B grade (Runs well). Expected decode speed: 2.0 tok/s.
Nomic Embed Text v1.5 (0.13699999451637268B parameters) requires approximately 5.2 GB of memory with F16 quantization.
The recommended quantization for Nomic Embed Text v1.5 is F16, which balances quality and memory efficiency.
On NVIDIA V100 32GB, 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 NVIDIA V100 32GB receives a B grade with 2.0 tok/s and 8K context.
On NVIDIA V100 32GB, 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-v100-32gb" 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 |
| A75 |
Q4_K_M | 4 | 0.1 GB | Medium | A75 |
Q5_K_M | 5 | 0.1 GB | High | A75 |
Q6_K | 6 | 0.1 GB | High | A75 |
Q8_0 | 8 | 0.1 GB | Very High | A75 |
F16Best for your GPU | 16 | 0.3 GB | Maximum | A75 |
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.