Mac Studio M3 Ultra 256GBBudget pick
256 GB Unified (+128)819 GB/s (+546)
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.
~$6,999 MSRP
Nomic Embed Text v1.5 needs ~15.1 GB VRAM. NVIDIA DGX Spark 128GB has 0 MB. 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
15.1 GB / 108.8 GB
The model fits in shared memory, but shared-memory bandwidth is now the real limiter.
Fit does not mean dedicated-VRAM speed
Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
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 | F | Too heavy | 2.0 tok/s | 96800 ms | 4K |
| 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 DGX Spark 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.1 GB | Low | A72 |
Q3_K_S | 3 | 0.1 GB | Low | A72 |
NVFP4 | 4 |
Copy-paste commands to run Nomic Embed Text v1.5 on your machine.
Run
ollama run nomic-embed-textUpgrade options
Yes, NVIDIA DGX Spark 128GB can run Nomic Embed Text v1.5 at F16 quantization (Runs well). The recommended F16 requires 1.8 GB which exceeds available memory, but at F16 it needs only 15.1 GB. Expected decode speed: 2.0 tok/s.
Nomic Embed Text v1.5 (0.13699999451637268B parameters) requires approximately 1.8 GB at F16 quantization. On NVIDIA DGX Spark 128GB, it fits at F16 using 15.1 GB.
The recommended quantization is F16, but on NVIDIA DGX Spark 128GB the best fitting quantization is F16, which uses 15.1 GB.
On NVIDIA DGX Spark 128GB, 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 DGX Spark 128GB receives a F grade with 2.0 tok/s and 4K context.
On NVIDIA DGX Spark 128GB, Nomic Embed Text v1.5 can safely use up to 8K tokens of context at F16 quantization. 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-dgx-spark-128gb" 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 |
| A72 |
Q4_K_M | 4 | 0.1 GB | Medium | A72 |
Q5_K_M | 5 | 0.1 GB | High | A72 |
Q6_K | 6 | 0.1 GB | High | A72 |
Q8_0 | 8 | 0.1 GB | Very High | A72 |
F16Best for your GPU | 16 | 0.3 GB | Maximum | A72 |
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.
Not always. NVIDIA DGX Spark 128GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.