Nous Hermes 1.0 needs ~44.9 GB VRAM. NVIDIA DGX Spark 128GB has 0 MB. With F16 quantization, expect ~12 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
29.8 tok/s
TTFT
6489 ms
Safe context
16K
Memory
32.0 GB / 108.8 GB
This setup is broadly balanced for this model.
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 5.4 tok/s | 19663 ms | 4K |
| Coding | F | Too heavy | 5.4 tok/s | 36049 ms | 4K |
| Agentic Coding | F | Too heavy | 5.4 tok/s | 52435 ms | 4K |
| Reasoning | F | Too heavy | 5.4 tok/s | 42603 ms | 4K |
| RAG | F | Too heavy | 5.4 tok/s | 65543 ms | 4K |
How Nous Hermes 1.0 (9B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | B60 |
Q3_K_S | 3 | 4.4 GB | Low | B60 |
NVFP4 | 4 |
Copy-paste commands to run Nous Hermes 1.0 on your machine.
Run
lms load Nous-Hermes-1.0 && lms server startUpgrade options
NVIDIA H200 141GBBudget pickRaises estimated decode speed by about 323%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
NVIDIA H200 PCIe 141GBBest valueRaises estimated decode speed by about 323%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
NVIDIA B200 180GBNVIDIA upgradeRaises estimated decode speed by about 323%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
Yes, NVIDIA DGX Spark 128GB can run Nous Hermes 1.0 at F16 quantization (Runs well). The recommended Q4_K_M requires 18.9 GB which exceeds available memory, but at F16 it needs only 44.9 GB. Expected decode speed: 12.4 tok/s.
Nous Hermes 1.0 (9B parameters) requires approximately 18.9 GB at Q4_K_M quantization. On NVIDIA DGX Spark 128GB, it fits at F16 using 44.9 GB.
The recommended quantization is Q4_K_M, but on NVIDIA DGX Spark 128GB the best fitting quantization is F16, which uses 44.9 GB.
On NVIDIA DGX Spark 128GB, Nous Hermes 1.0 achieves approximately 12.4 tokens per second decode speed with a time-to-first-token of 15576ms using F16 quantization.
For coding workloads, Nous Hermes 1.0 on NVIDIA DGX Spark 128GB receives a F grade with 5.4 tok/s and 4K context.
On NVIDIA DGX Spark 128GB, Nous Hermes 1.0 can safely use up to 16K tokens of context at F16 quantization. The model's official context limit is 16K, 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/nous-hermes-1.0-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:
5.0 GB |
| Medium |
| B60 |
Q4_K_M | 4 | 5.5 GB | Medium | B60 |
Q5_K_M | 5 | 6.5 GB | High | B60 |
Q6_K | 6 | 7.4 GB | High | B60 |
Q8_0 | 8 | 9.6 GB | Very High | B60 |
F16Best for your GPU | 16 | 18.5 GB | Maximum | B61 |
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.