Nous Dolphin 13B needs ~53.1 GB VRAM. NVIDIA DGX Spark 128GB has 0 MB. With F16 quantization, expect ~9 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
17.9 tok/s
TTFT
10846 ms
Safe context
16K
Memory
35.8 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 | B | Runs well | 17.9 tok/s | 5916 ms | 16K |
| Coding | F | Too heavy | 3.2 tok/s | 60255 ms | 4K |
| Agentic Coding | B | Runs well | 17.9 tok/s | 15776 ms | 16K |
| Reasoning | B | Runs well | 17.9 tok/s | 12818 ms | 16K |
| RAG | B | Runs well | 17.9 tok/s | 19720 ms | 16K |
How Nous Dolphin 13B (13B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.1 GB | Low | B60 |
Q3_K_S | 3 | 6.4 GB | Low | B60 |
NVFP4 | 4 | 7.3 GB | Medium | B60 |
Q4_K_M | 4 | 7.9 GB | Medium | B60 |
Q5_K_M | 5 | 9.4 GB | High | B60 |
Q6_K | 6 | 10.7 GB | High | B60 |
Q8_0 | 8 | 13.9 GB | Very High | B61 |
F16Best for your GPU | 16 | 26.7 GB | Maximum | B62 |
Copy-paste commands to run Nous Dolphin 13B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "nousresearch/Nous-Dolphin-13B" \
--hf-file "Nous-Dolphin-13B-Q5_K_M.gguf" \
-c 4096 -ngl 99Upgrade-Optionen
NVIDIA H200 141GBGünstige WahlRaises estimated decode speed by about 917%.
Adds memory headroom for longer context windows and future model growth.
ca. $30,000 MSRP
NVIDIA H200 PCIe 141GBBestes Preis-Leistungs-VerhältnisRaises estimated decode speed by about 917%.
Adds memory headroom for longer context windows and future model growth.
ca. $30,000 MSRP
NVIDIA B200 180GBNVIDIA-UpgradeRaises estimated decode speed by about 917%.
Adds memory headroom for longer context windows and future model growth.
ca. $30,000 MSRP
Yes, NVIDIA DGX Spark 128GB can run Nous Dolphin 13B at F16 quantization (Runs well). The recommended Q5_K_M requires 22.8 GB which exceeds available memory, but at F16 it needs only 53.1 GB. Expected decode speed: 8.6 tok/s.
Nous Dolphin 13B (13B parameters) requires approximately 22.8 GB at Q5_K_M quantization. On NVIDIA DGX Spark 128GB, it fits at F16 using 53.1 GB.
The recommended quantization is Q5_K_M, but on NVIDIA DGX Spark 128GB the best fitting quantization is F16, which uses 53.1 GB.
On NVIDIA DGX Spark 128GB, Nous Dolphin 13B achieves approximately 8.6 tokens per second decode speed with a time-to-first-token of 22499ms using F16 quantization.
For coding workloads, Nous Dolphin 13B on NVIDIA DGX Spark 128GB receives a F grade with 3.2 tok/s and 4K context.
On NVIDIA DGX Spark 128GB, Nous Dolphin 13B 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.
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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/nous-dolphin-13b-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>
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