Mistral Nemo 12B needs ~41.3 GB VRAM. NVIDIA DGX Spark 128GB has 0 MB. With F16 quantization, expect ~10 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
24.1 tok/s
TTFT
8048 ms
Safe context
128K
Memory
24.0 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 | 4.0 tok/s | 26217 ms | 4K |
| Coding | F | Too heavy | 4.0 tok/s | 48065 ms | 4K |
| Agentic Coding | F | Too heavy | 4.0 tok/s | 69913 ms | 4K |
| Reasoning | C | Runs well | 24.1 tok/s | 9511 ms | 128K |
| RAG | F | Too heavy | 4.0 tok/s | 87391 ms | 4K |
How Mistral Nemo 12B (12B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.7 GB | Low | C52 |
Q3_K_S | 3 | 5.9 GB | Low | C52 |
NVFP4 | 4 | 6.7 GB | Medium | C52 |
Q4_K_M | 4 | 7.3 GB | Medium | C52 |
Q5_K_M | 5 | 8.6 GB | High | C52 |
Q6_K | 6 | 9.8 GB | High | C52 |
Q8_0 | 8 | 12.8 GB | Very High | C52 |
F16Best for your GPU | 16 | 24.6 GB | Maximum | C54 |
Copy-paste commands to run Mistral Nemo 12B on your machine.
Run
ollama run mistral-nemoOpciones de mejora
NVIDIA H200 141GBOpción económicaSube la velocidad estimada de decodificación alrededor de un 597%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$30,000 MSRP
NVIDIA H200 PCIe 141GBMejor relación calidad-precioSube la velocidad estimada de decodificación alrededor de un 597%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$30,000 MSRP
NVIDIA B200 180GBMejora NVIDIASube la velocidad estimada de decodificación alrededor de un 597%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$30,000 MSRP
Yes, NVIDIA DGX Spark 128GB can run Mistral Nemo 12B at F16 quantization (Runs well). The recommended Q4_K_M requires 11.0 GB which exceeds available memory, but at F16 it needs only 41.3 GB. Expected decode speed: 10.0 tok/s.
Mistral Nemo 12B (12B parameters) requires approximately 11.0 GB at Q4_K_M quantization. On NVIDIA DGX Spark 128GB, it fits at F16 using 41.3 GB.
The recommended quantization is Q4_K_M, but on NVIDIA DGX Spark 128GB the best fitting quantization is F16, which uses 41.3 GB.
On NVIDIA DGX Spark 128GB, Mistral Nemo 12B achieves approximately 10.0 tokens per second decode speed with a time-to-first-token of 19319ms using F16 quantization.
For coding workloads, Mistral Nemo 12B on NVIDIA DGX Spark 128GB receives a F grade with 4.0 tok/s and 4K context.
On NVIDIA DGX Spark 128GB, Mistral Nemo 12B can safely use up to 128K tokens of context at F16 quantization. The model's official context limit is 128K, 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/mistral-nemo-12b-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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