Mistral Nemo 12B needs ~24.0 GB VRAM. NVIDIA DGX Spark 128GB has 108.8 GB. With Q4_K_M quantization, expect ~24 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 | C | Runs well | 24.1 tok/s | 4390 ms | 128K |
| Coding | C | Runs well | 24.1 tok/s | 8048 ms | 128K |
| Agentic Coding | B | Runs well | 24.1 tok/s | 11706 ms | 128K |
| Reasoning | C | Runs well | 24.1 tok/s | 9511 ms | 128K |
| RAG | B | Runs well | 24.1 tok/s | 14633 ms | 128K |
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-nemoUpgrade options
NVIDIA H200 141GBBudget pickRaises estimated decode speed by about 597%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
NVIDIA H200 PCIe 141GBBest valueRaises estimated decode speed by about 597%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
NVIDIA B200 180GBNVIDIA upgradeRaises estimated decode speed by about 597%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
Yes, NVIDIA DGX Spark 128GB can run Mistral Nemo 12B with a C grade (Runs well). Expected decode speed: 24.1 tok/s.
Mistral Nemo 12B (12B parameters) requires approximately 24.0 GB of memory with Q4_K_M quantization.
The recommended quantization for Mistral Nemo 12B is Q4_K_M, which balances quality and memory efficiency.
On NVIDIA DGX Spark 128GB, Mistral Nemo 12B achieves approximately 24.1 tokens per second decode speed with a time-to-first-token of 8048ms using Q4_K_M quantization.
For coding workloads, Mistral Nemo 12B on NVIDIA DGX Spark 128GB receives a C grade with 24.1 tok/s and 128K context.
On NVIDIA DGX Spark 128GB, Mistral Nemo 12B can safely use up to 128K tokens of context. 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>
Preview: