16 GB VRAM (+4)448 GB/s (+88)
B
Adds memory headroom for longer context windows and future model growth.39.6 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
Can Mistral Nemo 12B run on RTX 3060 12GB?
YES — With Offload
B63Good
○Estimated from fit model
Mistral Nemo 12B needs ~11.9 GB VRAM. RTX 3060 12GB has 12.0 GB. With Q4_K_M quantization, expect ~33 tok/s.
Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Balanced
Share:
Operating mode
Choose the run profile you care about
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
Q4_K_M (Medium quality) — 11.9 GB, 34.2 tok/s, Runs with offload
11.9 GB required12.0 GB available
99% VRAM used
Fit status
Runs with offload
Decode
34.2 tok/s
TTFT
5662 ms
Safe context
17K
Memory
11.9 GB / 12.0 GB
Memory breakdown
Weights7.3 GB
KV Cache2.4 GB
Runtime0.9 GB
Headroom1.2 GB
See how fast it feels
See how fast it feelsMistral Nemo 12B on RTX 3060 12GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 34.2 tok/s decode · 5.7s TTFT (warm) · 86 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Tight fit | 32.5 tok/s | 3253 ms | 17K |
| Coding | B | Runs with offload | 32.5 tok/s | 5964 ms | 17K |
| Agentic Coding | C | Very compromised | 16.8 tok/s | 16739 ms | 17K |
| Reasoning | B | Runs with offload | 32.5 tok/s | 7049 ms | 17K |
| RAG | C | Very compromised | 16.8 tok/s | 20923 ms | 17K |
Quantization options
How Mistral Nemo 12B (12B params) fits at each quantization level on RTX 3060 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.7 GB | Low | B64 |
Q3_K_S | 3 | 5.9 GB | Low | B65 |
NVFP4 | 4 |
Get started
Copy-paste commands to run Mistral Nemo 12B on your machine.
Run
ollama run mistral-nemoUpgrade options
Hardware that runs Mistral Nemo 12B well
16 GB VRAM (+4)
B
Adds memory headroom for longer context windows and future model growth.32.4 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$499 MSRP
16 GB VRAM (+4)
B
Adds memory headroom for longer context windows and future model growth.32.1 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$625 MSRP