24 GB VRAM (+8)936 GB/s (+648)
B
Raises estimated decode speed by about 191%.94.3 tok/s decode
Raises estimated decode speed by about 191%.
Adds memory headroom for longer context windows and future model growth.
~$1,499 MSRP
Can Mistral Nemo 12B run on RTX 4060 Ti 16GB?
YES — Runs Great
B65Good
○Estimated from fit model
Mistral Nemo 12B needs ~12.3 GB VRAM. RTX 4060 Ti 16GB has 16.0 GB. With Q4_K_M quantization, expect ~29 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 12.3 GB, 32.4 tok/s, Runs well
12.3 GB required16.0 GB available
77% VRAM used
Fit status
Runs well
Decode
32.4 tok/s
TTFT
5972 ms
Safe context
41K
Memory
12.3 GB / 16.0 GB
Memory breakdown
Weights7.3 GB
KV Cache2.4 GB
Runtime0.9 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsMistral Nemo 12B on RTX 4060 Ti 16GB
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: 32.4 tok/s decode · 6.0s TTFT (warm) · 81 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 28.7 tok/s | 3677 ms | 41K |
| Coding | B | Runs well | 28.7 tok/s | 6741 ms | 41K |
| Agentic Coding | B | Tight fit | 28.7 tok/s | 9805 ms | 41K |
| Reasoning | B | Runs well | 28.7 tok/s | 7966 ms | 41K |
| RAG | B | Tight fit | 28.7 tok/s | 12256 ms | 41K |
Quantization options
How Mistral Nemo 12B (12B params) fits at each quantization level on RTX 4060 Ti 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.7 GB | Low | B61 |
Q3_K_S | 3 | 5.9 GB | Low | B62 |
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
24 GB VRAM (+8)1008 GB/s (+720)
B
Raises estimated decode speed by about 265%.118.1 tok/s decode
Raises estimated decode speed by about 265%.
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP
24 GB VRAM (+8)1008 GB/s (+720)
B
Raises estimated decode speed by about 218%.103 tok/s decode
Raises estimated decode speed by about 218%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP