RTX 5060 Ti 16GBOpción económica16 GB VRAM (+4)
B
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.39.6 tok/s decodificación
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$449 MSRP
Can Mistral Nemo 12B run on RTX 4070 12GB?
YES — With Offload
B65Good
Estimated from fit model
Mistral Nemo 12B needs ~11.9 GB VRAM. RTX 4070 12GB has 12.0 GB. With Q4_K_M quantization, expect ~58 tok/s.
Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: Balanced
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, 58.3 tok/s, Runs with offload
11.9 GB required12.0 GB available
99% VRAM used
Fit status
Runs with offload
Decode
58.3 tok/s
TTFT
3321 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 4070 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: 58.3 tok/s decode · 3.3s TTFT (warm) · 146 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 | 58.3 tok/s | 1812 ms | 17K |
| Coding | B | Runs with offload | 58.3 tok/s | 3321 ms | 17K |
| Agentic Coding | C | Very compromised (needs ~1.2 GB host RAM) | 30.2 tok/s | 9321 ms | 17K |
| Reasoning | B | Runs with offload | 58.3 tok/s | 3925 ms | 17K |
| RAG | C | Very compromised (needs ~1.2 GB host RAM) | 30.2 tok/s | 11652 ms | 17K |
Quantization options
How Mistral Nemo 12B (12B params) fits at each quantization level on RTX 4070 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 | 6.7 GB | Medium | B64 |
Q4_K_M | 4 | 7.3 GB | Medium | B64 |
Q5_K_MBest for your GPU | 5 | 8.6 GB | High | B64 |
Q6_K | 6 | 9.8 GB | High | F0 |
Q8_0 | 8 | 12.8 GB | Very High | F0 |
F16 | 16 | 24.6 GB | Maximum | F0 |
Get started
Copy-paste commands to run Mistral Nemo 12B on your machine.
Run
ollama run mistral-nemoOpciones de mejora
Hardware que ejecuta bien Mistral Nemo 12B
RTX 5060 Ti 16GBOpción económicaRTX 4060 Ti 16GBMejor relación calidad-precio16 GB VRAM (+4)
B
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.32.4 tok/s decodificación
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$499 MSRP
RTX 2000 Ada 16GBMejora NVIDIA16 GB VRAM (+4)
B
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.32.1 tok/s decodificación
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$625 MSRP
Frequently asked questions
Can RTX 4070 12GB run Mistral Nemo 12B?
Yes, RTX 4070 12GB can run Mistral Nemo 12B with a B grade (Runs with offload). Expected decode speed: 58.3 tok/s.
How much VRAM does Mistral Nemo 12B need?
Mistral Nemo 12B (12B parameters) requires approximately 11.9 GB of memory with Q4_K_M quantization.
What is the best quantization for Mistral Nemo 12B?
The recommended quantization for Mistral Nemo 12B is Q4_K_M, which balances quality and memory efficiency.
What speed will Mistral Nemo 12B run at on RTX 4070 12GB?
On RTX 4070 12GB, Mistral Nemo 12B achieves approximately 58.3 tokens per second decode speed with a time-to-first-token of 3321ms using Q4_K_M quantization.
Can RTX 4070 12GB run Mistral Nemo 12B for coding?
For coding workloads, Mistral Nemo 12B on RTX 4070 12GB receives a B grade with 58.3 tok/s and 17K context.
What context window can Mistral Nemo 12B use on RTX 4070 12GB?
On RTX 4070 12GB, Mistral Nemo 12B can safely use up to 17K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.
What should I upgrade first if Mistral Nemo 12B feels slow on RTX 4070 12GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
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<iframe src="https://willitrunai.com/embed/mistral-nemo-12b-on-rtx-4070-12gb" 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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