RX 7600 XT 16GBOpción económica
16 GB VRAM (+4)
B
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.24.5 tok/s decodificación
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$329 MSRP
Can Mistral Nemo 12B run on RX 7700 XT 12GB?
YES — With Offload
B63Good
Estimated from fit model
Mistral Nemo 12B needs ~11.9 GB VRAM. RX 7700 XT 12GB has 12.0 GB. With Q4_K_M quantization, expect ~38 tok/s.
Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: 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, 38.1 tok/s, Runs with offload
11.9 GB required12.0 GB available
99% VRAM used
Fit status
Runs with offload
Decode
38.1 tok/s
TTFT
5086 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 RX 7700 XT 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: 38.1 tok/s decode · 5.1s TTFT (warm) · 95 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 | 38.1 tok/s | 2774 ms | 17K |
| Coding | B | Runs with offload | 38.1 tok/s | 5086 ms | 17K |
| Agentic Coding | C | Very compromised (needs ~1.2 GB host RAM) | 19.7 tok/s | 14273 ms | 17K |
| Reasoning | B | Runs with offload | 38.1 tok/s | 6011 ms | 17K |
| RAG | C | Very compromised (needs ~1.2 GB host RAM) | 19.7 tok/s | 17842 ms | 17K |
Quantization options
How Mistral Nemo 12B (12B params) fits at each quantization level on RX 7700 XT 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
RX 9060 XT 16GBMejor relación calidad-precio
16 GB VRAM (+4)
B
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.29.6 tok/s decodificación
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$349 MSRP
RX 9070 16GBMejora AMD
16 GB VRAM (+4)640 GB/s (+208)
B
Sube la velocidad estimada de decodificación alrededor de un 53%.58.3 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 53%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$479 MSRP
Frequently asked questions
Can RX 7700 XT 12GB run Mistral Nemo 12B?
Yes, RX 7700 XT 12GB can run Mistral Nemo 12B with a B grade (Runs with offload). Expected decode speed: 38.1 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 RX 7700 XT 12GB?
On RX 7700 XT 12GB, Mistral Nemo 12B achieves approximately 38.1 tokens per second decode speed with a time-to-first-token of 5086ms using Q4_K_M quantization.
Can RX 7700 XT 12GB run Mistral Nemo 12B for coding?
For coding workloads, Mistral Nemo 12B on RX 7700 XT 12GB receives a B grade with 38.1 tok/s and 17K context.
What context window can Mistral Nemo 12B use on RX 7700 XT 12GB?
On RX 7700 XT 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 RX 7700 XT 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-rx-7700-xt-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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