Will It Run AI

Can Mistral Nemo 12B run on RTX 4080 Laptop 12GB?

YES — With Offload

B64Good
Estimated from fit model

Mistral Nemo 12B needs ~11.9 GB VRAM. RTX 4080 Laptop 12GB has 12.0 GB. With Q4_K_M quantization, expect ~46 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 11.9 GB, 52.0 tok/s, Runs with offload
11.9 GB required12.0 GB available
99% VRAM used

Fit status

Runs with offload

Decode

52.0 tok/s

TTFT

3726 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 4080 Laptop 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: 52.0 tok/s decode · 3.7s TTFT (warm) · 130 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

WorkloadGradeFitDecodeTTFTContext
ChatBTight fit46.0 tok/s2294 ms17K
CodingBRuns with offload46.0 tok/s4206 ms17K
Agentic CodingCVery compromised23.9 tok/s11803 ms17K
ReasoningBRuns with offload46.0 tok/s4970 ms17K
RAGCVery compromised23.9 tok/s14754 ms17K

Quantization options

How Mistral Nemo 12B (12B params) fits at each quantization level on RTX 4080 Laptop 12GB (12.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
4.7 GB
LowB64
Q3_K_S
3
5.9 GB
LowB65
NVFP4
4
6.7 GB
MediumB64
Q4_K_M
4
7.3 GB
MediumB64
Q5_K_MBest for your GPU
5
8.6 GB
HighB64
Q6_K
6
9.8 GB
HighF0
Q8_0
8
12.8 GB
Very HighF0
F16
16
24.6 GB
MaximumF0

Get started

Copy-paste commands to run Mistral Nemo 12B on your machine.

Run

ollama run mistral-nemo

Opciones de mejora

Hardware que ejecuta bien Mistral Nemo 12B

NVIDIARTX 5060 Ti 16GBOpción económica
16 GB VRAM (+4)448 GB/s (+16)
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

NVIDIARTX 4060 Ti 16GBMejor relación calidad-precio
16 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

NVIDIARTX 5070 Ti 16GBMejora NVIDIA
16 GB VRAM (+4)896 GB/s (+464)
B
Sube la velocidad estimada de decodificación alrededor de un 57%.81.7 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 57%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$749 MSRP

Frequently asked questions

Can RTX 4080 Laptop 12GB run Mistral Nemo 12B?

Yes, RTX 4080 Laptop 12GB can run Mistral Nemo 12B with a B grade (Runs with offload). Expected decode speed: 46.0 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 4080 Laptop 12GB?

On RTX 4080 Laptop 12GB, Mistral Nemo 12B achieves approximately 46.0 tokens per second decode speed with a time-to-first-token of 4206ms using Q4_K_M quantization.

Can RTX 4080 Laptop 12GB run Mistral Nemo 12B for coding?

For coding workloads, Mistral Nemo 12B on RTX 4080 Laptop 12GB receives a B grade with 46.0 tok/s and 17K context.

What context window can Mistral Nemo 12B use on RTX 4080 Laptop 12GB?

On RTX 4080 Laptop 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 4080 Laptop 12GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

See all results for RTX 4080 Laptop 12GBSee all hardware for Mistral Nemo 12B
Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/mistral-nemo-12b-on-rtx-4080-laptop-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: