Can Mistral Nemo 12B run on RTX 4070 Ti Super 16GB?

YES — Runs Great

B69Good

○Estimated from fit model

Mistral Nemo 12B needs ~12.3 GB VRAM. RTX 4070 Ti Super 16GB has 16.0 GB. With Q4_K_M quantization, expect ~83 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 12.3 GB, 82.9 tok/s, Runs well

12.3 GB required16.0 GB available

77% VRAM used

Fit status

Runs well

Decode

82.9 tok/s

TTFT

2335 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 4070 Ti Super 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: 82.9 tok/s decode · 2.3s TTFT (warm) · 207 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	82.9 tok/s	1274 ms	41K
Coding	B	Runs well	82.9 tok/s	2335 ms	41K
Agentic Coding	B	Tight fit	82.9 tok/s	3397 ms	41K
Reasoning	B	Runs well	82.9 tok/s	2760 ms	41K
RAG	B	Tight fit	82.9 tok/s	4246 ms	41K

Quantization options

How Mistral Nemo 12B (12B params) fits at each quantization level on RTX 4070 Ti Super 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-nemo

Frequently asked questions

See all results for RTX 4070 Ti Super 16GB See all hardware for Mistral Nemo 12B

URL: https://willitrunai.com/can-run/mistral-nemo-12b-on-rtx-4070-ti-super-16gb

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