Can Mistral Nemo 12B run on RTX 4070 12GB?

Q: 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.

YES — With Offload

B64Good

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 ~52 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.

Capabilities:

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	51.6 tok/s	2045 ms	17K
Coding	B	Runs with offload	51.6 tok/s	3749 ms	17K
Agentic Coding	C	Very compromised	26.8 tok/s	10521 ms	17K
Reasoning	B	Runs with offload	51.6 tok/s	4431 ms	17K
RAG	C	Very compromised	26.8 tok/s	13152 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

Get started

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

Run

ollama run mistral-nemo

Upgrade options

Hardware that runs Mistral Nemo 12B well

RTX 5060 Ti 16GBBudget pick

16 GB VRAM (+4)

Adds memory headroom for longer context windows and future model growth.39.6 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$449 MSRP

RTX 4060 Ti 16GBBest value

16 GB VRAM (+4)

Adds memory headroom for longer context windows and future model growth.32.4 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$499 MSRP

RTX 2000 Ada 16GBNVIDIA upgrade

16 GB VRAM (+4)

Adds memory headroom for longer context windows and future model growth.32.1 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$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: 51.6 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 51.6 tokens per second decode speed with a time-to-first-token of 3749ms 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 51.6 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?

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

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