Can Mistral Nemo 12B run on RTX 5000 Ada 32GB?

YES — Runs Great

B63Good

Estimated from fit model

Mistral Nemo 12B needs ~14.2 GB VRAM. RTX 5000 Ada 32GB has 32.0 GB. With Q4_K_M quantization, expect ~63 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: 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) — 14.2 GB, 67.7 tok/s, Runs well

14.2 GB required32.0 GB available

44% VRAM used

Fit status

Runs well

Decode

67.7 tok/s

TTFT

2861 ms

Safe context

128K

Memory

14.2 GB / 32.0 GB

Memory breakdown

Weights7.3 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsMistral Nemo 12B on RTX 5000 Ada 32GB
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: 67.7 tok/s decode · 2.9s TTFT (warm) · 169 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	63.0 tok/s	1678 ms	128K
Coding	B	Runs well	63.0 tok/s	3075 ms	128K
Agentic Coding	B	Runs well	63.0 tok/s	4473 ms	128K
Reasoning	B	Runs well	63.0 tok/s	3635 ms	128K
RAG	B	Runs well	63.0 tok/s	5592 ms	128K

Quantization options

How Mistral Nemo 12B (12B params) fits at each quantization level on RTX 5000 Ada 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.7 GB	Low	B56
Q3_K_S	3	5.9 GB	Low	B56
NVFP4	4

Get started

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

Run

ollama run mistral-nemo

Frequently asked questions

Can RTX 5000 Ada 32GB run Mistral Nemo 12B?

Yes, RTX 5000 Ada 32GB can run Mistral Nemo 12B with a B grade (Runs well). Expected decode speed: 63.0 tok/s.

How much VRAM does Mistral Nemo 12B need?

Mistral Nemo 12B (12B parameters) requires approximately 14.2 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 5000 Ada 32GB?

On RTX 5000 Ada 32GB, Mistral Nemo 12B achieves approximately 63.0 tokens per second decode speed with a time-to-first-token of 3075ms using Q4_K_M quantization.

Can RTX 5000 Ada 32GB run Mistral Nemo 12B for coding?

For coding workloads, Mistral Nemo 12B on RTX 5000 Ada 32GB receives a B grade with 63.0 tok/s and 128K context.

What context window can Mistral Nemo 12B use on RTX 5000 Ada 32GB?

On RTX 5000 Ada 32GB, Mistral Nemo 12B can safely use up to 128K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

See all results for RTX 5000 Ada 32GB See all hardware for Mistral Nemo 12B

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