Can Mistral Nemo 12B run on NVIDIA A2 16GB?

YES — Runs Great

B65Good

Estimated from fit model

Mistral Nemo 12B needs ~12.6 GB VRAM. NVIDIA A2 16GB has 16.0 GB. With Q4_K_M quantization, expect ~21 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: Very lowStack: BasicBottleneck: Memory bandwidth

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) — 12.6 GB, 22.9 tok/s, Runs well

12.6 GB required16.0 GB available

79% VRAM used

Fit status

Runs well

Decode

22.9 tok/s

TTFT

8451 ms

Safe context

39K

Memory

12.6 GB / 16.0 GB

Memory breakdown

Weights7.3 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsMistral Nemo 12B on NVIDIA A2 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: 22.9 tok/s decode · 8.5s TTFT (warm) · 57 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	22.9 tok/s	4609 ms	39K
Coding	B	Runs well	21.3 tok/s	9084 ms	39K
Agentic Coding	B	Tight fit	22.9 tok/s	12292 ms	39K
Reasoning	B	Runs well	22.9 tok/s	9987 ms	39K
RAG	B	Tight fit	21.3 tok/s	16517 ms	39K

Quantization options

How Mistral Nemo 12B (12B params) fits at each quantization level on NVIDIA A2 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	6.7 GB	Medium	B63
Q4_K_M	4	7.3 GB	Medium	B63
Q5_K_M	5	8.6 GB	High	B64
Q6_KBest for your GPU	6	9.8 GB	High	B63
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-nemo

Opciones de mejora

Hardware que ejecuta bien Mistral Nemo 12B

RTX 4000 Ada 20GBOpción económica

20 GB VRAM (+4)360 GB/s (+160)

Sube la velocidad estimada de decodificación alrededor de un 80%.41.2 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 80%.

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

~$1,250 MSRP

RTX 3090 24GBMejor relación calidad-precio

24 GB VRAM (+8)936 GB/s (+736)

Sube la velocidad estimada de decodificación alrededor de un 320%.96.2 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 320%.

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

~$1,499 MSRP

RTX PRO 4000 Blackwell 24GBMejora NVIDIA

24 GB VRAM (+8)672 GB/s (+472)

Sube la velocidad estimada de decodificación alrededor de un 262%.82.9 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 262%.

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

~$1,599 MSRP

Frequently asked questions

Can NVIDIA A2 16GB run Mistral Nemo 12B?

Yes, NVIDIA A2 16GB can run Mistral Nemo 12B with a B grade (Runs well). Expected decode speed: 21.3 tok/s.

How much VRAM does Mistral Nemo 12B need?

Mistral Nemo 12B (12B parameters) requires approximately 12.6 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 NVIDIA A2 16GB?

On NVIDIA A2 16GB, Mistral Nemo 12B achieves approximately 21.3 tokens per second decode speed with a time-to-first-token of 9084ms using Q4_K_M quantization.

Can NVIDIA A2 16GB run Mistral Nemo 12B for coding?

For coding workloads, Mistral Nemo 12B on NVIDIA A2 16GB receives a B grade with 21.3 tok/s and 39K context.

What context window can Mistral Nemo 12B use on NVIDIA A2 16GB?

On NVIDIA A2 16GB, Mistral Nemo 12B can safely use up to 39K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

See all results for NVIDIA A2 16GB See all hardware for Mistral Nemo 12B

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