Can Mistral Nemo 12B run on Tesla P40 24GB?

YES — Runs Great

B63Good

Estimated from fit model

Mistral Nemo 12B needs ~13.4 GB VRAM. Tesla P40 24GB has 24.0 GB. With Q4_K_M quantization, expect ~30 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: 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) — 13.4 GB, 30.0 tok/s, Runs well

13.4 GB required24.0 GB available

56% VRAM used

Fit status

Runs well

Decode

30.0 tok/s

TTFT

6458 ms

Safe context

86K

Memory

13.4 GB / 24.0 GB

Memory breakdown

Weights7.3 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsMistral Nemo 12B on Tesla P40 24GB
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: 30.0 tok/s decode · 6.5s TTFT (warm) · 75 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	30.0 tok/s	3522 ms	86K
Coding	B	Runs well	30.0 tok/s	6458 ms	86K
Agentic Coding	B	Runs well	30.0 tok/s	9393 ms	86K
Reasoning	B	Runs well	30.0 tok/s	7632 ms	86K
RAG	B	Runs well	30.0 tok/s	11741 ms	86K

Quantization options

How Mistral Nemo 12B (12B params) fits at each quantization level on Tesla P40 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.7 GB	Low	B58
Q3_K_S	3	5.9 GB	Low	B58
NVFP4	4	6.7 GB	Medium	B59
Q4_K_M	4	7.3 GB	Medium	B59
Q5_K_M	5	8.6 GB	High	B60
Q6_K	6	9.8 GB	High	B61
Q8_0Best for your GPU	8	12.8 GB	Very High	B63
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

Opções de upgrade

Hardware que roda bem Mistral Nemo 12B

RTX 5090 32GBOpção econômica

32 GB VRAM (+8)1792 GB/s (+1446)

Raises estimated decode speed by about 460%.168 tok/s decodificação

Raises estimated decode speed by about 460%.

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

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBMelhor custo-benefício

32 GB VRAM (+8)896 GB/s (+550)

Raises estimated decode speed by about 268%.110.5 tok/s decodificação

Raises estimated decode speed by about 268%.

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

~$2,499 MSRP

RTX 5000 Ada 32GBUpgrade NVIDIA

32 GB VRAM (+8)576 GB/s (+230)

Raises estimated decode speed by about 126%.67.7 tok/s decodificação

Raises estimated decode speed by about 126%.

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

~$4,000 MSRP

Frequently asked questions

Can Tesla P40 24GB run Mistral Nemo 12B?

Yes, Tesla P40 24GB can run Mistral Nemo 12B with a B grade (Runs well). Expected decode speed: 30.0 tok/s.

How much VRAM does Mistral Nemo 12B need?

Mistral Nemo 12B (12B parameters) requires approximately 13.4 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 Tesla P40 24GB?

On Tesla P40 24GB, Mistral Nemo 12B achieves approximately 30.0 tokens per second decode speed with a time-to-first-token of 6458ms using Q4_K_M quantization.

Can Tesla P40 24GB run Mistral Nemo 12B for coding?

For coding workloads, Mistral Nemo 12B on Tesla P40 24GB receives a B grade with 30.0 tok/s and 86K context.

What context window can Mistral Nemo 12B use on Tesla P40 24GB?

On Tesla P40 24GB, Mistral Nemo 12B can safely use up to 86K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

See all results for Tesla P40 24GB See all hardware for Mistral Nemo 12B

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