Can I run Mistral Nemo Base 12B on my device?

Mistral Nemo Base 12B requires a minimum of 7.7GB VRAM. Use RunThisModel to check your specific hardware compatibility and find the best quantization for your device.

How much VRAM does Mistral Nemo Base 12B need?

Mistral Nemo Base 12B needs 7.7GB VRAM at minimum (BF16 quantization). Higher quality quantizations need more: BF16: 24.5GB, Q4_K_M: 7.7GB.

How do I download Mistral Nemo Base 12B?

You can download Mistral Nemo Base 12B in GGUF format from HuggingFace (7.2GB minimum). Use the RunThisModel iOS app to download and run it directly on your device, or download manually from HuggingFace.

Can Mistral Nemo Base 12B run on iPhone?

Mistral Nemo Base 12B at 12B parameters is too large for most iPhones. Consider using an iPad with M-series chip or Mac with Apple Silicon.

Mistral AI

Mistral Nemo Base 12B

Name: Mistral Nemo Base 12B
Author: Mistral AI

Official Mistral-Nemo 12B foundation model (NVIDIA collab) — pretrained only, no instruct or refusal layer. Naturally uncensored, Apache 2.0, 128K context.

12B parametersmistralapache-2.0128K context7.7GB - 24.5GB VRAM

Check Your Hardware

See which quantizations of Mistral Nemo Base 12B your hardware can run.

Quantization Options

Quantization	Bits	File Size	VRAM Needed	RAM Needed	Quality
BF16	16	24 GB	24.5 GB	25 GB	100%
Q4_K_M	4.5	7.2 GB	7.7 GB	8.2 GB	85%

Context window & KV cache

Adds 1.25 GB to VRAM

Long chats and RAG inputs cost real memory. Drag to see how 32K vs 128K context shifts your grade.

Model native max: 128K tokens. KV-cache estimate is approximate (±30 %); real usage depends on attention layout.

How to run Mistral Nemo Base 12B

Pick a runtime — copy & paste. Commands are pre-filled with this model’s repo.

GUI. Browse → download → chat. MLX on Apple Silicon.

LM Studio home →

1
Open LM Studio
Go to the 🔍 Search tab.
2
Search for
```
bartowski/Mistral-Nemo-Base-2407-GGUF
```
3
Download
Pick the Q4_K_M quant — best balance of size vs. quality.
4
Chat
Hit ▶ Load Model and start chatting. Toggle 'Local Server' to expose an OpenAI-compatible API on :1234.

Community benchmarks

Real tokens/sec reports from people running Mistral Nemo Base 12B on actual hardware.

No community runs yet for this model. Be the first to submit your numbers.

Self-host serving plan

Want to host Mistral Nemo Base 12Bfor many users? Or run it on a card that’s technically too small? Slide the knobs.

Concurrent users: 1VRAM budget: 24 GBSystem RAM: 64 GBAllow NVMe spilloverPermit weights to spill to NVMe SSD (last resort, ~50× slower)

VRAM needed

9.1 GB

7.7 GB weights + 0.9 GB KV

Aggregate tok/s

across 1 user

Per-user tok/s

12 B dense

✅ Fits in 24 GB VRAM with 14.9 GB headroom. Pure-GPU inference — full speed.

Throughput is a sub-linear estimate: doubling users adds ~70 % of single-user TPS until ~8, then plateaus on memory bandwidth. MoE models scale concurrency much better because each user activates a different subset of experts.

Download & Run

HuggingFace

View model & download weights

Ollama

One-command install & run

See It In Action

Real model outputs generated via RunThisModel.com — watch responses stream in real time.

Llama 3.3 70B responding...

Outputs generated by real AI models via RunThisModel.com. Generation speed shown is from cloud inference. Local speeds vary by hardware — check your device.

Frequently Asked Questions

How much VRAM do I need to run Mistral Nemo Base 12B?

Mistral Nemo Base 12B requires 7.7GB VRAM minimum with BF16 quantization. For full precision, you need 24.5GB VRAM.

What is the best quantization for Mistral Nemo Base 12B?

Q4_K_M offers the best balance of quality and VRAM usage. Q8_0 is near-lossless if you have enough VRAM.