Decision models · Mistral AI

Shieldstral 1.0 3B

Name: Shieldstral 1.0 3B
Author: Mistral AI

Task: Safety and policy checks
Returns: Probability or score
Precision tier: 86 / 100
Size: 3.9B
Max input: 32K tokens
License: apache-2.0
Released: 2026-07

What it decides

Returns: One yes/no token turned into an unsafe probability, judged against a policy you write; checks text and images.
Checks: Prompts, replies and images
Languages: 12 languages
Precision: 86 / 100 · F1 0.88 on WildGuardTest, 0.84 on ToxicChat and 0.85 on BeaverTails, level with Qwen3Guard 8B (model card).
Clarity: Probability or score (15 of 15 clarity points)
How it runs: Writes a short answer · a small language model, so it runs in the same apps as LLMs.
License: apache-2.0 (Open (Apache, MIT))

Source: model card on Hugging Face (opens in a new tab) · How decision models are rated

How to run it

Original weights · Hugging Face

Full-precision files from Mistral AI, for fine-tuning, vLLM, Transformers.

huggingface.co/mistralai/Shieldstral-1.0-3B (opens in a new tab)

No sign-in needed.

Links checked 3 days ago against Hugging Face, Ollama and GitHub.

Apps that run Shieldstral 1.0 3B

Our pick for each kind of computer, assuming the model fits in its memory. Pick your own GPU or Mac in the Model Finder for an exact answer.

Mac (Apple silicon)

Sentence Transformers

A few lines of Python with Transformers or Sentence Transformers; easiest for scripts and notebooks.

Also good: Docker Model Runner

Windows PC, NVIDIA GPU

Sentence Transformers

A few lines of Python with Transformers or Sentence Transformers; easiest for scripts and notebooks.

Also good: Docker Model Runner, vLLM

Windows PC, AMD Radeon

Sentence Transformers

A few lines of Python with Transformers or Sentence Transformers; easiest for scripts and notebooks.

Also good: Docker Model Runner

Linux, NVIDIA GPU

Docker Model Runner

Handy if you already work in Docker.

Also good: vLLM, Sentence Transformers

Linux server, 80 GB+ GPUs

vLLM

The full model fits in GPU memory at 8-bit, so it can batch many requests: best for serving a team or an app.

Also good: SGLang, Docker Model Runner

App	Type	Opens it as	Systems	Engine	Start
vLLM	Server	Original weights	WSLWindowsLinux	vLLM	`vllm serve mistralai/Shieldstral-1.0-3B`
SGLang	Server	Original weights	WSLWindowsLinux	SGLang	`python3 -m sglang.launch_server --model-path mistralai/Shieldstral-1.0-3B`
Docker Model Runner	CLI + API	Original weights	macOSWindowsLinux	llama.cpp, vLLM	`docker model run hf.co/mistralai/Shieldstral-1.0-3B`
Sentence Transformers	Library	Original weights	macOSWindowsLinux	PyTorch	Get Sentence Transformers

Which apps can open a model depends on the files published for it: GGUF for llama.cpp-based apps, MLX for Apple’s engine, the original weights for vLLM and SGLang. Compare every app.

Memory and speed

Each quantization on an NVIDIA GeForce RTX 4090, for one decision on a 512-token input. The best hardware table below covers other GPUs and Macs.

Format	Memory needed	Time per decision	Decisions per second	Quality	Download
`Q4_K_M`	3.2 GB	40 ms	25/s	81Great quality (A) · 81 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 91/100 (quality, fit, speed and memory use) sets the ranking.	All files (opens in a new tab)
`Q5_K_M`	3.6 GB	41 ms	24/s	83Great quality (A) · 83 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 92/100 (quality, fit, speed and memory use) sets the ranking.	All files (opens in a new tab)
`Q6_K`	4.0 GB	42 ms	24/s	85Excellent quality (S) · 85 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 93/100 (quality, fit, speed and memory use) sets the ranking.	All files (opens in a new tab)
`Q8_0`	4.9 GB	43 ms	23/s	86Excellent quality (S) · 86 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 93/100 (quality, fit, speed and memory use) sets the ranking.	All files (opens in a new tab)
`F16`	8.5 GB	50 ms	20/s	86Excellent quality (S) · 86 of 100: how capable the model is at F16, whatever it runs on. Match score 93/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)

One request at a time. Servers that batch (Text Embeddings Inference, vLLM, Infinity) handle many more decisions per second.

Best hardware for Shieldstral 1.0 3B

The least expensive GPU in our catalog that runs it without offloading is the Intel Arc A380 (launch price $139) at Q6_K.

Quality	Hardware	Quant	Memory	Time per decision	Runs
86Excellent quality (S) · 86 of 100: how capable the model is at F16, whatever it runs on. Match score 93/100 (quality, fit, speed and memory use) sets the ranking.	AMD Radeon RX 7900 XTX	`F16`	9.7 GB / 24.0 GB	46 ms	Runs great
86Excellent quality (S) · 86 of 100: how capable the model is at F16, whatever it runs on. Match score 93/100 (quality, fit, speed and memory use) sets the ranking.	AMD Radeon RX 9070 XT	`F16`	9.3 GB / 16.0 GB	61 ms	Runs great
86Excellent quality (S) · 86 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 93/100 (quality, fit, speed and memory use) sets the ranking.	AMD Ryzen AI Max+ 395 (128 GB)	`Q8_0`	12.6 GB / 96.0 GB	85 ms	Runs great
86Excellent quality (S) · 86 of 100: how capable the model is at F16, whatever it runs on. Match score 93/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA GeForce RTX 3090	`F16`	9.7 GB / 24.0 GB	95 ms	Runs great
86Excellent quality (S) · 86 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 93/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA GeForce RTX 4070 SUPER	`Q8_0`	5.5 GB / 12.0 GB	93 ms	Runs great
86Excellent quality (S) · 86 of 100: how capable the model is at F16, whatever it runs on. Match score 93/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA GeForce RTX 4070 Ti SUPER	`F16`	9.3 GB / 16.0 GB	86 ms	Runs great
86Excellent quality (S) · 86 of 100: how capable the model is at F16, whatever it runs on. Match score 93/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA GeForce RTX 4080 SUPER	`F16`	9.3 GB / 16.0 GB	75 ms	Runs great
86Excellent quality (S) · 86 of 100: how capable the model is at F16, whatever it runs on. Match score 93/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA GeForce RTX 4090	`F16`	9.7 GB / 24.0 GB	50 ms	Runs great
86Excellent quality (S) · 86 of 100: how capable the model is at F16, whatever it runs on. Match score 93/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA GeForce RTX 5070 Ti	`F16`	9.3 GB / 16.0 GB	81 ms	Runs great
86Excellent quality (S) · 86 of 100: how capable the model is at F16, whatever it runs on. Match score 93/100 (quality, fit, speed and memory use) sets the ranking.	NVIDIA GeForce RTX 5080	`F16`	9.3 GB / 16.0 GB	67 ms	Runs great

Check it on your own hardware

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.