gpt-oss-safeguard 20B on AMD Ryzen 5 1600 / 7 1700 / 2600 / 2700 NAS (16 GB)

Can the AMD Ryzen 5 1600 / 7 1700 / 2600 / 2700 NAS (16 GB) run gpt-oss-safeguard 20B?

Barely: it runs very slowly (far over memory limit)

Very slow (far over memory limit). At MXFP4 it needs about 13.9 GB of 12.0 GB usable and makes one decision in about 30.3 s.

Best quant: MXFP4
Time per decision: 30.3 s
Decisions per second: 0.0/s
Input assumed: 512 tokens
Returns: Reasoned verdict

Best app to run it on the AMD Ryzen 5 1600 / 7 1700 / 2600 / 2700 NAS (16 GB)

On Linux

Ollama

Command line + local API · runs on llama.cpp (ggml), MLX (Apple silicon, preview)

Splits the model between GPU memory and system RAM, which this setup needs.
One command with Ollama’s own build of this model.
Serves an OpenAI-compatible API that other apps and coding tools can use.

ollama run gpt-oss-safeguard:20b

llama.cpp Splits the model between GPU memory and system RAM, which this setup needs.
LocalAI One OpenAI-compatible server for GGUF guard models and rerankers together.

How we choose: the files published for this model, which apps run in a container on a NAS without a graphics card (models run on its processor), and whether the model fits fully in the memory models can use. Details.

Download gpt-oss-safeguard 20B

Quickest · Ollama

One command downloads a ready-to-run version and starts a chat.

Get Ollama at ollama.com/download (opens in a new tab) · model page (opens in a new tab)

ollama run gpt-oss-safeguard:20b

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

huggingface.co/unsloth/gpt-oss-safeguard-20b-GGUF (opens in a new tab)

ollama run hf.co/unsloth/gpt-oss-safeguard-20b-GGUF

Original weights · Hugging Face

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

huggingface.co/openai/gpt-oss-safeguard-20b (opens in a new tab)

No sign-in needed.

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

How it’s rated

Quality 76Great quality (A) · 76 of 100: how capable the model is at MXFP4, whatever it runs on. Match score 49/100 (quality, fit, speed and memory use) sets the ranking.: How capable the model itself is at MXFP4, from its editorial quality tier scaled by the quantization or precision. It’s the same on any hardware that runs this variant. Scale: A+ 85+ · A 70+ · B 55+ · C 40+ · D under 40.
Runs Very slowfar over memory limit: How well it runs on the AMD Ryzen 5 1600 / 7 1700 / 2600 / 2700 NAS (16 GB): speed, capped by how well it fits in memory. Runs great: 30+ tokens/s (or near the image, video or decision speed target) · Runs well: 10+ tokens/s · Slow: 3+ tokens/s · Very slow: under 3 · Won’t run: doesn’t fit even with offload. A tight fit or RAM offload caps it at Runs well; heavy offload caps it at Slow.

Match score

To order lists, each result also gets a match score out of 100 from four parts. It favors the most capable model that still runs well.

PrecisionEditorial precision tier from the model’s published benchmarks, scaled by the quantization or precision used.	34.2 / 45
Memory fitFull marks when the model fits in the share of the NAS’s memory that models can use (the rest stays with the NAS system and its storage services); fewer when it goes past that limit.	4.0 / 25
SpeedTime for one decision against a 100 ms target: reading the input plus any tokens the model writes.	0.9 / 15
ClarityHow easy the answer is to use in code: a probability or score earns the most, written feedback before a score the least.	10.0 / 15
Match score	49.1 / 100

The match score orders results; it is not shown as a grade. Quality and Runs are the two ratings in the tables.

Every quantization on the AMD Ryzen 5 1600 / 7 1700 / 2600 / 2700 NAS (16 GB)

Quant	Memory needed	Available	Speed	Runs	Quality	Download
`MXFP4`	13.9 GB	12.0 GB	30.3 s	Very slowfar over memory limit	76Great quality (A) · 76 of 100: how capable the model is at MXFP4, whatever it runs on. Match score 49/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)
`F16`	44.6 GB	12.0 GB	—	Won't run	76Great quality (A) · 76 of 100: how capable the model is at F16, whatever it runs on. Match score 10/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab) Original weights (opens in a new tab)

Times are for one request with a 512-token input and about 160 tokens written. Batching servers process many requests at once.

Other hardware that runs it

76Great quality (A) · 76 of 100: how capable the model is at MXFP4, whatever it runs on. Match score 74/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA RTX PRO 6000 Blackwell MXFP4 · 808 ms · Slow
76Great quality (A) · 76 of 100: how capable the model is at MXFP4, whatever it runs on. Match score 74/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5090 MXFP4 · 812 ms · Slow
76Great quality (A) · 76 of 100: how capable the model is at MXFP4, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4090 MXFP4 · 1.0 s · Slow
76Great quality (A) · 76 of 100: how capable the model is at MXFP4, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 3090 MXFP4 · 1.1 s · Slow
76Great quality (A) · 76 of 100: how capable the model is at MXFP4, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 7900 XTX MXFP4 · 1.1 s · Slow
76Great quality (A) · 76 of 100: how capable the model is at MXFP4, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. Apple M3 Ultra (256 GB) MXFP4 · 1.3 s · Slow

Quick answers

How much memory does gpt-oss-safeguard 20B need on the AMD Ryzen 5 1600 / 7 1700 / 2600 / 2700 NAS 16GB?

At MXFP4 it needs about 13.9 GB. The AMD Ryzen 5 1600 / 7 1700 / 2600 / 2700 NAS 16GB has 12.0 GB usable.

How fast is gpt-oss-safeguard 20B on the AMD Ryzen 5 1600 / 7 1700 / 2600 / 2700 NAS 16GB?

It makes one decision in about 30.3 s at MXFP4, an estimate from the hardware and the size of the model.

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.