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Can the AMD Ryzen 7 8845HS / Ryzen 7 255 NAS (16 GB) run CompassJudger-2 7B?

75Great quality (A) · 75 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 69/100 (quality, fit, speed and memory use) sets the ranking.

Barely: it runs very slowly

Very slow. At Q5_K_M it needs about 6.8 GB of 8.0 GB usable and makes one decision in about 11.9 s.

Best quant
Q5_K_M
Time per decision
11.9 s
Decisions per second
0.1/s
Input assumed
1,024 tokens
Returns
Written feedback, then a score

Best app to run it on the AMD Ryzen 7 8845HS / Ryzen 7 255 NAS (16 GB)

On Linux

llama.cpp

Command line + local API · runs on llama.cpp

  • Usually the first to support new model architectures.
llama-server -hf mradermacher/CompassJudger-2-7B-Instruct-GGUF:Q5_K_M
  • Ollama Runs the Hugging Face GGUF with ollama run hf.co/…
  • 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 CompassJudger-2 7B

Recommended for the AMD Ryzen 7 8845HS / Ryzen 7 255 NAS (16 GB): Q5_K_M. Every other quantization has its own download link in the table below.

Quantized files · GGUF

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

Download the Q5_K_M file (opens in a new tab)

huggingface.co/mradermacher/CompassJudger-2-7B-Instruct-GGUF (opens in a new tab)

ollama run hf.co/mradermacher/CompassJudger-2-7B-Instruct-GGUF:Q5_K_M

Original weights · Hugging Face

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

huggingface.co/opencompass/CompassJudger-2-7B-Instruct (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 75Great quality (A) · 75 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 69/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q5_K_M, 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 slow
How well it runs on the AMD Ryzen 7 8845HS / Ryzen 7 255 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.34.0 / 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.25.0 / 25
SpeedTime for one decision against a 100 ms target: reading the input plus any tokens the model writes.1.4 / 15
ClarityHow easy the answer is to use in code: a probability or score earns the most, written feedback before a score the least.9.0 / 15
Match score69.4 / 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 7 8845HS / Ryzen 7 255 NAS (16 GB)

Quant Memory needed Available Speed Runs Quality Download
Q4_K_M 6.0 GB 8.0 GB 9.9 s Very slow 74Great quality (A) · 74 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 68/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 6.8 GB 8.0 GB 11.9 s Very slow 75Great quality (A) · 75 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 69/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q6_K 7.6 GB 8.0 GB 14.1 s Very slowtight fit 77Great quality (A) · 77 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 69/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q8_0 9.4 GB 8.0 GB 22.2 s Very slowfar over GPU memory limit 78Great quality (A) · 78 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 49/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
F16 16.6 GB 8.0 GB — Won't run 78Great quality (A) · 78 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 1,024-token input and about 150 tokens written. Batching servers process many requests at once.

Other hardware that runs it

Quick answers

How much memory does CompassJudger-2 7B need on the AMD Ryzen 7 8845HS / Ryzen 7 255 NAS 16GB?

At Q5_K_M it needs about 6.8 GB. The AMD Ryzen 7 8845HS / Ryzen 7 255 NAS 16GB has 8.0 GB usable.

How fast is CompassJudger-2 7B on the AMD Ryzen 7 8845HS / Ryzen 7 255 NAS 16GB?

It makes one decision in about 11.9 s at Q5_K_M, 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.

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