on

Can the Intel Arc A580 run CompassJudger-2 32B?

83Great quality (A) · 83 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 51/100 (quality, fit, speed and memory use) sets the ranking.

Barely: it runs very slowly (heavy RAM offload)

Very slow (heavy RAM offload). At Q5_K_M it needs about 24.8 GB of 8.0 GB usable and makes one decision in about 60.9 s.

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

Best app to run it on the Intel Arc A580

On Windows

llama.cpp

Command line + local API · runs on llama.cpp

  • Splits the model between GPU memory and system RAM, which this setup needs.
  • Gives you direct control of how many layers sit on the GPU, the best way to squeeze a model that doesn’t quite fit.
  • Supports Intel Arc through SYCL and Vulkan.
llama-server -hf mradermacher/CompassJudger-2-32B-Instruct-GGUF:Q5_K_M
  • LM Studio Splits the model between GPU memory and system RAM, which this setup needs.
  • Jan Splits the model between GPU memory and system RAM, which this setup needs.

On Linux

llama.cpp

Command line + local API · runs on llama.cpp

  • Splits the model between GPU memory and system RAM, which this setup needs.
  • Gives you direct control of how many layers sit on the GPU, the best way to squeeze a model that doesn’t quite fit.
  • Supports Intel Arc through SYCL and Vulkan.
llama-server -hf mradermacher/CompassJudger-2-32B-Instruct-GGUF:Q5_K_M
  • LM Studio Splits the model between GPU memory and system RAM, which this setup needs.
  • Jan Splits the model between GPU memory and system RAM, which this setup needs.

How we choose: the files published for this model, your operating system and GPU, whether it fits fully in memory, and published speed tests (MLX vs llama.cpp on Apple silicon, vLLM vs llama.cpp vs Ollama on NVIDIA). Details.

Download CompassJudger-2 32B

Recommended for the Intel Arc A580: 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-2-32B-Instruct-GGUF (opens in a new tab)

ollama run hf.co/mradermacher/CompassJudger-2-32B-Instruct-GGUF:Q5_K_M

Original weights · Hugging Face

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

huggingface.co/opencompass/CompassJudger-2-32B-Instruct (opens in a new tab)

No sign-in needed.

Links checked 1 day ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 83Great quality (A) · 83 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 51/100 (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 slowheavy RAM offload
How well it runs on the Intel Arc A580: 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.37.5 / 45
Memory fitFull marks when everything sits in GPU memory; fewer when part spills to system RAM.4.0 / 25
SpeedTime for one decision against a 100 ms target: reading the input plus any tokens the model writes.0.6 / 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 score51.1 / 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 Intel Arc A580

Quant Memory needed Available Speed Runs Quality Download
Q4_K_M 21.5 GB 8.0 GB 48.7 s Very slowheavy RAM offload 81Great quality (A) · 81 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 50/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 24.8 GB 8.0 GB 60.9 s Very slowheavy RAM offload 83Great quality (A) · 83 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 51/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q6_K 28.4 GB 8.0 GB — Won't run 85Excellent quality (A+) · 85 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 11/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q8_0 36.3 GB 8.0 GB — Won't run 86Excellent quality (A+) · 86 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 11/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
F16 67.1 GB 8.0 GB — Won't run 86Excellent quality (A+) · 86 of 100: how capable the model is at F16, whatever it runs on. Match score 11/100 (quality, fit, speed and memory use) sets the ranking. 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

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.

Was this page helpful?