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Can the AMD EPYC 7002 / 7003 series NAS (256 GB) run Mage-VL 4.7B?

53Fair quality (C) · 53 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 64/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs well

Runs well. At Q4_K_M it needs about 23.1 GB of 230 GB usable, with an estimated speed of 29.5 tok/s.

Best quant
Q4_K_M
Generation speed
29.5 tok/s
Prompt processing
84.4 tok/s
First token (1K prompt)
12.1 s
Largest safe context
256K

Download Mage-VL 4.7B

Original weights · Hugging Face

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

huggingface.co/microsoft/Mage-VL (opens in a new tab)

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How it’s rated

Quality 53Fair quality (C) · 53 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 64/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q4_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 Runs well
How well it runs on the AMD EPYC 7002 / 7003 series NAS (256 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.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.23.9 / 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
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.12.9 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.2.3 / 15
Match score64.1 / 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 EPYC 7002 / 7003 series NAS (256 GB)

Quant Memory needed Available Speed Runs Quality Download
Q2_K 22.1 GB 230 GB 48.4 tok/s Runs great 39Basic quality (D) · 39 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 59/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q3_K_M 22.6 GB 230 GB 38.1 tok/s Runs great 47Fair quality (C) · 47 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 63/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q4_K_M 23.1 GB 230 GB 29.5 tok/s Runs well 53Fair quality (C) · 53 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 64/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q5_K_M 23.6 GB 230 GB 24.3 tok/s Runs well 54Fair quality (C) · 54 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 63/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q6_K 24.1 GB 230 GB 20.4 tok/s Runs well 55Good quality (B) · 55 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 63/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q8_0 25.3 GB 230 GB 15.0 tok/s Runs well 56Good quality (B) · 56 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 61/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
F16 29.7 GB 230 GB 6.9 tok/s Slow 56Good quality (B) · 56 of 100: how capable the model is at F16, whatever it runs on. Match score 59/100 (quality, fit, speed and memory use) sets the ranking. Original weights (opens in a new tab)

Memory figures assume 8K of context. Longer conversations or documents grow the KV cache.

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.

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