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Can the AMD EPYC 7002 / 7003 series NAS (16 GB) run MedGemma 27B Text IT?

49Fair quality (C) · 49 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 53/100 (quality, fit, speed and memory use) sets the ranking.

Yes, but slowly (over memory limit)

Slow (over memory limit). At Q2_K it needs about 13.2 GB of 12.0 GB usable, with an estimated speed of 7.2 tok/s.

Best quant
Q2_K
Generation speed
7.2 tok/s
Prompt processing
14.8 tok/s
First token (1K prompt)
69.1 s
Largest safe context
—

Best app to run it on the AMD EPYC 7002 / 7003 series NAS (16 GB)

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.
  • Usually the first to support new model architectures.
llama-server -hf unsloth/medgemma-27b-text-it-GGUF:Q2_K
  • Ollama Splits the model between GPU memory and system RAM, which this setup needs.
  • KoboldCpp Splits the model between GPU memory and system RAM, which this setup needs.

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 MedGemma 27B Text IT

Recommended for the AMD EPYC 7002 / 7003 series NAS (16 GB): Q2_K. 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 Q2_K file (opens in a new tab)

huggingface.co/unsloth/medgemma-27b-text-it-GGUF (opens in a new tab)

ollama run hf.co/unsloth/medgemma-27b-text-it-GGUF:Q2_K

Original weights · Hugging Face

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

huggingface.co/google/medgemma-27b-text-it (opens in a new tab)

Free Hugging Face account needed: accept the license on the model page and the download starts right away.

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

How it’s rated

Quality 49Fair quality (C) · 49 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 53/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q2_K, 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 Slowover memory limit
How well it runs on the AMD EPYC 7002 / 7003 series 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.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.22.1 / 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.10.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.6.4 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.15.0 / 15
Match score53.4 / 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 EPYC 7002 / 7003 series NAS (16 GB)

Quant Memory needed Available Speed Runs Quality Download
Q2_K 13.2 GB 12.0 GB 7.2 tok/s Slowover memory limit 49Fair quality (C) · 49 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 53/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q3_K_M 15.6 GB 12.0 GB — Won't run 58Good quality (B) · 58 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 7/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q4_K_M 18.8 GB 12.0 GB — Won't run 66Good quality (B) · 66 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 8/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 21.5 GB 12.0 GB — Won't run 67Good quality (B) · 67 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q6_K 24.5 GB 12.0 GB — Won't run 69Good quality (B) · 69 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q8_0 31.0 GB 12.0 GB — Won't run 70Great quality (A) · 70 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
F16 56.4 GB 12.0 GB — Won't run 70Great quality (A) · 70 of 100: how capable the model is at F16, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking. GGUF · folder (opens in a new tab) 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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