Prometheus 2 7B on AMD EPYC 7002 / 7003 series NAS (512 GB)

Can the AMD EPYC 7002 / 7003 series NAS (512 GB) run Prometheus 2 7B?

Barely: it runs very slowly

Very slow. At Q8_0 it needs about 46.2 GB of 471 GB usable and makes one decision in about 33.8 s.

Best quant: Q8_0
Time per decision: 33.8 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 AMD EPYC 7002 / 7003 series NAS (512 GB)

On Linux

llama.cpp

Command line + local API · runs on llama.cpp

Usually the first to support new model architectures.

llama-server -hf prometheus-eval/prometheus-7b-v2.0-GGUF:Q8_0

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 Prometheus 2 7B

Quantized files · GGUF

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

huggingface.co/prometheus-eval/prometheus-7b-v2.0-GGUF (opens in a new tab)

ollama run hf.co/prometheus-eval/prometheus-7b-v2.0-GGUF

Original weights · Hugging Face

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

huggingface.co/prometheus-eval/prometheus-7b-v2.0 (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 68Good quality (B) · 68 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 65/100 (quality, fit, speed and memory use) sets the ranking.: How capable the model itself is at Q8_0, 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 EPYC 7002 / 7003 series NAS (512 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.	30.6 / 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.	0.8 / 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 score	65.4 / 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 (512 GB)

Quant	Memory needed	Available	Speed	Runs	Quality	Download
`Q4_K_M`	43.0 GB	471 GB	26.3 s	Very slow	64Good quality (B) · 64 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 63/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q5_K_M`	43.7 GB	471 GB	28.0 s	Very slow	65Good quality (B) · 65 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 64/100 (quality, fit, speed and memory use) sets the ranking.	GGUF files (opens in a new tab)
`Q6_K`	44.5 GB	471 GB	29.7 s	Very slow	67Good quality (B) · 67 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 65/100 (quality, fit, speed and memory use) sets the ranking.	GGUF files (opens in a new tab)
`Q8_0`	46.2 GB	471 GB	33.8 s	Very slow	68Good quality (B) · 68 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 65/100 (quality, fit, speed and memory use) sets the ranking.	GGUF files (opens in a new tab)
`F16`	53.0 GB	471 GB	51.7 s	Very slow	68Good quality (B) · 68 of 100: how capable the model is at F16, whatever it runs on. Match score 65/100 (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

64Good quality (B) · 64 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 74/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA B200 Q4_K_M · 179 ms · Runs well
67Good quality (B) · 67 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. AMD Instinct MI350X Q6_K · 276 ms · Runs well
67Good quality (B) · 67 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GB300 Grace Blackwell Ultra Desktop Superchip Q6_K · 312 ms · Runs well
67Good quality (B) · 67 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking. AMD Instinct MI325X Q6_K · 324 ms · Runs well
67Good quality (B) · 67 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA H200 141GB SXM Q6_K · 347 ms · Runs well
67Good quality (B) · 67 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA H200 NVL Q6_K · 355 ms · Runs well

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.