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Can the Intel Core Ultra X7 358H NAS (64 GB) run Prometheus 2 8x7B?

70Great quality (A) · 70 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 65/100 (quality, fit, speed and memory use) sets the ranking.

Barely: it runs very slowly (tight fit)

Very slow (tight fit). At Q4_K_M it needs about 32.3 GB of 36.5 GB usable and makes one decision in about 15.6 s.

Best quant
Q4_K_M
Time per decision
15.6 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 Intel Core Ultra X7 358H NAS (64 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/prometheus-8x7b-v2.0-GGUF:Q4_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 Prometheus 2 8x7B

Recommended for the Intel Core Ultra X7 358H NAS (64 GB): Q4_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 Q4_K_M file (opens in a new tab)

huggingface.co/mradermacher/prometheus-8x7b-v2.0-GGUF (opens in a new tab)

ollama run hf.co/mradermacher/prometheus-8x7b-v2.0-GGUF:Q4_K_M

Original weights · Hugging Face

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

huggingface.co/prometheus-eval/prometheus-8x7b-v2.0 (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 70Great quality (A) · 70 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 65/100 (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 Very slowtight fit
How well it runs on the Intel Core Ultra X7 358H NAS (64 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.31.6 / 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.24.0 / 25
SpeedTime for one decision against a 100 ms target: reading the input plus any tokens the model writes.1.2 / 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 score65.8 / 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 Core Ultra X7 358H NAS (64 GB)

Quant Memory needed Available Speed Runs Quality Download
Q4_K_M 32.3 GB 36.5 GB 15.6 s Very slowtight fit 70Great quality (A) · 70 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 65/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 36.9 GB 36.5 GB 21.7 s Very slowover GPU memory limit 71Great quality (A) · 71 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 52/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q6_K 42.1 GB 36.5 GB 25.6 s Very slowfar over GPU memory limit 73Great quality (A) · 73 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 46/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q8_0 53.3 GB 36.5 GB — Won't run 74Great quality (A) · 74 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 97.2 GB 36.5 GB — Won't run 74Great quality (A) · 74 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. 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 Prometheus 2 8x7B need on the Intel Core Ultra X7 358H NAS 64GB?

At Q4_K_M it needs about 32.3 GB. The Intel Core Ultra X7 358H NAS 64GB has 36.5 GB usable.

How fast is Prometheus 2 8x7B on the Intel Core Ultra X7 358H NAS 64GB?

It makes one decision in about 15.6 s at Q4_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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