Hermes 2 Pro Mistral 7B on Intel Atom C3538 / C3558 NAS (128 GB)

Can the Intel Atom C3538 / C3558 NAS (128 GB) run Hermes 2 Pro Mistral 7B?

Barely: it runs very slowly

Very slow. At Q6_K it needs about 16.6 GB of 113 GB usable, with an estimated speed of 0.9 tok/s.

Best quant: Q6_K
Generation speed: 0.9 tok/s
Prompt processing: 2.5 tok/s
First token (1K prompt): 411.9 s
Largest safe context: 32K

Best app to run it on the Intel Atom C3538 / C3558 NAS (128 GB)

On Linux

llama.cpp

Command line + local API · runs on llama.cpp

Usually the first to support new model architectures.

llama-server -hf NousResearch/Hermes-2-Pro-Mistral-7B-GGUF:Q6_K

Ollama Runs the Hugging Face GGUF with ollama run hf.co/…
KoboldCpp

Want an agent? Hermes 2 Pro Mistral 7B supports tool use, so you can pair a llama.cpp or MLX LM server with one of these. Apps that run on this computer come first.

Coding agents

Personal assistants

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 Hermes 2 Pro Mistral 7B

Recommended for the Intel Atom C3538 / C3558 NAS (128 GB): Q6_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 Q6_K file (opens in a new tab)

huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B-GGUF (opens in a new tab)

ollama run hf.co/NousResearch/Hermes-2-Pro-Mistral-7B-GGUF:Q6_K

Original weights · Hugging Face

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

huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B (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 35Basic quality (D) · 35 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.: How capable the model itself is at Q6_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 Very slow: How well it runs on the Intel Atom C3538 / C3558 NAS (128 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.	16.0 / 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.	2.3 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.	3.4 / 15
Match score	46.7 / 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 Atom C3538 / C3558 NAS (128 GB)

Quant	Memory needed	Available	Speed	Runs	Quality	Download
`Q2_K`	13.6 GB	113 GB	2.2 tok/s	Very slow	25Basic quality (D) · 25 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 42/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q3_K_M`	14.2 GB	113 GB	1.8 tok/s	Very slow	30Basic quality (D) · 30 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 44/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q4_K_M`	15.1 GB	113 GB	1.4 tok/s	Very slow	34Basic quality (D) · 34 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 46/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q5_K_M`	15.8 GB	113 GB	1.1 tok/s	Very slow	34Basic quality (D) · 34 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 46/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q6_K`	16.6 GB	113 GB	0.9 tok/s	Very slow	35Basic quality (D) · 35 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`	18.4 GB	113 GB	0.7 tok/s	Very slow	36Basic quality (D) · 36 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 46/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`F16`	25.2 GB	113 GB	0.3 tok/s	Very slow	36Basic quality (D) · 36 of 100: how capable the model is at F16, whatever it runs on. Match score 47/100 (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

36Basic quality (D) · 36 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 71/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4070 SUPER Q8_0 · 43.5 tok/s · Runs great
36Basic quality (D) · 36 of 100: how capable the model is at F16, whatever it runs on. Match score 71/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4090 F16 · 40.1 tok/s · Runs great
35Basic quality (D) · 35 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 71/100 (quality, fit, speed and memory use) sets the ranking. Intel Arc B580 Q6_K · 41.0 tok/s · Runs great
35Basic quality (D) · 35 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 71/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 3060 12GB Q6_K · 42.4 tok/s · Runs great
36Basic quality (D) · 36 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 70/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 9070 XT Q8_0 · 47.6 tok/s · Runs great
36Basic quality (D) · 36 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 70/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4070 Ti SUPER Q8_0 · 58.0 tok/s · Runs great

Quick answers

How much memory does Hermes 2 Pro Mistral 7B need on the Intel Atom C3538 / C3558 NAS 128GB?

At Q6_K it needs about 16.6 GB. The Intel Atom C3538 / C3558 NAS 128GB has 113 GB usable.

How fast is Hermes 2 Pro Mistral 7B on the Intel Atom C3538 / C3558 NAS 128GB?

About 0.9 tok/s at Q6_K, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of Hermes 2 Pro Mistral 7B should I use on the Intel Atom C3538 / C3558 NAS 128GB?

Q6_K. Of the versions that fit, it is the one that runs best; when two run equally well, the check prefers the higher-rated one.

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.