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Can the AMD G-Series GX-420MC NAS (16 GB) run Hermes 4 14B?

62Good quality (B) · 62 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 68/100 (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 11.9 GB of 12.0 GB usable, with an estimated speed of 0.2 tok/s.

Best quant
Q4_K_M
Generation speed
0.2 tok/s
Prompt processing
0.3 tok/s
First token (1K prompt)
3,361.0 s
Largest safe context
8K

Best app to run it on the AMD G-Series GX-420MC NAS (16 GB)

On Linux

llama.cpp

Command line + local API · runs on llama.cpp

  • Usually the first to support new model architectures.
llama-server -hf bartowski/NousResearch_Hermes-4-14B-GGUF:Q4_K_M

Want an agent? Hermes 4 14B 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 4 14B

Recommended for the AMD G-Series GX-420MC NAS (16 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/bartowski/NousResearch_Hermes-4-14B-GGUF (opens in a new tab)

ollama run hf.co/bartowski/NousResearch_Hermes-4-14B-GGUF:Q4_K_M

Original weights · Hugging Face

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

huggingface.co/NousResearch/Hermes-4-14B (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 62Good quality (B) · 62 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 68/100 (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 AMD G-Series GX-420MC 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.28.2 / 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
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.1.1 / 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 score68.3 / 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 G-Series GX-420MC NAS (16 GB)

Quant Memory needed Available Speed Runs Quality Download
Q2_K 8.8 GB 12.0 GB 0.4 tok/s Very slow 46Fair quality (C) · 46 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 62/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q3_K_M 10.1 GB 12.0 GB 0.3 tok/s Very slow 55Good quality (B) · 55 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 66/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q4_K_M 11.9 GB 12.0 GB 0.2 tok/s Very slowtight fit 62Good quality (B) · 62 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 68/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 13.4 GB 12.0 GB 0.2 tok/s Very slowfar over memory limit 64Good quality (B) · 64 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 48/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q6_K 15.0 GB 12.0 GB — Won't run 65Good quality (B) · 65 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 8/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q8_0 18.6 GB 12.0 GB — Won't run 66Good quality (B) · 66 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 8/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
F16 32.4 GB 12.0 GB — Won't run 66Good quality (B) · 66 of 100: how capable the model is at F16, whatever it runs on. Match score 8/100 (quality, fit, speed and memory use) sets the ranking. GGUF (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

Quick answers

How much memory does Hermes 4 14B need on the AMD G-Series GX-420MC NAS 16GB?

At Q4_K_M it needs about 11.9 GB. The AMD G-Series GX-420MC NAS 16GB has 12.0 GB usable.

How fast is Hermes 4 14B on the AMD G-Series GX-420MC NAS 16GB?

About 0.2 tok/s at Q4_K_M, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of Hermes 4 14B should I use on the AMD G-Series GX-420MC NAS 16GB?

Q4_K_M. 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.

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