LLM4Decompile 6.7B V2 on AMD Ryzen Embedded V3C14 NAS (16 GB)

Can the AMD Ryzen Embedded V3C14 NAS (16 GB) run LLM4Decompile 6.7B V2?

Yes, but slowly

Slow. At Q4_K_M it needs about 10.0 GB of 12.0 GB usable, with an estimated speed of 5.8 tok/s.

Best quant: Q4_K_M
Generation speed: 5.8 tok/s
Prompt processing: 17.8 tok/s
First token (1K prompt): 57.5 s
Largest safe context: 8K

Best app to run it on the AMD Ryzen Embedded V3C14 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 DevQuasar-7/LLM4Binary.llm4decompile-6.7b-v2-GGUF:Q4_K_M

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

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 LLM4Decompile 6.7B V2

Recommended for the AMD Ryzen Embedded V3C14 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/DevQuasar-7/LLM4Binary.llm4decompile-6.7b-v2-GGUF (opens in a new tab)

ollama run hf.co/DevQuasar-7/LLM4Binary.llm4decompile-6.7b-v2-GGUF:Q4_K_M

Original weights · Hugging Face

Full-precision files from LLM4Binary, for fine-tuning, vLLM, Transformers.

huggingface.co/LLM4Binary/llm4decompile-6.7b-v2 (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from LLM4Binary. The weights themselves download from Hugging Face.

github.com/albertan017/LLM4Decompile (opens in a new tab)

Official code and the Ghidra script that feeds decompiler output to the V2 models.

git clone https://github.com/albertan017/LLM4Decompile

Links checked 2 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 30Basic quality (D) · 30 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 59/100 (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 Slow: How well it runs on the AMD Ryzen Embedded V3C14 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.	13.7 / 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.	5.7 / 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 score	59.4 / 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 Ryzen Embedded V3C14 NAS (16 GB)

Quant	Memory needed	Available	Speed	Runs	Quality	Download
`Q2_K`	8.6 GB	12.0 GB	9.6 tok/s	Slow	22Basic quality (D) · 22 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 57/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q3_K_M`	9.2 GB	12.0 GB	7.5 tok/s	Slow	26Basic quality (D) · 26 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 58/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q4_K_M`	10.0 GB	12.0 GB	5.8 tok/s	Slow	30Basic quality (D) · 30 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 59/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q5_K_M`	10.6 GB	12.0 GB	4.8 tok/s	Slowtight fit	31Basic quality (D) · 31 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 58/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q6_K`	11.4 GB	12.0 GB	4.0 tok/s	Slowtight fit	31Basic quality (D) · 31 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 58/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q8_0`	13.0 GB	12.0 GB	2.5 tok/s	Very slowover memory limit	32Basic quality (D) · 32 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 43/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`F16`	19.3 GB	12.0 GB	—	Won't run	32Basic quality (D) · 32 of 100: how capable the model is at F16, whatever it runs on. Match score 4/100 (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

32Basic quality (D) · 32 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 69/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 9070 XT Q8_0 · 51.1 tok/s · Runs great
32Basic quality (D) · 32 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 69/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4070 Ti SUPER Q8_0 · 62.3 tok/s · Runs great
32Basic quality (D) · 32 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 69/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4080 SUPER Q8_0 · 68.2 tok/s · Runs great
32Basic quality (D) · 32 of 100: how capable the model is at F16, whatever it runs on. Match score 69/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4090 F16 · 43.1 tok/s · Runs great
32Basic quality (D) · 32 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 69/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5060 Ti 16GB Q8_0 · 41.5 tok/s · Runs great
32Basic quality (D) · 32 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 69/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5070 Ti Q8_0 · 83.1 tok/s · Runs great

Quick answers

How much memory does LLM4Decompile 6.7B V2 need on the AMD Ryzen Embedded V3C14 NAS 16GB?

At Q4_K_M it needs about 10.0 GB. The AMD Ryzen Embedded V3C14 NAS 16GB has 12.0 GB usable.

How fast is LLM4Decompile 6.7B V2 on the AMD Ryzen Embedded V3C14 NAS 16GB?

About 5.8 tok/s at Q4_K_M, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of LLM4Decompile 6.7B V2 should I use on the AMD Ryzen Embedded V3C14 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.