LLM4Decompile 1.3B V2 on Apple M3 Ultra (256 GB)

Can the Apple M3 Ultra (256 GB) run LLM4Decompile 1.3B V2?

Yes, it runs great

Runs great. At F16 it needs about 20.3 GB of 192 GB usable, with an estimated speed of 150 tok/s.

Best quant: F16
Generation speed: 150 tok/s
Prompt processing: 13,043 tok/s
First token (1K prompt): 0.3 s
Largest safe context: 16K

Best app to run it on the Apple M3 Ultra (256 GB)

On macOS

LM Studio

Desktop app · runs on llama.cpp, MLX

Point-and-click: search, download and chat in one window.

Open LLM4Decompile 1.3B V2 in LM Studio

llama.cpp Usually the first to support new model architectures.
Ollama Runs the Hugging Face GGUF with ollama run hf.co/…

How we choose: the files published for this model, your operating system and GPU, whether it fits fully in memory, and published speed tests (MLX vs llama.cpp on Apple silicon, vLLM vs llama.cpp vs Ollama on NVIDIA). Details.

Download LLM4Decompile 1.3B V2

Recommended for the Apple M3 Ultra (256 GB): F16. 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 F16 file (opens in a new tab)

huggingface.co/DevQuasar-7/LLM4Binary.llm4decompile-1.3b-v2-GGUF (opens in a new tab)

ollama run hf.co/DevQuasar-7/LLM4Binary.llm4decompile-1.3b-v2-GGUF:F16

Original weights · Hugging Face

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

huggingface.co/LLM4Binary/llm4decompile-1.3b-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 3 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 12Basic quality (D) · 12 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.: How capable the model itself is at F16, 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 Runs great: How well it runs on the Apple M3 Ultra (256 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.	5.4 / 45
Memory fitFull marks when the model fits in the share of unified memory the GPU can use; 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.	15.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.	2.4 / 15
Match score	47.8 / 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 Apple M3 Ultra (256 GB)

Quant	Memory needed	Available	Speed	Runs	Quality	Download
`Q2_K`	18.1 GB	192 GB	1,053 tok/s	Runs great	8Basic quality (D) · 8 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 45/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q3_K_M`	18.2 GB	192 GB	829 tok/s	Runs great	10Basic quality (D) · 10 of 100: how capable the model is at Q3_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)
`Q4_K_M`	18.4 GB	192 GB	641 tok/s	Runs great	11Basic quality (D) · 11 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 47/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q5_K_M`	18.5 GB	192 GB	530 tok/s	Runs great	11Basic quality (D) · 11 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 47/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q6_K`	18.7 GB	192 GB	445 tok/s	Runs great	11Basic quality (D) · 11 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 47/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q8_0`	19.0 GB	192 GB	326 tok/s	Runs great	12Basic quality (D) · 12 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 47/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`F16`	20.3 GB	192 GB	150 tok/s	Runs great	12Basic quality (D) · 12 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.	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

12Basic quality (D) · 12 of 100: how capable the model is at F16, whatever it runs on. Match score 60/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4060 F16 · 58.0 tok/s · Runs great
12Basic quality (D) · 12 of 100: how capable the model is at F16, whatever it runs on. Match score 55/100 (quality, fit, speed and memory use) sets the ranking. Intel Arc B580 F16 · 74.3 tok/s · Runs great
12Basic quality (D) · 12 of 100: how capable the model is at F16, whatever it runs on. Match score 55/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 3060 12GB F16 · 76.8 tok/s · Runs great
12Basic quality (D) · 12 of 100: how capable the model is at F16, whatever it runs on. Match score 55/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4070 SUPER F16 · 108 tok/s · Runs great
12Basic quality (D) · 12 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 55/100 (quality, fit, speed and memory use) sets the ranking. Apple M4 (16 GB) Q8_0 · 47.8 tok/s · Runs great
12Basic quality (D) · 12 of 100: how capable the model is at F16, whatever it runs on. Match score 54/100 (quality, fit, speed and memory use) sets the ranking. Apple M4 Pro (24 GB) F16 · 50.2 tok/s · Runs great

Quick answers

How much memory does LLM4Decompile 1.3B V2 need on the M3 Ultra 256GB?

At F16 it needs about 20.3 GB. The M3 Ultra 256GB has 192 GB usable.

How fast is LLM4Decompile 1.3B V2 on the M3 Ultra 256GB?

About 150 tok/s at F16, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of LLM4Decompile 1.3B V2 should I use on the M3 Ultra 256GB?

F16. 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.