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Can the NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition run LLM4Decompile 6.7B V2?

32Basic quality (D) · 32 of 100: how capable the model is at F16, whatever it runs on. Match score 59/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs great

Runs great. At F16 it needs about 23.2 GB of 96.0 GB usable, with an estimated speed of 76.6 tok/s.

Best quant
F16
Generation speed
76.6 tok/s
Prompt processing
11,751 tok/s
First token (1K prompt)
0.3 s
Largest safe context
8K

Best app to run it on the NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition

On Windows

LM Studio

Desktop app · runs on llama.cpp, MLX

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

Open LLM4Decompile 6.7B 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/…

On Linux

vLLM

Inference server · runs on vLLM

  • The full model fits in GPU memory at 8-bit, so it can batch many requests: best for serving a team or an app.
vllm serve LLM4Binary/llm4decompile-6.7b-v2
  • LM Studio Point-and-click: search, download and chat in one window.
  • llama.cpp Usually the first to support new model architectures.

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 6.7B V2

Recommended for the NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition: 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-6.7b-v2-GGUF (opens in a new tab)

ollama run hf.co/DevQuasar-7/LLM4Binary.llm4decompile-6.7b-v2-GGUF:F16

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 1 day ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 32Basic quality (D) · 32 of 100: how capable the model is at F16, whatever it runs on. Match score 59/100 (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 NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition: 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.14.4 / 45
Memory fitFull marks when everything sits in GPU memory; fewer when part spills to system RAM.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.5.6 / 15
Match score60.0 / 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 NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition

Quant Memory needed Available Speed Runs Quality Download
Q2_K 12.4 GB 96.0 GB 536 tok/s Runs great 22Basic quality (D) · 22 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 53/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q3_K_M 13.0 GB 96.0 GB 422 tok/s Runs great 26Basic quality (D) · 26 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 55/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q4_K_M 13.8 GB 96.0 GB 326 tok/s Runs great 30Basic quality (D) · 30 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 56/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 14.5 GB 96.0 GB 270 tok/s Runs great 31Basic quality (D) · 31 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 57/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q6_K 15.2 GB 96.0 GB 226 tok/s Runs great 31Basic quality (D) · 31 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 57/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q8_0 16.8 GB 96.0 GB 166 tok/s Runs great 32Basic quality (D) · 32 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 58/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
F16 23.2 GB 96.0 GB 76.6 tok/s Runs great 32Basic quality (D) · 32 of 100: how capable the model is at F16, whatever it runs on. Match score 59/100 (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 LLM4Decompile 6.7B V2 need on the NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition 96GB?

At F16 it needs about 23.2 GB. The NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition 96GB has 96.0 GB usable.

How fast is LLM4Decompile 6.7B V2 on the NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition 96GB?

About 76.6 tok/s at F16, 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 NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition 96GB?

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.

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