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Can the NVIDIA RTX A4500 run LLM4Decompile 22B V2?

55Good quality (B) · 55 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 77/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs well (tight fit)

Runs well (tight fit). At Q4_K_M it needs about 17.1 GB of 20.0 GB usable, with an estimated speed of 35.3 tok/s.

Best quant
Q4_K_M
Generation speed
35.3 tok/s
Prompt processing
767 tok/s
First token (1K prompt)
1.3 s
Largest safe context
20K

Best app to run it on the NVIDIA RTX A4500

On Windows

LM Studio

Desktop app · runs on llama.cpp, MLX

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

Open LLM4Decompile 22B 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

LM Studio

Desktop app · runs on llama.cpp, MLX

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

Open LLM4Decompile 22B 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 22B V2

Recommended for the NVIDIA RTX A4500: 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/mradermacher/llm4decompile-22b-v2-GGUF (opens in a new tab)

ollama run hf.co/mradermacher/llm4decompile-22b-v2-GGUF:Q4_K_M

Original weights · Hugging Face

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

huggingface.co/LLM4Binary/llm4decompile-22b-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 55Good quality (B) · 55 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 77/100 (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 Runs welltight fit
How well it runs on the NVIDIA RTX A4500: 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.24.8 / 45
Memory fitFull marks when everything sits in GPU memory; fewer when part spills to system RAM.24.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.14.1 / 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 score77.9 / 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 A4500

Quant Memory needed Available Speed Runs Quality Download
Q2_K 12.4 GB 20.0 GB 58.0 tok/s Runs great 40Fair quality (C) · 40 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q3_K_M 14.4 GB 20.0 GB 45.6 tok/s Runs great 48Fair quality (C) · 48 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 76/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q4_K_M 17.1 GB 20.0 GB 35.3 tok/s Runs welltight fit 55Good quality (B) · 55 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 77/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 19.3 GB 20.0 GB 29.2 tok/s Runs welltight fit 56Good quality (B) · 56 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 77/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q6_K 21.7 GB 20.0 GB 13.5 tok/s Runs welloffloads to RAM 57Good quality (B) · 57 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 59/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q8_0 27.1 GB 20.0 GB 5.0 tok/s Slowoffloads to RAM 58Good quality (B) · 58 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 56/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
F16 48.0 GB 20.0 GB — Won't run 58Good quality (B) · 58 of 100: how capable the model is at F16, whatever it runs on. Match score 7/100 (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

Quick answers

How much memory does LLM4Decompile 22B V2 need on the NVIDIA RTX A4500 20GB?

At Q4_K_M it needs about 17.1 GB. The NVIDIA RTX A4500 20GB has 20.0 GB usable.

How fast is LLM4Decompile 22B V2 on the NVIDIA RTX A4500 20GB?

About 35.3 tok/s at Q4_K_M, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of LLM4Decompile 22B V2 should I use on the NVIDIA RTX A4500 20GB?

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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