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Can the Apple M2 Ultra (192 GB) run WeVisOne-VL 4B?

45Fair quality (C) · 45 of 100: how capable the model is at F16, whatever it runs on. Match score 63/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs great

Runs great. At F16 it needs about 23.0 GB of 144 GB usable, with an estimated speed of 41.1 tok/s.

Best quant
F16
Generation speed
41.1 tok/s
Prompt processing
3,492 tok/s
First token (1K prompt)
0.3 s
Largest safe context
256K

Best app to run it on the Apple M2 Ultra (192 GB)

On macOS

LM Studio

Desktop app · runs on llama.cpp, MLX

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

Open WeVisOne-VL 4B 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 WeVisOne-VL 4B

Recommended for the Apple M2 Ultra (192 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/mradermacher/WeVisOne-VL-4B-GGUF (opens in a new tab)

ollama run hf.co/mradermacher/WeVisOne-VL-4B-GGUF:F16

Original weights · Hugging Face

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

huggingface.co/tencent/WeVisOne-VL-4B (opens in a new tab)

No sign-in needed.

Official repo · GitHub

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

github.com/Tencent/WeVisOne-VL (opens in a new tab)

git clone https://github.com/Tencent/WeVisOne-VL

Links checked 3 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 45Fair quality (C) · 45 of 100: how capable the model is at F16, whatever it runs on. Match score 63/100 (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 M2 Ultra (192 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.20.3 / 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.3.7 / 15
Match score63.9 / 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 Apple M2 Ultra (192 GB)

Quant Memory needed Available Speed Runs Quality Download
Q2_K 15.3 GB 144 GB 288 tok/s Runs great 31Basic quality (D) · 31 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 56/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q3_K_M 15.7 GB 144 GB 226 tok/s Runs great 37Basic quality (D) · 37 of 100: how capable the model is at Q3_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)
Q4_K_M 16.3 GB 144 GB 175 tok/s Runs great 42Fair quality (C) · 42 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 61/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 16.8 GB 144 GB 145 tok/s Runs great 43Fair quality (C) · 43 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 62/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q6_K 17.3 GB 144 GB 121 tok/s Runs great 44Fair quality (C) · 44 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 62/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q8_0 18.4 GB 144 GB 89.1 tok/s Runs great 45Fair quality (C) · 45 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 63/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
F16 23.0 GB 144 GB 41.1 tok/s Runs great 45Fair quality (C) · 45 of 100: how capable the model is at F16, whatever it runs on. Match score 63/100 (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 WeVisOne-VL 4B need on the M2 Ultra 192GB?

At F16 it needs about 23.0 GB. The M2 Ultra 192GB has 144 GB usable.

How fast is WeVisOne-VL 4B on the M2 Ultra 192GB?

About 41.1 tok/s at F16, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of WeVisOne-VL 4B should I use on the M2 Ultra 192GB?

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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