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Can the Apple M3 Max (64 GB) run WeVisOne VL 2B?

27Basic quality (D) · 27 of 100: how capable the model is at F16, whatever it runs on. Match score 57/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs great

Runs great. At F16 it needs about 10.3 GB of 48.0 GB usable, with an estimated speed of 40.7 tok/s.

Best quant
F16
Generation speed
40.7 tok/s
Prompt processing
4,167 tok/s
First token (1K prompt)
0.3 s
Largest safe context
256K

Best app to run it on the Apple M3 Max (64 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 2B 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 2B

Recommended for the Apple M3 Max (64 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-2B-GGUF (opens in a new tab)

ollama run hf.co/mradermacher/WeVisOne-VL-2B-GGUF:F16

Original weights · Hugging Face

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

huggingface.co/tencent/WeVisOne-VL-2B (opens in a new tab)

No sign-in needed.

Links checked 1 day ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 27Basic quality (D) · 27 of 100: how capable the model is at F16, whatever it runs on. Match score 57/100 (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 Max (64 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.12.2 / 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.4.9 / 15
Match score57.1 / 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 Max (64 GB)

Quant Memory needed Available Speed Runs Quality Download
Q2_K 6.4 GB 48.0 GB 285 tok/s Runs great 18Basic quality (D) · 18 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 51/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q3_K_M 6.6 GB 48.0 GB 224 tok/s Runs great 22Basic quality (D) · 22 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 53/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q4_K_M 6.9 GB 48.0 GB 173 tok/s Runs great 25Basic quality (D) · 25 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 54/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 7.1 GB 48.0 GB 143 tok/s Runs great 26Basic quality (D) · 26 of 100: how capable the model is at Q5_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)
Q6_K 7.4 GB 48.0 GB 120 tok/s Runs great 26Basic quality (D) · 26 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 55/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q8_0 8.0 GB 48.0 GB 88.2 tok/s Runs great 27Basic quality (D) · 27 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. GGUF (opens in a new tab)
F16 10.3 GB 48.0 GB 40.7 tok/s Runs great 27Basic quality (D) · 27 of 100: how capable the model is at F16, whatever it runs on. Match score 57/100 (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 2B need on the M3 Max 64GB?

At F16 it needs about 10.3 GB. The M3 Max 64GB has 48.0 GB usable.

How fast is WeVisOne VL 2B on the M3 Max 64GB?

About 40.7 tok/s at F16, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of WeVisOne VL 2B should I use on the M3 Max 64GB?

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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