WeVisOne VL 4B
WeVisOne VL 4B is an open-weights language model from Tencent, released October 22, 2026. It has 4.8B parameters (dense). The context window is 256K tokens.
Check it against your own PC →
Can you run WeVisOne VL 4B locally?
The smallest common GPU it runs on is 6 GB of VRAM (with 32 GB of RAM): Q6_K, split GPU / CPU, roughly 12 tokens per second. Estimates assume 32 GB of DDR5 RAM, a 16K context and llama.cpp-style GGUF quantization. Enter your exact hardware in the live app.
| GPU memory | Verdict | Best quant | VRAM | RAM | tok/s |
|---|---|---|---|---|---|
| 6 GB VRAM | Runs, slowly | Q6_K (≈ lossless) | 5.2 GB | 1.8 GB | ~12 |
| 8 GB VRAM | Runs on GPU | Q6_K (≈ lossless) | 7.0 GB | – | ~40 |
| 12 GB VRAM | Runs on GPU | Q6_K (≈ lossless) | 7.0 GB | – | ~52 |
| 16 GB VRAM | Runs on GPU | Q6_K (≈ lossless) | 7.0 GB | – | ~70 |
| 24 GB VRAM | Runs on GPU | Q6_K (≈ lossless) | 7.0 GB | – | ~120 |
| 32 GB VRAM | Runs on GPU | Q6_K (≈ lossless) | 7.0 GB | – | ~210 |
Ready-made quantizations on Hugging Face: mradermacher/WeVisOne-VL-4B-GGUF · prithivMLmods/tencent-WeVisOne-VL-4B-GGUF · SandLogicTechnologies/WeVisOne-VL-4B-GGUF · litert-community/WeVisOne-VL-4B
Benchmark results
Epoch AI has not published results for WeVisOne VL 4B yet. New models are usually evaluated within days to weeks.
Links
Hugging Face: tencent/WeVisOne-VL-4B
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