Ling-3.0-flash-VL: hardware requirements
Ling-3.0-flash-VL needs about 85 GB of memory at Q4_K_M quantization: a 79 GB download plus room for context and a runtime allowance. That fits a machine with 128GB RAM.
This is a mixture-of-experts model. Only a fraction of its 125B parameters are used for each token, so it runs faster than its size suggests, but all of it still has to fit in memory. The speed is that of a small model; the memory requirement is that of a large one. More on this →
Will it run on your machine?
Use it without your own hardware
OpenRouter lists Ling 3.0 Flash VL at $0.06 per million input tokens and $0.18 per million output tokens. Artificial Analysis measured hosted providers generating 147 tokens per second (Oct 12, 2026).
Price as listed on OpenRouter today. Speed is for hosted servers, not a home computer.
Download sizes by quantization
Lower quantization means a smaller file and less memory, at some cost to quality. Q4_K_M is the usual starting point. What is this?
| Quant | Download | RAM @ 4K | RAM @ 32K | Source |
|---|---|---|---|---|
| Q4_K_Mrecommended | 79 GB | 85 GB | 104 GB | bartowski/Ling-3.0-flash-VL-GGUF |
| Q5_K_M | 96 GB | 102 GB | 122 GB | bartowski/Ling-3.0-flash-VL-GGUF |
| Q6_K | 110 GB | 116 GB | 135 GB | bartowski/Ling-3.0-flash-VL-GGUF |
| Q8_0 | 132 GB | 138 GB | 158 GB | bartowski/Ling-3.0-flash-VL-GGUF |
| BF16 | 250 GB | 256 GB | 276 GB | bartowski/Ling-3.0-flash-VL-GGUF |
Download sizes are measured from the published files named in the Source column. The RAM columns add the context cache, calculated from the model's configuration, and a runtime allowance of 10% of the download (at least 0.5GB, at most 3GB).