Can you run Ling 3.0 Tiny locally?
Yes—even on some 8–12GB devices. The recommended Q4_K_M GGUF is 4.9GB and needs about 6.3GB at 4K context before OS reserve. Use Q2_K only when memory is the hard limit; 12GB or 16GB is the better Q4 target.
Straight answer by memory tier
These are capacity estimates, not AICanRun speed measurements. They include the real file size, estimated weight overhead, a 4K-context KV cache and runtime buffers; your OS and app need reserve on top.
| Hardware memory | Verdict | Best starting point |
|---|---|---|
| 8GB Mac / PC | Borderline | Q2_K, 4K context; close other apps |
| 8GB Android | Possible | Q2_K only; short context and a lightweight frontend |
| 12GB | Yes | Q4_K_M with useful OS and context headroom |
| 16GB | Yes, comfortable | Q4_K_M; Q8_0 also fits with tighter headroom |
| 24GB+ | Yes | Q8_0 and longer context become practical |
Use llama.cpp release b10472 or newer because the Bailing MoE V3 architecture is newly supported. Start at 4K context, confirm a clean load, then raise context gradually. Thinking mode is enabled by default.
llama-server -hf bartowski/Ling-3.0-tiny-GGUF:Q4_K_M --ctx-size 4096Why “1.4B active” does not mean a 1.4B download
Ling routes each token through a subset of its experts, so decode compute can resemble a much smaller model. Storage follows the 7.9B total, however: every expert remains in the GGUF. That is why Q4 is 4.9GB, not roughly one gigabyte. InclusionAI reports an 86–90 tok/s FP8 run on an M4 Pro MacBook at 8K context; that publisher result is useful evidence, but it is a different precision and is not our own benchmark.
Quick answers
Can I run Ling 3.0 Tiny locally?
Yes. Its recommended Q4_K_M GGUF is 4.9GB and the estimated working set is about 6.3GB at 4K context before operating-system reserve. A 12GB device is practical; a 16GB Mac or PC gives comfortable headroom.
Can Ling 3.0 Tiny run with 8GB RAM or VRAM?
Only with compromises. The 3GB Q2_K file has an estimated 4.3GB 4K working set, but quality is lower and an 8GB computer still has little room after its operating system. Q4_K_M is too tight for most 8GB shared-memory machines.
Can a phone run Ling 3.0 Tiny?
A high-memory Android phone can. Start with Q2_K on an 8GB phone or Q4_K_M on a 12GB+ phone, keep context short, and expect frontend and mobile-OS limits to matter. This is a capacity estimate, not a measured phone benchmark.
How large is Ling 3.0 Tiny?
The official model has 7.9B total parameters and activates 1.4B per token. MoE activation can reduce compute and improve decoding speed, but all expert weights still need to fit in memory.
Is Ling 3.0 Tiny open source?
InclusionAI publishes the open weights under the MIT license. The tracked GGUF is a third-party Bartowski quantization because the publisher does not currently provide an official GGUF repository.
Primary source: InclusionAI official model card. Quant source: Bartowski GGUF repository. The imatrix helper file is excluded from model weight totals. No speed estimate on this page is labeled as an AICanRun measurement.