Can the RTX 5080 Mobile run Ling 3.0 Tiny?

Yes. Ling 3.0 Tiny fits the RTX 5080 Mobile's 16 GB at Q4_K_M (about 6.8 GB needed), running at an estimated 103 tok/s. There's room for up to Q8_0 if you want higher quality.

VRAM
16 GB
Needs at Q4
6.8 GB
Headroom
9.2 GB
Speed (Q4_K_M)
~103 tok/s

Every quantization

Ling 3.0 Tiny (7.9B) on 16 GB

QuantWeightsNeedsFitEst. speed
Q4_K_M5 GB6.8 GBFits easily~103 tok/s
Q5_K_M6 GB7.8 GBFits easily~86 tok/s
Q8_08 GB9.8 GBFits~64 tok/s
FP1616 GB17.8 GBToo big -

Estimates. Needs = weights + 1.8 GB for KV cache and runtime at modest context. Speed is single-stream decode scaled from the RTX 5080 Mobile's 896 GB/s bandwidth (Estimated from memory bandwidth (llama.cpp decode)). Long contexts need more memory and run slower.

Run it

Copy, paste, go

llama-server -hf inclusionAI/Ling-3.0-tiny-GGUF -ngl 99

Ollama pulls a Q4 build by default. Pick a larger quant only if the table above says it fits. Full Ling 3.0 Tiny details →

Compare

Same model, other hardware - and other models on this card

Ling 3.0 Tiny on other hardware

Also runs on the RTX 5080 Mobile

Frequently asked

Quick answers

Can the GeForce RTX 5080 Mobile run Ling 3.0 Tiny?

Yes. Ling 3.0 Tiny fits the RTX 5080 Mobile's 16 GB at Q4_K_M (about 6.8 GB needed), running at an estimated 103 tok/s. There's room for up to Q8_0 if you want higher quality.

How much VRAM does Ling 3.0 Tiny need?

About 5 GB of weights at Q4_K_M, 8 GB at Q8_0 and 16 GB at FP16. Add roughly 1.8 GB for the KV cache and runtime at modest context lengths.

How fast is Ling 3.0 Tiny on the RTX 5080 Mobile?

Roughly 103 tokens per second at Q4_K_M (single user, llama.cpp-style decode). This is an estimate from the RTX 5080 Mobile's 896 GB/s memory bandwidth.