Can the RTX 5080 Mobile run Qwen2.5-Coder-14B-Instruct?

Yes. Qwen2.5-Coder-14B-Instruct fits the RTX 5080 Mobile's 16 GB at Q4_K_M (about 10.8 GB needed), running at an estimated 57 tok/s. There's room for up to Q5_K_M if you want higher quality.

VRAM
16 GB
Needs at Q4
10.8 GB
Headroom
5.2 GB
Speed (Q4_K_M)
~57 tok/s

Every quantization

Qwen2.5-Coder-14B-Instruct (14.8B) on 16 GB

QuantWeightsNeedsFitEst. speed
Q4_K_M9 GB10.8 GBFits~57 tok/s
Q5_K_M11 GB12.8 GBFits~47 tok/s
Q8_016 GB17.8 GBToo big -
FP1630 GB31.8 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 Qwen/Qwen2.5-Coder-14B-Instruct-GGUF -ngl 99

Ollama pulls a Q4 build by default. Pick a larger quant only if the table above says it fits. Full Qwen2.5-Coder-14B-Instruct details →

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

Qwen2.5-Coder-14B-Instruct on other hardware

Also runs on the RTX 5080 Mobile

Frequently asked

Quick answers

Can the GeForce RTX 5080 Mobile run Qwen2.5-Coder-14B-Instruct?

Yes. Qwen2.5-Coder-14B-Instruct fits the RTX 5080 Mobile's 16 GB at Q4_K_M (about 10.8 GB needed), running at an estimated 57 tok/s. There's room for up to Q5_K_M if you want higher quality.

How much VRAM does Qwen2.5-Coder-14B-Instruct need?

About 9 GB of weights at Q4_K_M, 16 GB at Q8_0 and 30 GB at FP16. Add roughly 1.8 GB for the KV cache and runtime at modest context lengths.

How fast is Qwen2.5-Coder-14B-Instruct on the RTX 5080 Mobile?

Roughly 57 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.