Can the RTX Spark N1X (128 GB) run Ling 3.0 Tiny?

Yes. Ling 3.0 Tiny fits the RTX Spark N1X (128 GB)'s 128 GB at Q4_K_M (about 6.8 GB needed), running at an estimated 31 tok/s. There's room for up to FP16 if you want higher quality.

Memory
128 GB
Needs at Q4
6.8 GB
Headroom
121.2 GB
Speed (Q4_K_M)
~31 tok/s

Every quantization

Ling 3.0 Tiny (7.9B) on 128 GB

QuantWeightsNeedsFitEst. speed
Q4_K_M5 GB6.8 GBFits easily~31 tok/s
Q5_K_M6 GB7.8 GBFits easily~26 tok/s
Q8_08 GB9.8 GBFits easily~20 tok/s
FP1616 GB17.8 GBFits easily~9.8 tok/s

Estimates. Needs = weights + 1.8 GB for KV cache and runtime at modest context. Speed is single-stream decode scaled from the RTX Spark N1X (128 GB)'s 273 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 →

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

Ling 3.0 Tiny on other hardware

Also runs on the RTX Spark N1X (128 GB)

Frequently asked

Quick answers

Can the NVIDIA RTX Spark N1X (128 GB) run Ling 3.0 Tiny?

Yes. Ling 3.0 Tiny fits the RTX Spark N1X (128 GB)'s 128 GB at Q4_K_M (about 6.8 GB needed), running at an estimated 31 tok/s. There's room for up to FP16 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 Spark N1X (128 GB)?

Roughly 31 tokens per second at Q4_K_M (single user, llama.cpp-style decode). This is an estimate from the RTX Spark N1X (128 GB)'s 273 GB/s memory bandwidth.