Ling Mini 2.0 (16B-A1.4B) on Intel Arc A580

Can the Intel Arc A580 run Ling Mini 2.0 (16B-A1.4B)?

70 pts

Yes, with little memory to spare

Tight fit. At Q2_K it needs about 7.8 GB of 8.0 GB usable, with an estimated speed of 282 tok/s.

Best quant: Q2_K
Generation speed: 282 tok/s
Prompt processing: 9,664 tok/s
First token (1K prompt): 0.3 s
Largest safe context: 11K

Download Ling Mini 2.0 (16B-A1.4B)

Recommended for the Intel Arc A580: Q2_K. Every other quantization has its own download link in the table below.

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

Download the Q2_K file (opens in a new tab)

huggingface.co/inclusionAI/Ling-mini-2.0-GGUF (opens in a new tab)

ollama run hf.co/inclusionAI/Ling-mini-2.0-GGUF:Q2_K

Original weights · Hugging Face

Full-precision files from Ant Group (inclusionAI), for fine-tuning, vLLM, Transformers.

huggingface.co/inclusionAI/Ling-mini-2.0 (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from Ant Group (inclusionAI). The weights themselves download from Hugging Face.

github.com/inclusionAI/Ling-V2 (opens in a new tab)

git clone https://github.com/inclusionAI/Ling-V2

Links checked 7 days ago against Hugging Face, Ollama and GitHub.

How the B grade is scored

Every result gets up to 100 points from four parts. The points decide the letter grade.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.	16.4 / 45
Memory fitFull marks when everything sits in GPU or unified memory; fewer when part spills to system RAM.	24.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.	15.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.	15.0 / 15
Total	70.4 / 100	70 ptsGood (B) · 70 of 100 points: Model quality 16.4/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15

Grade scale: S 85+ · A 72+ · B 58+ · C 42+ · D 25+ · F under 25 points.

Every quantization on the Intel Arc A580

Quant	Memory needed	Available	Speed	Fit	Grade · points	Download
`Q2_K`	7.8 GB	8.0 GB	282 tok/s	Tight fit	70 ptsGood (B) · 70 of 100 points: Model quality 16.4/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15	GGUF (opens in a new tab)
`Q3_K_M`	9.3 GB	8.0 GB	123 tok/s	Runs with offload	59 ptsGood (B) · 59 of 100 points: Model quality 19.7/45, Memory fit 10.0/25, Speed 15.0/15, Memory use 15.0/15	GGUF files (opens in a new tab)
`Q4_K_M`	11.3 GB	8.0 GB	66.0 tok/s	Heavy offload	56 ptsUsable (C) · 56 of 100 points: Model quality 22.2/45, Memory fit 4.0/25, Speed 15.0/15, Memory use 15.0/15	GGUF (opens in a new tab)
`Q5_K_M`	12.9 GB	8.0 GB	46.3 tok/s	Heavy offload	56 ptsUsable (C) · 56 of 100 points: Model quality 22.7/45, Memory fit 4.0/25, Speed 15.0/15, Memory use 15.0/15	GGUF files (opens in a new tab)
`Q6_K`	14.7 GB	8.0 GB	34.6 tok/s	Heavy offload	56 ptsUsable (C) · 56 of 100 points: Model quality 23.2/45, Memory fit 4.0/25, Speed 14.0/15, Memory use 15.0/15	GGUF (opens in a new tab)
`Q8_0`	18.6 GB	8.0 GB	21.9 tok/s	Heavy offload	53 ptsUsable (C) · 53 of 100 points: Model quality 23.4/45, Memory fit 4.0/25, Speed 11.1/15, Memory use 15.0/15	GGUF (opens in a new tab)
`F16`	33.9 GB	8.0 GB	—	Won't run	7 ptsWon't run (F) · 7 of 100 points: Model quality 7.0/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15	GGUF (opens in a new tab) Original weights (opens in a new tab)

Memory figures assume 8K of context. Longer conversations or documents grow the KV cache.

Other hardware that runs it

78 ptsGreat (A) · 78 of 100 points: Model quality 23.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 AMD Radeon RX 7900 XTX Q8_0 · 184 tok/s
78 ptsGreat (A) · 78 of 100 points: Model quality 23.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 Apple M4 Max (64 GB) F16 · 48.4 tok/s
78 ptsGreat (A) · 78 of 100 points: Model quality 23.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 3090 Q8_0 · 209 tok/s
78 ptsGreat (A) · 78 of 100 points: Model quality 23.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 4090 Q8_0 · 225 tok/s
77 ptsGreat (A) · 77 of 100 points: Model quality 22.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 AMD Radeon RX 9070 XT Q5_K_M · 200 tok/s
77 ptsGreat (A) · 77 of 100 points: Model quality 22.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 4060 Ti 16GB Q5_K_M · 104 tok/s

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.