ASUS NUC 14 Pro AI

Hardware · Intel

What AI models can the Intel Core Ultra 200V (Arc 140V) (16 GB) run?

Unified memory
16.0 GB
Usable for models
9.1 GB
Memory bandwidth
137 GB/s
FP16 compute
8.0 TFLOPS
Released
2024
348 / 622 LLMs run Best: Mellum2.1 Thinking (12B-A2.5B) 52 / 61 Image models run Best: FLUX.2 [klein] 4B 9 / 43 Video models run Best: LTX-Video 2B 62 / 70 Decision models run Best: Jina Reranker v3.5

macOS lets the GPU use roughly two-thirds to three-quarters of unified memory by default, so we plan around that figure rather than the full 16.0 GB.

More about this hardware

MakerIntel
TypeUnified-memory system on a chip
GPU share of memory57% (what the GPU can typically be given for a model)
ChipIntel Core Ultra 200V (Arc 140V)

Apps that run models well on the Intel Core Ultra 200V (Arc 140V) (16 GB)

Windows

  1. llama.cppSupports Intel Arc through SYCL and Vulkan.
  2. LM StudioPoint-and-click: search, download and chat in one window.
  3. OllamaIntel GPU support here is experimental (Vulkan).
  4. JanPoint-and-click: search, download and chat in one window.

Linux

  1. llama.cppSupports Intel Arc through SYCL and Vulkan.
  2. LM StudioPoint-and-click: search, download and chat in one window.
  3. OllamaIntel GPU support here is experimental (Vulkan).
  4. JanPoint-and-click: search, download and chat in one window.

For a typical model that fits in memory. Each model page picks the best app for that model on this hardware. All apps

1 product with the Intel Core Ultra 200V (Arc 140V) (16 GB)

Best LLMs for the Intel Core Ultra 200V (Arc 140V) (16 GB)

Quality Model Quant Memory Speed Runs
66Good quality (B) · 66 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 83/100 (quality, fit, speed and memory use) sets the ranking. Mellum2.1 Thinking (12B-A2.5B)JetBrains Q4_K_M 8.9 GB 43.7 tok/s Runs welltight fit
66Good quality (B) · 66 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 82/100 (quality, fit, speed and memory use) sets the ranking. Nemotron 3.5 Bolt E4BNVIDIA Q4_K_M 6.3 GB 28.4 tok/s Runs well
76Great quality (A) · 76 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 82/100 (quality, fit, speed and memory use) sets the ranking. K2 Horizon 7BInstitute of Foundation Models Q4_K_M 8.0 GB 14.2 tok/s Runs welltight fit
74Great quality (A) · 74 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 82/100 (quality, fit, speed and memory use) sets the ranking. MiMo V2.6 Distill Qwen 9BXiaomi Q4_K_M 7.3 GB 13.6 tok/s Runs well
74Great quality (A) · 74 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 81/100 (quality, fit, speed and memory use) sets the ranking. SenseNova Loop 9B PreviewSenseTime Q4_K_M 7.7 GB 13.3 tok/s Runs well
64Good quality (B) · 64 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 81/100 (quality, fit, speed and memory use) sets the ranking. Gemma 4 E4BGoogle Q4_K_M 6.3 GB 28.4 tok/s Runs well
73Great quality (A) · 73 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 81/100 (quality, fit, speed and memory use) sets the ranking. Ornith 1.5 9BDeepReinforce Q4_K_M 7.5 GB 13.3 tok/s Runs well
61Good quality (B) · 61 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 81/100 (quality, fit, speed and memory use) sets the ranking. Gemma 4 E2BGoogle Q6_K 5.6 GB 38.6 tok/s Runs great
58Good quality (B) · 58 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 81/100 (quality, fit, speed and memory use) sets the ranking. Ling 3.0 Tiny (8B-A1.3B)Ant Group (inclusionAI) Q5_K_M 7.2 GB 63.3 tok/s Runs great
72Great quality (A) · 72 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 81/100 (quality, fit, speed and memory use) sets the ranking. Qwen3.5 9BAlibaba Q4_K_M 7.5 GB 13.3 tok/s Runs well
64Good quality (B) · 64 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 80/100 (quality, fit, speed and memory use) sets the ranking. Nanbeige4.2 3BBOSS Zhipin (Nanbeige) Q4_K_M 5.3 GB 30.7 tok/s Runs great
71Great quality (A) · 71 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 80/100 (quality, fit, speed and memory use) sets the ranking. Ornith 1.0 9BDeepReinforce Q4_K_M 7.5 GB 13.3 tok/s Runs well

Best Image models for the Intel Core Ultra 200V (Arc 140V) (16 GB)

Quality Model Quant Memory Speed Runs
76Great quality (A) · 76 of 100: how capable the model is at FP8, whatever it runs on. Match score 84/100 (quality, fit, speed and memory use) sets the ranking. FLUX.2 [klein] 4BBlack Forest Labs FP8 6.2 GB 9.8 s Runs greatencoder unloaded after use
82Great quality (A) · 82 of 100: how capable the model is at FP8, whatever it runs on. Match score 79/100 (quality, fit, speed and memory use) sets the ranking. Z-Image TurboAlibaba (Tongyi) FP8 8.5 GB 24 s Runs welltight fit · encoder unloaded after use
89Excellent quality (A+) · 89 of 100: how capable the model is at FP8, whatever it runs on. Match score 79/100 (quality, fit, speed and memory use) sets the ranking. FLUX 3 Image KleinBlack Forest Labs FP8 7.9 GB 56 s Slowtight fit · encoder unloaded after use
56Good quality (B) · 56 of 100: how capable the model is at FP8, whatever it runs on. Match score 78/100 (quality, fit, speed and memory use) sets the ranking. DreamShaper XL v2 TurboLykon FP8 5.6 GB 12 s Runs great
78Great quality (A) · 78 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 77/100 (quality, fit, speed and memory use) sets the ranking. FLUX.2 [klein] 9BBlack Forest Labs Q4 GGUF 8.0 GB 24 s Runs welltight fit · encoder unloaded after use
52Fair quality (C) · 52 of 100: how capable the model is at FP8, whatever it runs on. Match score 77/100 (quality, fit, speed and memory use) sets the ranking. SDXL-Lightning (4-step)ByteDance FP8 5.6 GB 6.5 s Runs great

Best Video models for the Intel Core Ultra 200V (Arc 140V) (16 GB)

Quality Model Quant Memory Speed Runs
49Fair quality (C) · 49 of 100: how capable the model is at FP8, whatever it runs on. Match score 69/100 (quality, fit, speed and memory use) sets the ranking. LTX-Video 2BLightricks FP8 6.1 GB 4.1 min Runs wellencoder unloaded after use
35Basic quality (D) · 35 of 100: how capable the model is at FP16, whatever it runs on. Match score 66/100 (quality, fit, speed and memory use) sets the ranking. AnimateDiff v3 (SD 1.5)Shanghai AI Lab FP16 4.7 GB 3.5 min Runs great
53Fair quality (C) · 53 of 100: how capable the model is at FP8, whatever it runs on. Match score 63/100 (quality, fit, speed and memory use) sets the ranking. Wan 2.1 T2V 1.3BAlibaba FP8 7.1 GB 39.2 min Slowencoder unloaded after use
44Fair quality (C) · 44 of 100: how capable the model is at FP8, whatever it runs on. Match score 63/100 (quality, fit, speed and memory use) sets the ranking. CogVideoX 2BZhipu AI FP8 6.2 GB 9.8 min Runs wellencoder unloaded after use
53Fair quality (C) · 53 of 100: how capable the model is at FP8, whatever it runs on. Match score 61/100 (quality, fit, speed and memory use) sets the ranking. Wan 2.1 VACE 1.3BAlibaba FP8 8.0 GB 42.5 min Very slowtight fit · encoder unloaded after use

Best Decision models for the Intel Core Ultra 200V (Arc 140V) (16 GB)

Quality Model Quant Memory Speed Runs
86Excellent quality (A+) · 86 of 100: how capable the model is at F16, whatever it runs on. Match score 93/100 (quality, fit, speed and memory use) sets the ranking. Jina Reranker v3.5Jina AI F16 2.8 GB 94 ms Runs great
84Great quality (A) · 84 of 100: how capable the model is at F16, whatever it runs on. Match score 92/100 (quality, fit, speed and memory use) sets the ranking. Jina Reranker v3Jina AI F16 2.8 GB 94 ms Runs great
80Great quality (A) · 80 of 100: how capable the model is at F16, whatever it runs on. Match score 91/100 (quality, fit, speed and memory use) sets the ranking. FRIDA-DecisionsSber AI F16 2.4 GB 64 ms Runs great
80Great quality (A) · 80 of 100: how capable the model is at F16, whatever it runs on. Match score 91/100 (quality, fit, speed and memory use) sets the ranking. GLiNER2.5 DecideFastino F16 1.7 GB 40 ms Runs great
80Great quality (A) · 80 of 100: how capable the model is at F16, whatever it runs on. Match score 91/100 (quality, fit, speed and memory use) sets the ranking. Qwen3 Reranker 0.6BAlibaba F16 2.8 GB 94 ms Runs great
76Great quality (A) · 76 of 100: how capable the model is at F16, whatever it runs on. Match score 89/100 (quality, fit, speed and memory use) sets the ranking. Llama Prompt Guard 2 86MMeta F16 1.2 GB 45 ms Runs great
76Great quality (A) · 76 of 100: how capable the model is at F16, whatever it runs on. Match score 89/100 (quality, fit, speed and memory use) sets the ranking. mxbai-rerank-base v2Mixedbread F16 2.3 GB 77 ms Runs great
74Great quality (A) · 74 of 100: how capable the model is at F16, whatever it runs on. Match score 88/100 (quality, fit, speed and memory use) sets the ranking. GLiClass ModernBERT Large v3Knowledgator F16 1.5 GB 33 ms Runs great

Best LLMs by task on this hardware Open in Model Finder

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.

Was this page helpful?