ASUS NUC 15 Pro

Hardware · Intel

What AI models can the Intel Core Ultra 200H (Arc 140T) (96 GB) run?

Unified memory
96.0 GB
Usable for models
54.7 GB
Memory bandwidth
102 GB/s
FP16 compute
9.2 TFLOPS
Released
2025
522 / 622 LLMs run Best: Kolibri 1 (78B-A3.5B) 61 / 61 Image models run Best: FLUX.2 [klein] 9B 42 / 43 Video models run Best: LTX-2.5 (22B, video + audio) 69 / 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 96.0 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 200H (Arc 140T)

Apps that run models well on the Intel Core Ultra 200H (Arc 140T) (96 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. vLLMThe full model fits in GPU memory at 8-bit, so it can batch many requests: best for serving a team or an app.
  3. LM StudioPoint-and-click: search, download and chat in one window.
  4. OllamaIntel GPU support here is experimental (Vulkan).

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

Best LLMs for the Intel Core Ultra 200H (Arc 140T) (96 GB)

Quality Model Quant Memory Speed Runs
86Excellent quality (A+) · 86 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 89/100 (quality, fit, speed and memory use) sets the ranking. Kolibri 1 (78B-A3.5B)Aleph Alpha Q4_K_M 52.8 GB 25.4 tok/s Runs welltight fit
85Excellent quality (A+) · 85 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 89/100 (quality, fit, speed and memory use) sets the ranking. Flint (40B-A2.5B)Recursive Q4_K_M 29.5 GB 34.0 tok/s Runs great
88Excellent quality (A+) · 88 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 89/100 (quality, fit, speed and memory use) sets the ranking. Qwen3.6 35B-A3BAlibaba Q4_K_M 27.1 GB 28.9 tok/s Runs well
88Excellent quality (A+) · 88 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 88/100 (quality, fit, speed and memory use) sets the ranking. Qwen3.5 35B-A3BAlibaba Q4_K_M 27.1 GB 28.9 tok/s Runs well
88Excellent quality (A+) · 88 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 88/100 (quality, fit, speed and memory use) sets the ranking. MiniCPM-V 4.7 35B-A3BOpenBMB Q4_K_M 26.6 GB 28.9 tok/s Runs well
81Great quality (A) · 81 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 88/100 (quality, fit, speed and memory use) sets the ranking. Qwen3 Coder Next (80B-A3B)Alibaba Q4_K_M 53.8 GB 28.9 tok/s Runs welltight fit
87Excellent quality (A+) · 87 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 88/100 (quality, fit, speed and memory use) sets the ranking. Nex N2 Mini (35B-A3B)Nex-AGI Q4_K_M 26.6 GB 28.9 tok/s Runs well
86Excellent quality (A+) · 86 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 88/100 (quality, fit, speed and memory use) sets the ranking. Sage-A1.5 35B-A3BInternScience Q4_K_M 27.1 GB 28.9 tok/s Runs well
86Excellent quality (A+) · 86 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 87/100 (quality, fit, speed and memory use) sets the ranking. KAT-Coder V2.5 Dev (35B-A3B)Kuaishou Q4_K_M 26.3 GB 28.9 tok/s Runs well
85Excellent quality (A+) · 85 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 87/100 (quality, fit, speed and memory use) sets the ranking. Ornith 1.5 35B-A3BDeepReinforce Q4_K_M 27.1 GB 28.9 tok/s Runs well
84Great quality (A) · 84 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 87/100 (quality, fit, speed and memory use) sets the ranking. Apodex 1.1 Mini (35B-A3B)Apodex Q4_K_M 27.1 GB 28.9 tok/s Runs well
84Great quality (A) · 84 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 87/100 (quality, fit, speed and memory use) sets the ranking. Ornith 1.0 35B-A3BDeepReinforce Q4_K_M 27.1 GB 28.9 tok/s Runs well

Best Image models for the Intel Core Ultra 200H (Arc 140T) (96 GB)

Quality Model Quant Memory Speed Runs
84Great quality (A) · 84 of 100: how capable the model is at FP16, whatever it runs on. Match score 89/100 (quality, fit, speed and memory use) sets the ranking. FLUX.2 [klein] 9BBlack Forest Labs FP16 37.1 GB 17 s Runs well
92Excellent quality (A+) · 92 of 100: how capable the model is at Q8 GGUF, whatever it runs on. Match score 83/100 (quality, fit, speed and memory use) sets the ranking. Qwen-Image 2.1Alibaba Q8 GGUF 41.6 GB 10.7 min Very slow
85Excellent quality (A+) · 85 of 100: how capable the model is at FP16, whatever it runs on. Match score 83/100 (quality, fit, speed and memory use) sets the ranking. ERNIE-Image TurboBaidu FP16 26.4 GB 30 s Runs well
78Great quality (A) · 78 of 100: how capable the model is at FP16, whatever it runs on. Match score 82/100 (quality, fit, speed and memory use) sets the ranking. FLUX.2 [klein] 4BBlack Forest Labs FP16 18.1 GB 9.3 s Runs great
70Great quality (A) · 70 of 100: how capable the model is at FP16, whatever it runs on. Match score 82/100 (quality, fit, speed and memory use) sets the ranking. FLUX.1 [schnell]Black Forest Labs FP16 36.4 GB 19 s Runs well
88Excellent quality (A+) · 88 of 100: how capable the model is at FP16, whatever it runs on. Match score 82/100 (quality, fit, speed and memory use) sets the ranking. ERNIE-ImageBaidu FP16 34.0 GB 3.4 min Very slow

Best Video models for the Intel Core Ultra 200H (Arc 140T) (96 GB)

Quality Model Quant Memory Speed Runs
86Excellent quality (A+) · 86 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. LTX-2.5 (22B, video + audio)Lightricks FP8 48.3 GB 12.7 min Slowtight fit
84Great quality (A) · 84 of 100: how capable the model is at FP8, whatever it runs on. Match score 81/100 (quality, fit, speed and memory use) sets the ranking. LTX-2.3Lightricks FP8 44.3 GB 56.0 min Very slow
85Excellent quality (A+) · 85 of 100: how capable the model is at FP16, whatever it runs on. Match score 80/100 (quality, fit, speed and memory use) sets the ranking. Bernini-R A14B (video generation + editing)ByteDance FP16 46.0 GB 1.9 h Very slow
85Excellent quality (A+) · 85 of 100: how capable the model is at Q8 GGUF, whatever it runs on. Match score 79/100 (quality, fit, speed and memory use) sets the ranking. Wan 2.2 I2V A14BAlibaba Q8 GGUF 39.3 GB 4.8 h Very slow
85Excellent quality (A+) · 85 of 100: how capable the model is at Q8 GGUF, whatever it runs on. Match score 79/100 (quality, fit, speed and memory use) sets the ranking. Wan 2.2 T2V A14BAlibaba Q8 GGUF 39.3 GB 4.8 h Very slow

Best Decision models for the Intel Core Ultra 200H (Arc 140T) (96 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 6.4 GB 91 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 6.4 GB 91 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 56 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 35 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 6.4 GB 91 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 39 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 6.0 GB 75 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 29 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.

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