What AI models can the Intel Core Ultra 200H (Arc 140T) (64 GB) run?

Unified memory: 64.0 GB
Usable for models: 36.5 GB
Memory bandwidth: 102 GB/s
FP16 compute: 9.2 TFLOPS
Released: 2025

494 / 622 LLMs run Best: Qwen3.6 35B-A3B 61 / 61 Image models run Best: Z-Image Turbo 41 / 43 Video models run Best: Bernini-R A14B (video generation + editing) 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 64.0 GB.

More about this hardware

Maker	Intel
Type	Unified-memory system on a chip
GPU share of memory	57% (what the GPU can typically be given for a model)
Chip	Intel Core Ultra 200H (Arc 140T)

Apps that run models well on the Intel Core Ultra 200H (Arc 140T) (64 GB)

Windows

Linux

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) (64 GB)

Quality	Model	Quant	Memory	Speed	Runs
88Excellent quality (A+) · 88 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 92/100 (quality, fit, speed and memory use) sets the ranking.	Qwen3.6 35B-A3BAlibaba	`Q4_K_M`	25.6 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 92/100 (quality, fit, speed and memory use) sets the ranking.	MiniCPM-V 4.7 35B-A3BOpenBMB	`Q4_K_M`	25.2 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 92/100 (quality, fit, speed and memory use) sets the ranking.	Qwen3.5 35B-A3BAlibaba	`Q4_K_M`	25.6 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 92/100 (quality, fit, speed and memory use) sets the ranking.	Flint (40B-A2.5B)Recursive	`Q4_K_M`	28.0 GB	34.0 tok/s	Runs great
87Excellent quality (A+) · 87 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 92/100 (quality, fit, speed and memory use) sets the ranking.	Nex N2 Mini (35B-A3B)Nex-AGI	`Q4_K_M`	25.1 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 91/100 (quality, fit, speed and memory use) sets the ranking.	KAT-Coder V2.5 Dev (35B-A3B)Kuaishou	`Q4_K_M`	24.8 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 91/100 (quality, fit, speed and memory use) sets the ranking.	Sage-A1.5 35B-A3BInternScience	`Q4_K_M`	25.6 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 91/100 (quality, fit, speed and memory use) sets the ranking.	Ornith 1.5 35B-A3BDeepReinforce	`Q4_K_M`	25.6 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 90/100 (quality, fit, speed and memory use) sets the ranking.	Apodex 1.1 Mini (35B-A3B)Apodex	`Q4_K_M`	25.6 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 90/100 (quality, fit, speed and memory use) sets the ranking.	Ornith 1.0 35B-A3BDeepReinforce	`Q4_K_M`	25.6 GB	28.9 tok/s	Runs well
82Great quality (A) · 82 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.	Agents-A1 35B-A3BInternScience	`Q4_K_M`	25.1 GB	28.9 tok/s	Runs well
82Great quality (A) · 82 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.	Qwen AgentWorld 35B-A3BAlibaba	`Q4_K_M`	24.8 GB	28.9 tok/s	Runs well

Best Image models for the Intel Core Ultra 200H (Arc 140T) (64 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 86/100 (quality, fit, speed and memory use) sets the ranking.	Z-Image TurboAlibaba (Tongyi)	`FP16`	22.7 GB	23 s	Runs well
85Excellent quality (A+) · 85 of 100: how capable the model is at FP16, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking.	ERNIE-Image TurboBaidu	`FP16`	26.4 GB	30 s	Runs well
82Great quality (A) · 82 of 100: how capable the model is at FP8, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking.	FLUX.2 [klein] 9BBlack Forest Labs	`FP8`	19.9 GB	15 s	Runs well
91Excellent quality (A+) · 91 of 100: how capable the model is at FP16, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking.	FLUX 3 Image KleinBlack Forest Labs	`FP16`	22.4 GB	53 s	Slow
78Great quality (A) · 78 of 100: how capable the model is at FP16, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking.	FLUX.2 [klein] 4BBlack Forest Labs	`FP16`	18.1 GB	9.3 s	Runs great
90Excellent quality (A+) · 90 of 100: how capable the model is at FP8, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking.	Krea 2 TurboKrea	`FP8`	20.2 GB	36 s	Runs well

Best Video models for the Intel Core Ultra 200H (Arc 140T) (64 GB)

Quality	Model	Quant	Memory	Speed	Runs
83Great quality (A) · 83 of 100: how capable the model is at FP8, whatever it runs on. Match score 80/100 (quality, fit, speed and memory use) sets the ranking.	Bernini-R A14B (video generation + editing)ByteDance	`FP8`	26.0 GB	1.7 h	Very slow
66Good quality (B) · 66 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.	LTX-Video 13B 0.9.8 (distilled)Lightricks	`FP8`	30.1 GB	8.4 min	Runs well
86Excellent quality (A+) · 86 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.	LTX-2.5 (22B, video + audio)Lightricks	`FP8`	35.2 GB	13.4 min	Slowtight fit · encoder unloaded after use
78Great quality (A) · 78 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.	SCAIL-2 14B (character animation)Zhipu AI	`FP8`	29.8 GB	2.8 h	Very slow
74Great quality (A) · 74 of 100: how capable the model is at FP8, whatever it runs on. Match score 76/100 (quality, fit, speed and memory use) sets the ranking.	HY-OmniWeaving (8.3B, unified video)Tencent	`FP8`	24.6 GB	74.2 min	Very slow

Best Decision models for the Intel Core Ultra 200H (Arc 140T) (64 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`	5.0 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`	5.0 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`	5.0 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`	4.5 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.