Intel Arc Pro A60, AI & Machine Learning Benchmarks & Specs

12GB · AI Score 1.8/100 · anchored estimate vs 51 measured cards

1.8 AI Score Includes estimates

We have not run Intel Arc Pro A60 on our bench. These figures are anchored estimates, interpolated per workload against the 51 GPUs we did measure (confidence: low). On Llama 3.1 8B (Q4_K_M) Intel Arc Pro A60 should deliver about 33.6 tokens/sec. Llama 3.3 70B does not fit. It needs roughly 42GB and this card has 12GB. 2 of the 12 workloads won't fit on 12GB at the tested precision, Qwen3 32B, Llama 3.3 70B. We publish those as hard gates rather than quietly dropping to a smaller quant. Image, video and 3D models aren't listed for this card. They're built for NVIDIA's CUDA, and on Intel Arc they only run through PyTorch XPU workarounds whose speed depends more on the driver and OS than on the card, so we don't estimate them, and they count as not supported in the AI score. For text generation (llama.cpp runs natively on SYCL and Vulkan), the numbers above hold up.

AI & Machine Learning benchmark results

Text Generation tok/s 8

Qwen3 4B54.8

Llama 3.1 8B33.6

Qwen2.5-Coder 14B18.5

Workload	Result	Telemetry	Data
Qwen3 4B	54.8 tok/s	estimated	Est.
Llama 3.1 8B	33.6 tok/s	estimated	Est.
Qwen2.5-Coder 14B	18.5 tok/s	estimated	Est.
Gemma 4 26B A4B	✕ Won't fit needs ~20 GB	VRAM-gated at this precision	Est.
Qwen3 30B A3B	✕ Won't fit needs ~20 GB	VRAM-gated at this precision	Est.
Gemma 4 31B	✕ Won't fit needs ~22 GB	VRAM-gated at this precision	Est.
Qwen3 32B	✕ Won't fit	VRAM-gated at this precision	Est.
Llama 3.3 70B	✕ Won't fit	VRAM-gated at this precision	Est.

How this estimate is derived. This card hasn’t been through our bench yet, so its numbers are anchored estimates, interpolated from the 51 first-party measured cards (Bandwidth Theil-Sen ladder off NVIDIA anchors × vendor factor (LLM ×0.5, diffusion ×0.35); measured VRAM floors; recalibrated 2026-09-19 against published llama.cpp (CUDA/ROCm/Vulkan/SYCL) and Stable Diffusion results.). The VRAM “won’t fit” gates are exact, since they’re pure capacity limits. Confidence: low. Estimates are replaced with measured data as more silicon goes through the bench. Full methodology →

Intel Arc Pro A60 specifications

Architecture	Xe HPG (Alchemist)
Xe cores	16
VRAM	12GB GDDR6
Memory bus	192-bit
Memory bandwidth	384 GB/s
Boost clock	2,050 MHz
TDP	130 W
Process	6nm
Interface	PCIe 4.0 x16
Release date	2023-06-01

Verdict, capable, but 12GB sets the ceiling

Intel Arc Pro A60 scores 1.8/100, #92 of 102. It ran 3 of 12; 2 exceeded its 12GB. Figures are anchored estimates, not measurements, we flag that on every row.

Relative performance: where the Intel Arc Pro A60 lands

100% = this card, AI & Machine Learning headline metric (AI Score). #19 of 20 workstation cards in this vertical.

GPU	%	AI Score
NVIDIA Quadro RTX 5000	194%	3.5
NVIDIA RTX 2000 Ada Generation	172%	3.1
AMD Radeon Pro W6800	167%	3
AMD Radeon Pro W7800	167%	3
Intel Arc Pro A60	100%	1.8
NVIDIA RTX A2000	72%	1.3

One tier up: AMD Radeon Pro W7800 (+67%)
One tier down: NVIDIA RTX A2000 (-28%)

← All AI & Machine Learning GPU rankings

The silicon

Transistors	7,200 million
Die size	157 mm²
Process node	6 nm
Fabricated by	TSMC
Transistor density	45.9 million per mm²

Denser than 83% of the 746 cards we have silicon data for. Density is the clearest measure of what a process node bought: a card that gained it without growing the die got its speed from the fab rather than the architecture.

Silicon figures from Wikipedia (CC BY-SA 4.0). Benchmarks on this page are our own. Compare every chip.

What this card can build

Whole-job timings, composed from our measured per-model results on this card.

Workflow	Time	Energy	Basis
Full codebase review	54.1 min	n/a	estimate, 0 of 1 stage measured

Can't run: Short social clips (needs Qwen3 32B), 60-second AI short film (needs Qwen3 32B), 24-frame storyboard (needs Qwen3 32B), 6-panel comic page (needs Qwen3 32B), Long-form article batch (needs Llama 3.3 70B).