Intel Arc A380E x2 vs NVIDIA RTX 1000 Mobile Ada Generation Comparison

The Verdict

The recorded data presents two fundamentally different mobile and embedded GPU solutions. The Intel Arc A380E x2 is a dual-GPU configuration based on the DG2-128 chip, built on TSMC's 6 nm process, and positioned as an end-of-life product from the Alchemist generation. The NVIDIA RTX 1000 Mobile Ada Generation is a single AD107 chip on TSMC's 5 nm process, currently active in production, and belongs to the Ada Lovelace architecture family. Benchmark results show both products sit at the 50th percentile among all GPUs in the database, with no head-to-head benchmark entries recorded, meaning the comparison rests entirely on architectural specifications and compute capabilities.

The Intel Arc A380E x2 configuration delivers a combined 4.096 TFLOPS of FP32 performance across its two GPUs, while the NVIDIA RTX 1000 Mobile delivers 10.37 TFLOPS from a single chip. The NVIDIA part also leads in pixel throughput at 97.20 GPixel/s versus 64.00 GPixel/s for the Intel dual setup, and in texture rate at 162.0 GTexel/s versus 128.0 GTexel/s. The Intel solution carries a 130 W TDP with a single-slot form factor and one 6-pin power connector, whereas the NVIDIA mobile part operates at 35 W TDP with no power connectors and an integrated form factor. The data indicates the NVIDIA solution wins on raw performance per watt and absolute throughput, while the Intel dual-GPU approach offers a unique multi-output display configuration with eight mini-DisplayPort 2.0 outputs.

Architecture Differences

The Intel Arc A380E x2 uses the DG2-128 chip, fabricated on a 6 nm process at TSMC, with 7,200 million transistors across a 157 mm² die, yielding a transistor density of 45.9M per mm². The architecture is Xe-HPG, specifically from the Alchemist generation. Each GPU in the dual configuration contains 1,024 shading units, 64 texture mapping units, 32 raster output units, and 8 ray tracing cores. The memory subsystem consists of 6 GB of GDDR6 per GPU, with a 96-bit bus width and 186.0 GB/s bandwidth per GPU. Clock speeds are fixed at 2000 MHz for both base and boost, with memory running at 1937 MHz or 15.5 Gbps effective. The dual configuration doubles these resources, providing 2,048 total shading units, 128 TMUs, 64 ROPs, and 16 ray tracing cores across the two GPUs.

The NVIDIA RTX 1000 Mobile Ada Generation uses the AD107 chip, fabricated on TSMC's 5 nm process, with 18,900 million transistors on a 159 mm² die, resulting in a much higher transistor density of 118.9M per mm². The Ada Lovelace architecture brings 2,560 shading units, 80 TMUs, 48 ROPs, 20 ray tracing cores, and 80 tensor cores on a single chip. Memory is 6 GB of GDDR6 with a 96-bit bus and 192.0 GB/s bandwidth. The base clock is 1485 MHz with a boost clock of 2025 MHz, and memory operates at 2000 MHz or 16 Gbps effective.

The NVIDIA chip achieves more than double the transistor density of the Intel chip, reflecting the more advanced 5 nm process node. FP16 compute differs significantly: the Intel part reaches 8.192 TFLOPS with a 2:1 ratio relative to FP32, while the NVIDIA part offers 10.37 TFLOPS at a 1:1 ratio. The NVIDIA tensor cores (80 total) provide dedicated AI acceleration hardware that the Intel part lacks entirely. Both support DirectX 12 Ultimate (12_2), OpenGL 4.6, and Vulkan 1.4, so API compatibility is identical.

Head-to-Head Benchmarks

The database contains no recorded head-to-head benchmark entries between the Intel Arc A380E x2 and the NVIDIA RTX 1000 Mobile Ada Generation. With winsA and winsB both at zero, the comparison must be derived from the specification sheet. The FP32 compute gap is substantial: the NVIDIA single chip delivers 10.37 TFLOPS, which is approximately 2.5 times the 4.096 TFLOPS of one Intel GPU. Even when combining both Intel GPUs, the dual configuration reaches only 8.192 TFLOPS combined FP32, still below the NVIDIA single-chip result of 10.37 TFLOPS.

Pixel fill rate favors NVIDIA at 97.20 GPixel/s versus 64.00 GPixel/s for the Intel dual setup, a 33.20 GPixel/s difference. Texture fill rate also favors NVIDIA at 162.0 GTexel/s versus 128.0 GTexel/s for the Intel dual configuration, a 34.0 GTexel/s difference. The NVIDIA part provides 80 texture units versus 64 on each Intel GPU, and 48 ROPs versus 32 on each Intel GPU, meaning the NVIDIA single chip out-specifies even the combined Intel dual-GPU arrangement in both categories.

Memory bandwidth is closer: the NVIDIA part offers 192.0 GB/s versus 186.0 GB/s per Intel GPU. In a dual configuration, Intel can access 372.0 GB/s total across both GPUs, but this bandwidth is split between separate memory pools, which introduces cross-GPU communication overhead not present in the unified NVIDIA memory space. The NVIDIA memory clock runs at 2000 MHz (16 Gbps effective) versus 1937 MHz (15.5 Gbps effective) for Intel.

The Intel dual configuration does lead in display outputs with eight mini-DisplayPort 2.0 connections, while the NVIDIA mobile part relies on portable device dependent outputs. This makes the Intel solution more suitable for multi-display industrial or embedded applications.

FAQ

Q: Which GPU has higher FP32 compute performance?

A: The NVIDIA RTX 1000 Mobile Ada Generation delivers 10.37 TFLOPS of FP32 performance from a single chip. The Intel Arc A380E x2 provides 4.096 TFLOPS per GPU, reaching 8.192 TFLOPS combined across both GPUs. The NVIDIA single chip outperforms the entire Intel dual-GPU configuration by 2.178 TFLOPS.

Q: How do the power requirements compare?

A: The Intel Arc A380E x2 has a 130 W TDP and requires a 300 W suggested power supply, using a single 6-pin power connector in a single-slot form factor. The NVIDIA RTX 1000 Mobile Ada Generation has a 35 W TDP, uses no power connectors, and comes in an integrated form factor. The NVIDIA part consumes 95 W less power.

Q: What memory configurations do these GPUs use?

A: Both GPUs use 6 GB of GDDR6 memory with a 96-bit bus width. The Intel Arc A380E x2 runs memory at 1937 MHz (15.5 Gbps effective) providing 186.0 GB/s bandwidth per GPU, while the NVIDIA RTX 1000 Mobile runs at 2000 MHz (16 Gbps effective) providing 192.0 GB/s. The Intel dual configuration totals 12 GB across both GPUs.

Q: Do both GPUs support ray tracing?

A: Yes, both support ray tracing. The Intel Arc A380E x2 includes 8 ray tracing cores per GPU, totaling 16 across the dual configuration. The NVIDIA RTX 1000 Mobile Ada Generation includes 20 ray tracing cores on its single chip.

Q: What is the production status of each product?

A: The Intel Arc A380E x2 is marked as end-of-life, with a release date of 2024-03-31. The NVIDIA RTX 1000 Mobile Ada Generation is active in production, with a release date of 2024-02-25. The Intel part lists Battlemage as its successor, while the NVIDIA part lists Blackwell-MW as its successor.

Q: How do the transistor counts and die sizes compare?

A: The Intel DG2-128 chip contains 7,200 million transistors on a 157 mm² die, using a 6 nm process with a density of 45.9M transistors per mm². The NVIDIA AD107 chip contains 18,900 million transistors on a 159 mm² die, using a 5 nm process with a density of 118.9M transistors per mm².

Where Each One Wins

The NVIDIA RTX 1000 Mobile Ada Generation wins decisively in raw compute throughput. Its FP32 figure of 10.37 TFLOPS exceeds the combined 8.192 TFLOPS of the Intel dual configuration. The NVIDIA part also leads in pixel rate at 97.20 GPixel/s versus 64.00 GPixel/s, texture rate at 162.0 GTexel/s versus 128.0 GTexel/s, and memory bandwidth at 192.0 GB/s versus 186.0 GB/s per Intel GPU. The 80 tensor cores provide dedicated AI acceleration that the Intel part cannot match, and the 20 ray tracing cores outnumber the 16 available across both Intel GPUs. The 5 nm process node with 118.9M transistors per mm² density indicates a more modern and efficient design. The 35 W TDP makes the NVIDIA solution suitable for power-constrained mobile applications, and the 1:1 FP16 ratio at 10.37 TFLOPS offers consistent precision performance across workloads.

The Intel Arc A380E x2 wins in specific deployment scenarios that favor its dual-GPU architecture. The eight mini-DisplayPort 2.0 outputs enable multi-display configurations that the NVIDIA mobile part cannot match with its portable device dependent outputs. The dual configuration provides 12 GB total memory across two separate 6 GB pools, which can be advantageous for workloads that partition data across GPUs. The 130 W TDP with a 300 W suggested power supply indicates the Intel solution targets systems with dedicated power delivery, and the single-slot form factor with a 265 mm length (10.4 inches) and 127 mm height (5 inches) fits standard expansion slots. The fixed 2000 MHz base and boost clocks simplify performance prediction, and the 2:1 FP16 ratio at 8.192 TFLOPS per GPU offers substantial half-precision throughput for compatible workloads.

The data shows no benchmark wins for either product, so the performance picture is purely speculative from specifications. The NVIDIA part appears better suited for general compute, AI inference, and ray-traced workloads with its higher throughput and dedicated tensor cores. The Intel dual configuration appears better suited for multi-display embedded systems, digital signage, or industrial visualization where the eight display outputs and dual memory pools provide structural advantages. The end-of-life status of the Intel part and the active production status of the NVIDIA part indicate different support lifecycles, with the Intel product likely facing diminishing availability while the NVIDIA product continues to ship.

The transistor density difference, 118.9M per mm² for NVIDIA versus 45.9M per mm² for Intel, suggests the NVIDIA design achieves higher performance with less silicon area per transistor, consistent with its 2.6 times higher transistor count on nearly the same die size. The NVIDIA part also offers higher boost clocks at 2025 MHz versus 2000 MHz for Intel, combined with more than double the shading units at 2,560 versus 1,024 per Intel GPU. These architectural advantages translate directly to the measured specification gaps in fill rates and compute throughput.