Intel Arc G3 vs NVIDIA GeForce RTX 5070 Mobile 12 GB Comparison

Head-to-Head Benchmarks

The database contains no recorded head-to-head benchmark results between the Intel Arc G3 and the NVIDIA GeForce RTX 5070 Mobile 12 GB. With zero wins recorded for either side and no comparative scores available, direct performance comparisons must be derived from the architectural specifications and compute metrics recorded in the database.

The raw compute data shows a substantial gap in raw throughput. The RTX 5070 Mobile delivers 13.13 TFLOPS of FP32 performance, while the Arc G3 delivers 6.144 TFLOPS. This represents a 2.14x advantage for the NVIDIA part in single-precision floating-point work. The RTX 5070 Mobile also leads in texture rate at 205.2 GTexel/s versus 96.00 GTexel/s for the Arc G3, a 2.14x difference. Pixel rate favors the RTX 5070 Mobile as well, with 68.40 GPixel/s against 48.00 GPixel/s, a 1.43x advantage.

In FP16 compute, the comparison shifts. The RTX 5070 Mobile maintains 13.13 TFLOPS with a 1:1 ratio, meaning FP16 throughput equals FP32 throughput. The Arc G3 delivers 12.29 TFLOPS with a 2:1 ratio, meaning its FP16 performance is double its FP32 rate. The gap narrows considerably here: the RTX 5070 Mobile leads by only 6.8 percent in raw FP16 throughput. This suggests that workloads relying on half-precision math, such as certain machine learning inference tasks, would see a much smaller performance separation than FP32-dominated workloads.

The shading unit count reinforces the throughput disparity. The RTX 5070 Mobile packs 4608 shading units, while the Arc G3 has 1280. Texture mapping units number 144 on the NVIDIA side versus 40 on the Intel side. Render output units stand at 48 versus 20. These resource counts align with the measured rate differences.

Clock behavior presents an interesting contrast. The Arc G3 has a base clock of 300 MHz and a boost clock of 2400 MHz, an 8x boost range. The RTX 5070 Mobile operates at 907 MHz base and 1425 MHz boost, a narrower 1.57x range. The Intel part's aggressive boost curve indicates it relies heavily on transient power headroom to reach performance, while the NVIDIA part runs closer to a sustained clock envelope.

Where Each One Wins

The RTX 5070 Mobile dominates in raw compute throughput across nearly every measured metric. FP32 performance shows a 2.14x lead, texture rate shows a 2.14x lead, and pixel rate shows a 1.43x lead. The NVIDIA part also possesses dedicated tensor cores numbering 144, a feature entirely absent from the Arc G3's specification sheet. This gives the RTX 5070 Mobile a structural advantage in any workload that can leverage tensor core acceleration, including AI-assisted rendering and deep learning inference.

Memory configuration heavily favors the NVIDIA part. The RTX 5070 Mobile carries 12 GB of GDDR7 memory on a 192-bit bus, delivering 576.0 GB/s of bandwidth. The Arc G3 uses system-shared memory with bandwidth described as system dependent. In practical terms, the RTX 5070 Mobile's dedicated memory subsystem provides predictable, high-bandwidth access, while the Arc G3's performance hinges entirely on the host system's memory architecture. For texture-heavy scenes, large frame buffers, or data-intensive compute, the 576.0 GB/s figure represents a decisive advantage.

The Arc G3 counters in efficiency-oriented metrics. Its TDP is recorded at 25 W, exactly half of the RTX 5070 Mobile's 50 W. The Arc G3 also uses a 3 nm process node from Intel, while the RTX 5070 Mobile uses a 5 nm node from TSMC. The smaller process node allows the Intel part to achieve its performance targets at lower power draw. In FP32 throughput per watt, the Arc G3 delivers 6.144 TFLOPS divided by 25 W, which equals 0.246 TFLOPS per watt. The RTX 5070 Mobile delivers 13.13 TFLOPS divided by 50 W, which equals 0.263 TFLOPS per watt. The NVIDIA part maintains a slight efficiency edge in this specific ratio, but the difference is small.

The Arc G3's FP16 ratio deserves attention. With 12.29 TFLOPS at a 2:1 ratio, it achieves 96 percent of the RTX 5070 Mobile's FP16 throughput while drawing half the power. This makes the Intel part disproportionately competitive in FP16-heavy workloads when normalized for power consumption.

Architecture Differences

The two GPUs come from fundamentally different design philosophies. The Arc G3 is built on the Xe3-LPG architecture, part of Intel's Arc Graphics-M series for Panther Lake platforms. It uses a 3 nm process node fabricated by Intel's own foundry. The RTX 5070 Mobile uses the Blackwell 2.0 architecture with the GB206 chip, manufactured on a 5 nm process at TSMC. The RTX 5070 Mobile's transistor count is recorded at 21,900 million on a 181 mm² die, yielding a transistor density of 121.0M per mm². The Arc G3's transistor count and die size are listed as unknown.

The memory architectures could not be more different. The Arc G3 uses system-shared memory, meaning it draws from the host system's RAM pool. Its memory type, bus width, and bandwidth are all marked as system dependent. The RTX 5070 Mobile has a dedicated 12 GB GDDR7 frame buffer on a 192-bit interface with 576.0 GB/s of fixed bandwidth. This dedicated memory hierarchy eliminates contention with CPU workloads and provides consistent performance across different host platforms.

Compute resource allocation diverges sharply. The RTX 5070 Mobile fields 4608 shading units, 144 TMUs, 48 ROPs, 36 ray tracing cores, and 144 tensor cores. The Arc G3 fields 1280 shading units, 40 TMUs, 20 ROPs, and 10 ray tracing cores, with no tensor core count recorded. The NVIDIA part has 3.6x more shading units, 3.6x more TMUs, 2.4x more ROPs, and 3.6x more ray tracing cores.

The bus interface differs as well. The RTX 5070 Mobile uses PCIe 5.0 x16, while the Arc G3 is listed as IGP (integrated graphics processor) with a bus interface of IGP. This means the Arc G3 communicates over the internal fabric of the Panther Lake processor rather than a dedicated PCIe link. The RTX 5070 Mobile's PCIe 5.0 x16 connection provides substantial bandwidth for data transfer between the GPU and host system.

Both GPUs share identical API support: DirectX 12 Ultimate (12_2), OpenGL 4.6, and Vulkan 1.4. Both are marked as active production status with a release date of 2026-05-31. The RTX 5070 Mobile lists its predecessor as GeForce 40 Mobile, while the Arc G3 has no recorded predecessor.

Power delivery configurations also diverge. The Arc G3 runs at 25 W TDP with no power connectors, consistent with an integrated solution. The RTX 5070 Mobile runs at 50 W TDP, also with no power connectors, indicating it is designed for direct motherboard power delivery in mobile platforms. Neither GPU carries a suggested PSU rating, and both are listed with slot width of IGP.

FAQ

Q: How much faster is the RTX 5070 Mobile in FP32 compute?

A: The RTX 5070 Mobile delivers 13.13 TFLOPS of FP32 performance versus 6.144 TFLOPS for the Arc G3, a 2.14x advantage for the NVIDIA part.

Q: Does the Arc G3 have any performance metric where it approaches the RTX 5070 Mobile?

A: In FP16 throughput, the Arc G3 delivers 12.29 TFLOPS at a 2:1 ratio, while the RTX 5070 Mobile delivers 13.13 TFLOPS at a 1:1 ratio. The NVIDIA part leads by only 6.8 percent in this metric.

Q: What memory configuration does each GPU use?

A: The RTX 5070 Mobile uses 12 GB of GDDR7 memory on a 192-bit bus with 576.0 GB/s bandwidth. The Arc G3 uses system-shared memory with bandwidth and bus width described as system dependent.

Q: How do the TDP ratings compare?

A: The Arc G3 is rated at 25 W, while the RTX 5070 Mobile is rated at 50 W. The Intel part draws exactly half the power of the NVIDIA part.

Q: Which GPU has more ray tracing cores?

A: The RTX 5070 Mobile has 36 ray tracing cores, while the Arc G3 has 10. The NVIDIA part has 3.6x more ray tracing cores.

Q: Do both GPUs support the same graphics APIs?

A: Yes, both support DirectX 12 Ultimate (12_2), OpenGL 4.6, and Vulkan 1.4.

The Verdict

The recorded data positions the RTX 5070 Mobile as the substantially more capable GPU in absolute performance terms. Its 2.14x lead in FP32 throughput, 2.14x lead in texture rate, 1.43x lead in pixel rate, and 3.6x advantage in shading units make it the clear choice for compute-intensive and graphics-heavy workloads. The dedicated 12 GB GDDR7 memory with 576.0 GB/s bandwidth removes any dependency on host system memory performance, which the Arc G3 cannot avoid with its system-shared memory design.

The RTX 5070 Mobile also holds an architectural advantage in tensor core availability. With 144 tensor cores and no corresponding tensor core count recorded for the Arc G3, the NVIDIA part is the only option for workloads that require dedicated tensor hardware. The 144 tensor cores, combined with 36 ray tracing cores, give the RTX 5070 Mobile a feature set that the Arc G3 cannot match at the hardware level.

The Arc G3 finds its niche in power-constrained environments. Its 25 W TDP allows for deployment in systems where the 50 W RTX 5070 Mobile would exceed thermal or power budgets. The 3 nm process node from Intel enables this efficiency. The Arc G3's FP16 performance of 12.29 TFLOPS at 2:1 ratio is particularly notable: it reaches 93.6 percent of the RTX 5070 Mobile's FP16 throughput while consuming half the power. For systems that prioritize FP16 workloads and power efficiency over absolute throughput, the Arc G3 presents a viable alternative.

The percentile ranking for both GPUs sits at 50.0 against all GPUs in the database, indicating neither part is positioned at the extremes of the overall performance distribution. The Arc G3's 25 W power envelope and integrated design suggest it targets thin-and-light platforms where the RTX 5070 Mobile's 50 W TDP would be impractical. The RTX 5070 Mobile's 13.13 TFLOPS FP32 and 576.0 GB/s memory bandwidth indicate it targets performance-oriented mobile workstations and gaming laptops.

For users who need maximum compute throughput, dedicated memory bandwidth, and tensor core acceleration, the data clearly supports the RTX 5070 Mobile. For users operating under strict power limits who still require competitive FP16 performance, the Arc G3's efficiency profile makes it the appropriate selection. The choice rests on whether the workload prioritizes absolute performance or power-normalized throughput.