AMD Ryzen Z2 GPU vs NVIDIA B300 Comparison

FAQ

Q: What are the core specifications of the AMD Ryzen Z2 GPU?

A: The AMD Ryzen Z2 GPU is built on the Hawk Point chip using RDNA 3.0 architecture on a 4 nm process from TSMC. It contains 25,390 million transistors on a 178 mm² die, with 768 shading units, 48 TMUs, 32 ROPs, and 12 RT cores. Its memory configuration is 16 GB of LPDDR5X on a 128-bit bus, delivering 119.9 GB/s bandwidth.

Q: What are the core specifications of the NVIDIA B300?

A: The NVIDIA B300 uses the GB110 chip with Blackwell Ultra architecture on a 5 nm process from TSMC. It contains 104,000 million transistors, with 18,944 shading units, 592 TMUs, 24 ROPs, and 592 tensor cores. Memory is 144 GB of HBM3e on a 4096-bit bus, providing 4.10 TB/s bandwidth.

Q: How do the clock speeds compare between these two GPUs?

A: The AMD Ryzen Z2 GPU has a base clock of 800 MHz and a boost clock of 2700 MHz, with memory clocked at 937 MHz (7.5 Gbps effective). The NVIDIA B300 has a base clock of 1665 MHz and a boost clock of 2032 MHz, with memory at 2000 MHz (8 Gbps effective).

Q: What are the power requirements for each GPU?

A: The AMD Ryzen Z2 GPU has a thermal design power (TDP) of 28 W and uses no power connectors. The NVIDIA B300 has a TDP of 1400 W and requires a suggested power supply of 1800 W. The B300 is an SXM Module, while the Z2 GPU has no slot width specified.

Q: What is the FP32 compute performance for each?

A: The AMD Ryzen Z2 GPU delivers 8.294 TFLOPS of FP32 performance and the same 8.294 TFLOPS for FP16 (1:1 ratio). The NVIDIA B300 delivers 76.99 TFLOPS of FP32 and 1,231.8 TFLOPS of FP16 (16:1 ratio), showing a substantially higher compute ceiling.

Q: What are the display and connectivity options?

A: The AMD Ryzen Z2 GPU provides a single USB Type-C display output and supports DirectX 12 Ultimate (12_2), OpenGL 4.6, and Vulkan 1.4. The NVIDIA B300 has no display outputs and lists no API compatibility data in the database; it uses a PCIe 5.0 x16 bus interface.

The Verdict

The data separates these two products into entirely different classes. The AMD Ryzen Z2 GPU, with its 28 W TDP and 8.294 TFLOPS FP32 output, is a low-power integrated-class solution. The NVIDIA B300, with a 1400 W TDP and 76.99 TFLOPS FP32, is a server accelerator. The percentile scores for both are 50 against all GPUs, but the raw specifications indicate divergent use cases.

For clients needing a compact, low-power GPU with display output, the AMD Ryzen Z2 GPU is the only option between the two, as it provides a USB Type-C connection and standard graphics APIs. For compute-heavy server workloads, the NVIDIA B300 is the clear choice, given its 144 GB memory capacity, 4.10 TB/s bandwidth, and tensor core count of 592. The B300 also uses the PCIe 5.0 x16 interface, which aligns with server infrastructure.

The B300's FP16 performance of 1,231.8 TFLOPS is 148 times higher than the Z2 GPU's 8.294 TFLOPS FP16, which indicates its dominance in machine learning and AI inference tasks. Conversely, the Z2 GPU has no tensor cores listed, confirming it lacks dedicated AI acceleration hardware.

Head-to-Head Benchmarks

The database records no direct head-to-head benchmark scores for these two products, and both have zero wins in the winsA and winsB fields. The average benchmark score for each is 0. However, the specification data provides clear comparisons for performance estimation.

In FP32 compute, the NVIDIA B300 delivers 76.99 TFLOPS versus 8.294 TFLOPS for the AMD Ryzen Z2 GPU. This means the B300 offers roughly 9.3 times the raw single-precision throughput. The difference is even more pronounced in FP16: the B300's 1,231.8 TFLOPS dwarfs the Z2 GPU's 8.294 TFLOPS, a 148-fold gap.

The texture rate follows a similar pattern. The B300 achieves 1,202.9 GTexel/s, while the Z2 GPU manages 129.6 GTexel/s. The B300 is about 9.3 times faster in texture fill, consistent with its higher TMU count of 592 versus 48.

Pixel rate tells a different story. The AMD Ryzen Z2 GPU has a pixel rate of 86.40 GPixel/s, which is higher than the NVIDIA B300's 48.77 GPixel/s. This occurs because the Z2 GPU has 32 ROPs compared to the B300's 24 ROPs, and the Z2 GPU's boost clock of 2700 MHz exceeds the B300's 2032 MHz boost clock. For rasterization-heavy tasks, the Z2 GPU holds a 77% advantage in pixel throughput.

Memory bandwidth heavily favors the B300. With 4.10 TB/s from HBM3e on a 4096-bit bus, the B300 provides roughly 34 times the bandwidth of the Z2 GPU's 119.9 GB/s. The memory capacity difference is also substantial: 144 GB versus 16 GB, a 9-fold increase.

The transistor counts show the scale difference. The B300 packs 104,000 million transistors versus 25,390 million for the Z2 GPU, about 4.1 times more. However, the Z2 GPU achieves a transistor density of 142.6M per mm² on its 178 mm² die, while the B300's die size and density are not recorded in the database.

Specification Differences

The AMD Ryzen Z2 GPU and NVIDIA B300 differ across nearly every major specification category. The process nodes differ, with the Z2 GPU using 4 nm and the B300 using 5 nm, both from TSMC. The Z2 GPU has a die size of 178 mm², while the B300's die size is not recorded.

Memory specifications show significant divergence. The Z2 GPU uses 16 GB of LPDDR5X with a 128-bit bus and 119.9 GB/s bandwidth. The B300 uses 144 GB of HBM3e with a 4096-bit bus and 4.10 TB/s bandwidth. The memory clock also differs: 937 MHz (7.5 Gbps effective) for the Z2 GPU versus 2000 MHz (8 Gbps effective) for the B300.

Compute unit counts vary widely. The Z2 GPU has 768 shading units, 48 TMUs, and 32 ROPs. The B300 has 18,944 shading units, 592 TMUs, and 24 ROPs. The Z2 GPU includes 12 RT cores, while the B300's RT core count is not listed. The B300 includes 592 tensor cores, while the Z2 GPU has none listed.

Power and physical specifications are starkly different. The Z2 GPU has a 28 W TDP and no power connectors. The B300 has a 1400 W TDP, uses an SXM Module slot width, and requires a suggested power supply of 1800 W. The bus interfaces also differ, with the B300 using PCIe 5.0 x16 and the Z2 GPU having no bus interface recorded.

Display outputs separate them completely. The Z2 GPU has one USB Type-C output, while the B300 has no outputs. The Z2 GPU supports DirectX 12 Ultimate (12_2), OpenGL 4.6, and Vulkan 1.4. The B300 lists no API support in the database.

Architecture Differences

The AMD Ryzen Z2 GPU uses the Hawk Point chip with RDNA 3.0 architecture, classified as a Console GPU (AMD) generation. It is fabricated on a 4 nm process by TSMC. The transistor density is recorded at 142.6M per mm², derived from 25,390 million transistors across a 178 mm² die.

The NVIDIA B300 uses the GB110 chip with Blackwell Ultra architecture, classified as a Server Blackwell (Bxx) generation. It is fabricated on a 5 nm process by TSMC. The transistor count is 104,000 million, but the die size and density are not recorded in the database.

The Z2 GPU's RDNA 3.0 architecture includes 12 RT cores for ray tracing and supports FP16 at a 1:1 ratio with FP32. Its FP16 output equals its FP32 output at 8.294 TFLOPS. This suggests a unified shader approach without separate tensor hardware.

The B300's Blackwell Ultra architecture includes 592 tensor cores, which support FP16 at a 16:1 ratio. This means FP16 throughput of 1,231.8 TFLOPS is significantly higher than its FP32 throughput of 76.99 TFLOPS, indicating a design optimized for mixed-precision AI workloads.

The release dates differ, with the Z2 GPU released on 2024-12-31 and the B300 on 2025-09-10. The B300 has a predecessor listed as Server Hopper and a successor as Server Rubin, while the Z2 GPU has no predecessor or successor recorded. The B300's production status is Active, matching the Z2 GPU's Active status.

Where Each One Wins

The AMD Ryzen Z2 GPU wins in pixel rate, delivering 86.40 GPixel/s compared to the B300's 48.77 GPixel/s. This advantage comes from its higher ROP count of 32 versus 24 and its boost clock of 2700 MHz versus 2032 MHz. For workloads that emphasize rasterization and pixel fill, the Z2 GPU has the edge, despite its lower overall compute.

The Z2 GPU also wins on power efficiency. With a 28 W TDP, it consumes 50 times less power than the B300's 1400 W TDP. This makes it suitable for environments where heat dissipation and power delivery are constrained, such as compact or mobile systems. The Z2 GPU's lack of power connectors further simplifies installation.

The Z2 GPU provides display output through a USB Type-C port, while the B300 has no display outputs. Any use case requiring direct video output must use the Z2 GPU. The Z2 GPU also supports standard graphics APIs including DirectX 12 Ultimate, OpenGL 4.6, and Vulkan 1.4, which are absent from the B300's recorded data.

The NVIDIA B300 wins decisively in compute throughput. Its FP32 performance of 76.99 TFLOPS is 9.3 times the Z2 GPU's 8.294 TFLOPS. Its FP16 performance of 1,231.8 TFLOPS is 148 times higher. These figures indicate dominance in scientific computing, simulation, and any FP32-heavy workload.

The B300 wins in memory capacity and bandwidth. Its 144 GB of HBM3e provides 9 times the capacity of the Z2 GPU's 16 GB, and its 4.10 TB/s bandwidth is 34 times higher. This makes the B300 suitable for large model inference, data processing, and workloads with massive memory footprints.

The B300's 592 tensor cores give it a clear advantage in AI and machine learning tasks, as the Z2 GPU has no tensor cores recorded. The B300's 16:1 FP16 ratio indicates it is designed for mixed-precision training and inference. The B300 also uses PCIe 5.0 x16, which provides a modern high-bandwidth host connection.

For texture-heavy workloads, the B300 wins with 1,202.9 GTexel/s versus 129.6 GTexel/s, a 9.3-fold advantage. Its 592 TMUs handle texture filtering far faster than the Z2 GPU's 48 TMUs.

The B300's larger transistor count of 104,000 million versus 25,390 million suggests more complex and capable hardware overall. The Z2 GPU's higher transistor density of 142.6M per mm² indicates a more compact design on a smaller die.

In summary, the Z2 GPU serves display-oriented, low-power, and raster-focused scenarios. The B300 serves compute-intensive, memory-hungry, and AI-driven server workloads. The data shows no overlap in their optimal usage domains.