AMD Instinct MI350P vs NVIDIA Rubin GPU Comparison

FAQ

Q: What are the core architectural identities of these two accelerators?

A: The AMD Instinct MI350P uses the CDNA 4.0 architecture and is built on a 3 nm process at TSMC. The NVIDIA Rubin GPU uses the Rubin architecture, also built on a 3 nm process at TSMC, and is part of the Server Rubin (Rxx) generation.

Q: How do the memory subsystems compare between the two?

A: The AMD Instinct MI350P has 144 GB of HBM3e memory on an 8192-bit bus, delivering 8.19 TB/s of bandwidth. The NVIDIA Rubin GPU has 288 GB of HBM4 memory on a 16384-bit bus, delivering 22.1 TB/s of bandwidth, roughly 2.7 times the capacity and bandwidth of the MI350P.

Q: What are the transistor counts and die sizes for each chip?

A: The MI350P packs 73,000 million transistors on a 1190 mm² die, giving a density of 61.3M transistors per mm². The Rubin GPU contains 336,000 million transistors on a 1456 mm² die, achieving a density of 230.8M per mm², which is significantly higher despite the larger physical die.

Q: How do the FP32 and FP16 compute capabilities differ?

A: The MI350P delivers 36.04 TFLOPS for FP32 and the same 36.04 TFLOPS for FP16, indicating a 1:1 ratio. The Rubin GPU delivers 130.0 TFLOPS for FP32 and 260.0 TFLOPS for FP16, a 2:1 ratio, meaning it has roughly 3.6 times the FP32 throughput and 7.2 times the FP16 throughput of the MI350P.

Q: What power and interface specifications separate these cards?

A: The MI350P has a 600 W TDP, uses a dual-slot form factor, a single 16-pin power connector, and a suggested 1000 W PSU with a PCIe 5.0 x16 interface. The Rubin GPU has a 2300 W TDP, uses an SXM module form factor, no listed power connector, a suggested 2700 W PSU, and a PCIe 6.0 x16 interface.

Q: When were these accelerators released and what are their production statuses?

A: The NVIDIA Rubin GPU has a release date of 2025-12-31 and is marked as "Active" in production. The AMD Instinct MI350P has a release date of 2026-05-06, which is later than the Rubin, and its production status is not listed in the database.

Where Each One Wins

The recorded data shows a clear split in strengths based on the specification sheets. The NVIDIA Rubin GPU wins decisively in raw compute throughput, memory capacity, and memory bandwidth. Its FP32 output of 130.0 TFLOPS is 3.6 times that of the MI350P's 36.04 TFLOPS, and its FP16 output of 260.0 TFLOPS is 7.2 times the MI350P's 36.04 TFLOPS. For workloads that scale with memory size, the Rubin's 288 GB versus 144 GB is a direct 2x advantage, and its 22.1 TB/s bandwidth versus 8.19 TB/s is a 2.7x advantage. The Rubin also has more shading units (28672 versus 8192), more TMUs (896 versus 512), and a higher texture rate (2,031.2 GTexel/s versus 1,126.4 GTexel/s).

The AMD Instinct MI350P wins in areas of efficiency and form factor practicality. Its 600 W TDP is dramatically lower than the Rubin's 2300 W, making it far more manageable in terms of cooling and power delivery. The MI350P's dual-slot design with a 267 mm length and 111 mm height is a standard PCIe card form factor, whereas the Rubin requires an SXM module, which is a different mounting and cooling infrastructure. The MI350P also uses the established PCIe 5.0 x16 interface, while the Rubin uses the newer PCIe 6.0 x16, which may have compatibility considerations in existing systems. The MI350P has a higher base clock at 1000 MHz versus 700 MHz, though the boost clocks are similar (2200 MHz versus 2267 MHz).

Architecture Differences

The two accelerators represent fundamentally different design philosophies within their respective architectures. The AMD Instinct MI350P uses CDNA 4.0, which is designed specifically for compute and AI workloads, and it integrates 8192 shading units organized into a 128CU configuration as indicated by the chip name "MI350 128CU." The NVIDIA Rubin GPU uses the Rubin architecture with the GR100 chip, which scales to 28672 shading units and includes 896 tensor cores, a feature not listed for the MI350P.

The memory architectures differ substantially. The MI350P uses HBM3e, which is the previous generation of high-bandwidth memory, in a 144 GB configuration. The Rubin uses HBM4, the next generation, in a 288 GB configuration. The bus widths reflect this difference: 8192 bit for the MI350P versus 16384 bit for the Rubin, explaining the bandwidth gap. The memory clocks also differ, with the MI350P running at 2000 MHz (8 Gbps effective) and the Rubin at 2695 MHz (10.8 Gbps effective).

The transistor densities tell a story of manufacturing efficiency. Despite both using a 3 nm TSMC process, the Rubin achieves 230.8M transistors per mm² versus 61.3M for the MI350P, indicating a much more compact logic design or a significantly different mix of SRAM and logic. The Rubin's die size is 1456 mm² versus 1190 mm² for the MI350P, but the transistor count is over 4.6 times higher, showing that the Rubin packs far more logic into its silicon.

Specification Differences

The two cards differ in nearly every measurable specification field. The MI350P has 8192 shading units, 512 TMUs, and 0 ROPs, while the Rubin has 28672 shading units, 896 TMUs, and 24 ROPs. The pixel rate for the MI350P is 0 MPixel/s, while the Rubin achieves 54.41 GPixel/s. The texture rates are 1,126.4 GTexel/s for the MI350P and 2,031.2 GTexel/s for the Rubin.

Clock speeds show a nuanced picture. The MI350P has a higher base clock at 1000 MHz versus 700 MHz for the Rubin. The boost clocks are close, with the MI350P at 2200 MHz and the Rubin at 2267 MHz. The memory clocks are 2000 MHz (8 Gbps effective) for the MI350P and 2695 MHz (10.8 Gbps effective) for the Rubin.

Power and physical specifications differ greatly. The MI350P is rated at 600 W TDP with a dual-slot form factor, 267 mm length, 111 mm height, and 40 mm width. The Rubin is rated at 2300 W TDP with an SXM module form factor and no listed dimensions. The MI350P uses a 1x 16-pin power connector and suggests a 1000 W PSU, while the Rubin has no listed power connector and suggests a 2700 W PSU. The bus interfaces are PCIe 5.0 x16 for the MI350P and PCIe 6.0 x16 for the Rubin. Neither card has display outputs, and both list their APIs as N/A for DirectX, OpenGL, and Vulkan.

Head-to-Head Benchmarks

The database contains no direct head-to-head benchmark results for these two accelerators, and the average benchmark score for both is 0, placing both at the 50th percentile of all GPUs. However, the specification data provides a basis for comparison that shows where each card would dominate in a hypothetical benchmark scenario.

The most significant gap is in FP16 compute, which is critical for AI training and inference workloads. The Rubin's 260.0 TFLOPS is 7.2 times the MI350P's 36.04 TFLOPS. This means that for mixed-precision training, the Rubin would complete iterations in roughly one-seventh the time, assuming the workload scales linearly with compute throughput. The FP32 gap is similarly large at 3.6 times (130.0 TFLOPS versus 36.04 TFLOPS), which matters for scientific computing and workloads that require full precision.

Memory bandwidth is the other major differentiator. The Rubin's 22.1 TB/s versus the MI350P's 8.19 TB/s represents a 2.7x advantage. For memory-bound workloads, such as large language model inference with substantial model weights, this bandwidth advantage directly translates to lower latency per token or the ability to serve larger batch sizes. The capacity difference (288 GB versus 144 GB) allows the Rubin to hold larger models entirely in memory, avoiding the need for memory partitioning or offloading.

The MI350P is not without its advantages in a head-to-head comparison. Its 600 W TDP means that in a system with a given power budget, multiple MI350P cards could be deployed where a single Rubin might be the limit. The MI350P's higher base clock of 1000 MHz versus 700 MHz suggests it may sustain throughput better in power-constrained scenarios, though the boost clocks are nearly identical. The texture rate gap is 1.8x (2,031.2 GTexel/s versus 1,126.4 GTexel/s), which is smaller than the compute gap, indicating the MI350P is relatively more balanced in its resource allocation.

The Verdict

The data indicates that the NVIDIA Rubin GPU is the superior performer in absolute terms. Its FP32 output of 130.0 TFLOPS, FP16 output of 260.0 TFLOPS, memory capacity of 288 GB, and bandwidth of 22.1 TB/s place it in a higher performance class entirely. For workloads where peak compute and memory capacity are the primary constraints, the Rubin is the clear choice based on these specifications.

The AMD Instinct MI350P, however, has a distinct role. Its 600 W TDP and dual-slot form factor make it suitable for systems that cannot accommodate the Rubin's 2300 W power requirement and SXM module form factor. The MI350P's PCIe 5.0 interface is also more widely supported in existing server platforms than the Rubin's PCIe 6.0. For organizations with power delivery limits or those looking to deploy multiple accelerators per server, the MI350P offers a viable path forward, albeit with significantly lower compute and memory specifications.

The release timeline also matters for planning. The Rubin has a release date of 2025-12-31 and is marked as active, while the MI350P has a release date of 2026-05-06. This means the Rubin is available first, and the MI350P arrives several months later. The MI350P's predecessor is listed as "Radeon Instinct," while the Rubin's predecessor is "Server Blackwell," indicating different lineage paths.

In practical terms, the choice comes down to the workload and the physical constraints of the deployment. The Rubin delivers 3.6 times the FP32 performance, 7.2 times the FP16 performance, 2.7 times the memory bandwidth, and twice the memory capacity, but it requires 3.8 times the power (2300 W versus 600 W) and a different physical form factor. The MI350P offers a more conventional installation profile and a much lower power footprint, making it suitable for dense compute nodes where the Rubin's power and thermal requirements would be prohibitive. The data does not favor one card universally; it favors the Rubin for maximum performance and the MI350P for power-conscious, standard-form-factor deployments.