GPU Comparison

AMD Instinct MI300X and NVIDIA RTX 6000 Ada Generation represent two fundamentally different approaches to high-performance computing, and the benchmark data reflects that divergence clearly. The MI300X, an OAM module built for massive-scale AI and HPC deployments, posts an average benchmark score of 317,994, placing it in the 100th percentile of all GPUs. The RTX 6000 Ada, a dual-slot workstation card, achieves an average score of 287,237, sitting in the 99th percentile. The single head-to-head benchmark available, Geekbench OpenCL, shows the MI300X winning by a narrow 2% margin (317,994 vs 311,629), but this slim victory masks substantial architectural and use-case distinctions that matter far more than the raw score difference.

Where Each One Wins

The data points to a clear split: the AMD Instinct MI300X is built to win in memory-bound, large-model AI and HPC workloads, while the NVIDIA RTX 6000 Ada Generation wins in flexibility, ecosystem compatibility, and traditional graphics-adjacent compute tasks. The MI300X’s nearest rivals tell the story, it sits 8% behind the NVIDIA B200 (345,482) and 5% behind the NVIDIA H200 NVL (334,891), but it beats the NVIDIA L40S by 7.5% and the RTX 6000 Ada by 10.7%. This positioning indicates the MI300X competes at the very top tier of accelerator performance, where its 192 GB of HBM3 memory and 5.32 TB/s bandwidth are the primary weapons. For workloads that fit in memory, large language model inference, scientific simulations with massive datasets, the MI300X’s capacity advantage is decisive.

The RTX 6000 Ada, by contrast, is the more versatile tool. Its nearest rivals include the NVIDIA L40 (1.1% behind) and the L40S (2.9% ahead), showing it sits comfortably in the professional workstation segment. It wins on integration: the RTX 6000 Ada has 4x DisplayPort 1.4a outputs, full DirectX 12 Ultimate, OpenGL 4.6, and Vulkan 1.4 API support, while the MI300X has no display outputs and lists N/A for all graphics APIs. The RTX 6000 Ada also carries 142 RT cores and 568 tensor cores, making it suitable for real-time ray tracing, rendering, and inference workloads that require a visual output or a standard PCIe form factor. The MI300X has zero ROPs and a pixel rate of 0 MPixel/s, confirming it is not designed for any rasterization task. In short, the MI300X wins where memory capacity and bandwidth are the bottleneck; the RTX 6000 Ada wins where a GPU must do many things, including displaying results.

Architecture Differences

The architectural gap between these two accelerators is vast, starting with the silicon itself. The MI300X uses AMD’s CDNA 3.0 architecture on a chip codenamed Aqua Vanjaram, fabricated on a 5 nm process at TSMC. It packs 153,000 million transistors on a 1017 mm² die, yielding a transistor density of 150.4 million per square millimeter. The RTX 6000 Ada uses NVIDIA’s Ada Lovelace architecture on the AD102 chip, also on a 5 nm TSMC process, but with 76,300 million transistors on a 609 mm² die, for a density of 125.3 million per square millimeter. The MI300X has roughly double the transistor count and a 67% larger die, reflecting its focus on massive parallel compute and memory capacity.

Memory architecture diverges completely. The MI300X uses 192 GB of HBM3 on an 8192-bit bus, delivering 5.32 TB/s of bandwidth, a figure that dwarfs most competing accelerators. The RTX 6000 Ada uses 48 GB of GDDR6 on a 384-bit bus, delivering 960.0 GB/s. That is an 11x difference in capacity and a 5.5x difference in bandwidth, which explains why the MI300X is aimed at datasets that would never fit in the RTX 6000 Ada’s frame buffer. Clock speeds also differ: the MI300X has a base of 1000 MHz and a boost of 2100 MHz, while the RTX 6000 Ada has a lower base of 915 MHz but a higher boost of 2505 MHz. The RTX 6000 Ada compensates for its smaller memory bus with higher clocks and a more conventional shader arrangement.

Compute resources are structured differently. The MI300X has 19,456 shading units, 1,216 TMUs, and zero ROPs, with a texture rate of 2,553.6 GTexel/s and FP32 throughput of 81.72 TFLOPS. The RTX 6000 Ada has 18,176 shading units, 568 TMUs, and 192 ROPs, with a texture rate of 1,422.8 GTexel/s and FP32 throughput of 91.06 TFLOPS. Despite having fewer shaders, the RTX 6000 Ada achieves higher FP32 performance due to its higher boost clock. The MI300X has no dedicated RT or tensor cores listed, while the RTX 6000 Ada includes 142 RT cores and 568 tensor cores, giving it hardware acceleration for ray tracing and AI inference that the MI300X lacks entirely. Power and form factor reinforce the split: the MI300X is an OAM module with a 750 W TDP and no power connectors (it uses the module slot), while the RTX 6000 Ada is a dual-slot card with a 300 W TDP and a single 16-pin connector, plus a suggested PSU of 700 W versus 1150 W for the MI300X.

Head-to-Head Benchmarks

The only direct benchmark comparison in the data is Geekbench OpenCL, where the AMD Instinct MI300X scores 317,994 against the NVIDIA RTX 6000 Ada’s 311,629. That is a 2% delta in favor of the MI300X, a relatively narrow margin given the architectural differences. However, interpreting this score requires context from each card’s broader rival set. The MI300X’s average benchmark score of 317,994 is 10.7% higher than the RTX 6000 Ada’s 287,237 average, a larger gap than the single OpenCL test suggests, because the RTX 6000 Ada’s average is pulled down by its Vulkan result of 262,845, which has no MI300X counterpart. The MI300X also has a perfect 100th percentile ranking, while the RTX 6000 Ada sits at 99th percentile, indicating the MI300X is at the absolute top of the database.

Looking at rival deltas clarifies the picture. The MI300X is 7.5% ahead of the NVIDIA L40S (295,763) and 10.7% ahead of the RTX 6000 Ada, but it trails the B200 by 8% and the H200 NVL by 5%. The RTX 6000 Ada, on the other hand, is 1.1% ahead of the NVIDIA L40 (284,111) and 14.4% ahead of the NVIDIA L20 (251,147), but it is 2.9% behind the L40S and 9.7% behind the MI300X. The data shows the MI300X is competing in a higher performance tier, while the RTX 6000 Ada is the top of the workstation tier but not the top of the overall accelerator market. The 2% OpenCL win for the MI300X is the smallest margin among its recorded rival deltas, suggesting that in a synthetic compute test, the two are close, but in memory-bound real-world workloads, the MI300X’s 192 GB and 5.32 TB/s would likely widen the gap substantially, as those resources are not stressed in a generic OpenCL benchmark.

FAQ

Q: Which GPU has more memory, and how much more?

A: The AMD Instinct MI300X has 192 GB of HBM3, while the NVIDIA RTX 6000 Ada Generation has 48 GB of GDDR6. That is a 4x difference in capacity.

Q: What is the performance gap in the only head-to-head test?

A: In Geekbench OpenCL, the MI300X scores 317,994 versus the RTX 6000 Ada’s 311,629, a 2% delta in favor of the MI300X.

Q: Does the RTX 6000 Ada support display outputs?

A: Yes, the RTX 6000 Ada has 4x DisplayPort 1.4a outputs. The MI300X has no display outputs and lists N/A for DirectX, OpenGL, and Vulkan APIs.

Q: Which card has higher FP32 compute throughput?

A: The RTX 6000 Ada leads in FP32 with 91.06 TFLOPS, while the MI300X delivers 81.72 TFLOPS.

Q: What is the power consumption difference?

A: The MI300X has a 750 W TDP and requires a suggested 1150 W PSU, while the RTX 6000 Ada has a 300 W TDP and a suggested 700 W PSU.

Q: How does each card rank against its nearest competitors?

A: The MI300X is 10.7% ahead of the RTX 6000 Ada and 7.5% ahead of the L40S, but 8% behind the B200. The RTX 6000 Ada is 1.1% ahead of the L40 and 14.4% ahead of the L20, but 2.9% behind the L40S and 9.7% behind the MI300X.

Specification Differences

| Specification | AMD Instinct MI300X | NVIDIA RTX 6000 Ada Generation |

|---|---|---|

| Architecture | CDNA 3.0 | Ada Lovelace |

| Chip | Aqua Vanjaram | AD102 |

| Transistors | 153,000 million | 76,300 million |

| Die Size | 1017 mm² | 609 mm² |

| Transistor Density | 150.4M / mm² | 125.3M / mm² |

| Base Clock | 1000 MHz | 915 MHz |

| Boost Clock | 2100 MHz | 2505 MHz |

| Memory Size | 192 GB HBM3 | 48 GB GDDR6 |

| Memory Bus | 8192 bit | 384 bit |

| Memory Bandwidth | 5.32 TB/s | 960.0 GB/s |

| Shading Units | 19456 | 18176 |

| TMUs | 1216 | 568 |

| ROPs | 0 | 192 |

| RT Cores | N/A | 142 |

| Tensor Cores | N/A | 568 |

| Pixel Rate | 0 MPixel/s | 481.0 GPixel/s |

| Texture Rate | 2,553.6 GTexel/s | 1,422.8 GTexel/s |

| FP32 | 81.72 TFLOPS | 91.06 TFLOPS |

| TDP | 750 W | 300 W |

| Slot Width | OAM Module | Dual-slot |

| Power Connectors | None | 1x 16-pin |

| Suggested PSU | 1150 W | 700 W |

| Bus Interface | PCIe 5.0 x16 | PCIe 4.0 x16 |

| Display Outputs | No outputs | 4x DisplayPort 1.4a |

| DirectX | N/A | 12 Ultimate (12_2) |

| OpenGL | N/A | 4.6 |

| Vulkan | N/A | 1.4 |

| Release Date | 2023-12-05 | 2022-12-02 |

| Production Status | N/A | End-of-life |

| Launch MSRP | N/A | 6,799 USD |

The Verdict

The data supports a straightforward choice based on workload. The AMD Instinct MI300X is the pick for anyone building a large-scale compute node where memory capacity is the primary constraint. Its 192 GB of HBM3 and 5.32 TB/s bandwidth are unmatched in this comparison, and its 100th percentile ranking confirms it sits at the very top of the performance hierarchy. The 2% OpenCL win over the RTX 6000 Ada is less important than the 10.7% average score advantage, which reflects the MI300X’s ability to sustain performance across a broader range of compute tasks. The lack of display outputs, graphics APIs, and rasterization hardware means the MI300X is exclusively a compute accelerator, it will never render a frame or drive a monitor, and its 750 W TDP and OAM form factor require a server environment with appropriate power delivery.

The NVIDIA RTX 6000 Ada Generation is the choice for a professional workstation that must balance compute with flexibility. Its 91.06 TFLOPS FP32 throughput is actually higher than the MI300X’s 81.72 TFLOPS, and its 300 W TDP with a dual-slot form factor makes it deployable in standard PCIe systems. The 142 RT cores and 568 tensor cores enable hardware-accelerated ray tracing and AI inference, and the 4x DisplayPort outputs mean it can serve as a visual workstation card. Its 48 GB of GDDR6 is sufficient for many professional workloads, though it will struggle with datasets that exceed that capacity, a scenario where the MI300X’s 192 GB becomes a 4x advantage. The RTX 6000 Ada’s 99th percentile ranking is still elite, and its 14.4% lead over the NVIDIA L20 shows it outperforms lower-tier workstation cards. Ultimately, the MI300X wins on raw capacity and top-end performance; the RTX 6000 Ada wins on versatility, efficiency, and ecosystem integration. Pick the MI300X for dedicated compute servers, pick the RTX 6000 Ada for workstations that need to do more than compute alone.