AMD Instinct MI300X vs AMD Radeon Instinct MI308X Comparison

FAQ

Q: What is the core architecture shared by both accelerators?

A: Both the AMD Instinct MI300X and the AMD Radeon Instinct MI308X use the same Aqua Vanjaram chip, built on CDNA 3.0 architecture, fabricated on a 5 nm process at TSMC.

Q: Do the two cards have identical memory configurations?

A: Yes, both have 192 GB of HBM3 memory on an 8192-bit bus. However, the MI308X operates its memory at 2525 MHz (10.1 Gbps effective) versus 1300 MHz (5.2 Gbps effective) on the MI300X, resulting in a major bandwidth difference.

Q: What is the key performance metric difference between them?

A: The FP16 throughput differs substantially. The MI300X delivers 81.72 TFLOPS (1:1 ratio), while the MI308X reaches 653.7 TFLOPS (8:1 ratio). Their FP32 performance is identical at 81.72 TFLOPS.

Q: How do their benchmark scores compare?

A: The MI300X has a recorded Geekbench OpenCL score of 317,994, placing it in the 100th percentile of all GPUs. The MI308X currently has no benchmark scores in the database, with a 50th percentile ranking and an average score of zero.

Q: Are there any differences in their physical specifications?

A: No. Both are OAM modules with no power connectors, no display outputs, a 750 W TDP, and a suggested PSU of 1150 W. They also share the same PCIe 5.0 x16 bus interface.

Q: What are the nearest competitive rivals for the MI300X?

A: According to the database, the NVIDIA H200 NVL scores 334,891 (5% higher), the NVIDIA B200 scores 345,482 (8% higher), the NVIDIA L40S scores 295,763 (7.5% lower), and the NVIDIA RTX 6000 Ada Generation scores 287,237 (10.7% lower).

Architecture Differences

Both accelerators are built on the same fundamental die: the Aqua Vanjaram chip with 153,000 million transistors on a 1017 mm² die. The transistor density is identical at 150.4M per mm². This means the core compute resources are essentially the same silicon. The shading units count is 19,456 on both, and the texture mapping units are 1,216 each. Neither has ROPs, with a pixel rate of 0 MPixel/s, and both show a texture rate of 2,553.6 GTexel/s.

The architectural split appears in the memory clock and the FP16 execution path. The MI300X runs its HBM3 memory at 1300 MHz with a 5.2 Gbps effective data rate, yielding 5.32 TB/s of bandwidth. The MI308X runs the same memory type and bus width at 2525 MHz with a 10.1 Gbps effective rate, doubling the bandwidth to 10.3 TB/s. This is a substantial difference for memory-bound workloads.

The FP16 capability also diverges. The MI300X offers 81.72 TFLOPS with a 1:1 ratio, meaning it processes FP16 at the same rate as FP32. The MI308X offers 653.7 TFLOPS with an 8:1 ratio, indicating a specialized or accelerated path for FP16 that is 8 times its FP32 rate. This suggests the MI308X is tuned for workloads that leverage reduced precision, such as AI inference or training where FP16 is common.

Both cards share the same CDNA 3.0 architecture and generation label (Instinct MIx for the MI300X, Radeon Instinct MIx for the MI308X). The process node is 5 nm at TSMC for both. There are no distinct RT cores or tensor cores listed for either, which is characteristic of the CDNA line focusing on compute rather than graphics. The API support is N/A for both, and neither has display outputs, confirming their server-oriented design.

Where Each One Wins

The data shows a clear division based on workload type. The MI300X wins in general-purpose compute because it has a recorded benchmark score. Its Geekbench OpenCL result of 317,994 places it at the 100th percentile of all GPUs, meaning it outperforms every other recorded device in that test. This gives it a measurable advantage in tasks that rely on standard compute paths, such as FP32 processing, HPC simulations, or any workload that does not specifically optimize for FP16 with high throughput.

The MI308X wins in memory bandwidth and FP16 throughput. The doubling of memory bandwidth from 5.32 TB/s to 10.3 TB/s directly benefits applications that are bandwidth-limited, such as large-scale matrix operations, data analytics, or memory-heavy AI models. The FP16 figure of 653.7 TFLOPS is 8 times the MI300X's FP16 output, so any workload that can use FP16 arithmetic will see a massive theoretical advantage on the MI308X.

There is no head-to-head benchmark data in the database, and the MI308X has no standalone benchmarks. This means the MI300X's win is empirical, based on a measured score, while the MI308X's advantages are purely specification-based. The absence of benchmark data for the MI308X is a notable gap, as the theoretical FP16 and bandwidth advantages cannot be verified against real-world performance yet.

In terms of rival comparisons, the MI300X sits between several NVIDIA parts. It is 7.5% ahead of the L40S and 10.7% ahead of the RTX 6000 Ada Generation, but 5% behind the H200 NVL and 8% behind the B200. This positions the MI300X as a strong mid-to-high performer, though not the absolute leader. The MI308X has no rival data, so its competitive standing is unknown.

Specification Differences

The two cards differ in only a few recorded fields, all of which are significant. The memory clock is the most obvious: the MI300X runs at 1300 MHz with 5.2 Gbps effective, while the MI308X runs at 2525 MHz with 10.1 Gbps effective. This leads to the bandwidth difference of 5.32 TB/s versus 10.3 TB/s.

The FP16 performance is another key divergence. The MI300X lists 81.72 TFLOPS with a 1:1 ratio, while the MI308X lists 653.7 TFLOPS with an 8:1 ratio. The FP32 performance is identical at 81.72 TFLOPS for both, as are the shading units (19,456), TMUs (1,216), texture rate (2,553.6 GTexel/s), and pixel rate (0 MPixel/s).

The generation labels differ slightly: the MI300X is listed under "Instinct (MIx)" while the MI308X is under "Radeon Instinct (MIx)". The predecessor fields also differ: the MI300X lists "Radeon Instinct" as its predecessor, while the MI308X lists "FirePro Data Center". All other core specifications are identical, including transistors, die size, process node, TDP, slot width, power connectors, suggested PSU, bus interface, display outputs, and API support.

The benchmark data is another difference. The MI300X has one benchmark entry (Geekbench OpenCL with a score of 317,994) and a 100th percentile ranking. The MI308X has no benchmark entries, a 50th percentile ranking, and an average score of zero. The MI300X also has four nearest rivals listed, while the MI308X has none.

Head-to-Head Benchmarks

There are no recorded head-to-head benchmarks between the MI300X and MI308X in the database. The winsA and winsB fields are both zero, indicating no direct comparison data exists. This forces the analysis to rely on the individual benchmarks and specification differences.

The only empirical data point is the MI300X's Geekbench OpenCL score of 317,994. This score places it in the 100th percentile of all GPUs, meaning it is at the top of the recorded distribution. Against its nearest rivals, the MI300X is 7.5% ahead of the NVIDIA L40S (which scores 295,763) and 10.7% ahead of the NVIDIA RTX 6000 Ada Generation (which scores 287,237). It trails the NVIDIA H200 NVL by 5% (334,891) and the NVIDIA B200 by 8% (345,482).

The MI308X has no benchmark scores, so there is no direct way to measure its performance against the MI300X or any other GPU. The specification sheet suggests it would excel in FP16 and memory bandwidth, but the database does not confirm this with actual results. The 50th percentile ranking for the MI308X is a placeholder, not a measured outcome, as its average score is zero.

When interpreting the MI300X's score, the delta percentages against rivals are more telling than the raw number. A 7.5% lead over the L40S and a 10.7% lead over the RTX 6000 Ada Generation show a comfortable margin in OpenCL compute. The 5% and 8% deficits to the H200 NVL and B200, respectively, indicate that the MI300X is competitive but not dominant at the very high end. The MI308X, with its doubled bandwidth and octupled FP16, could potentially close or reverse those gaps, but that is speculative without data.

The FP16 difference is the most striking specification gap. The MI308X's 653.7 TFLOPS is 8 times the MI300X's 81.72 TFLOPS. For any workload that can exploit FP16, this is a massive theoretical advantage. However, the 8:1 ratio on the MI308X implies that FP16 is processed at a fraction of the FP32 rate, likely using a different execution path or packed math. The MI300X's 1:1 ratio means it handles FP16 at the same speed as FP32, which is simpler but slower for pure FP16 tasks.

Memory bandwidth is the second major gap. The MI308X's 10.3 TB/s is nearly double the MI300X's 5.32 TB/s. This can directly impact any workload that reads or writes large datasets, such as large language model inference, scientific computing, or real-time data processing. The identical memory size and bus width mean the only variable is the clock speed, which the MI308X has pushed significantly higher.

The Verdict

The data supports a clear but conditional choice. For general-purpose compute with a verified performance record, the AMD Instinct MI300X is the safer pick. Its Geekbench OpenCL score of 317,994 is real and places it at the 100th percentile. It also has known competitive positioning against NVIDIA parts, being 7.5% ahead of the L40S and 10.7% ahead of the RTX 6000 Ada Generation, while trailing the H200 NVL by 5% and B200 by 8%. This makes it a proven performer in mixed workloads.

For workloads specifically targeting FP16 throughput or memory bandwidth, the AMD Radeon Instinct MI308X has the theoretical edge. Its 653.7 TFLOPS FP16 capability is 8 times the MI300X's, and its 10.3 TB/s bandwidth is nearly double. These specifications suggest it is designed for AI training or inference where reduced precision and large data movement are the bottlenecks. However, the absence of any benchmark scores means this advantage is unverified. The database shows zero recorded performance for the MI308X, so its real-world behavior remains unknown.

The specification similarities are extensive: both use the same chip, process node, transistor count, die size, shading units, TMUs, FP32 performance, TDP, and physical form factor. The differences are concentrated in memory clock and FP16 path. This means the MI308X is not a generational leap but a specialized variant of the same silicon, tuned for specific compute patterns.

The choice hinges on data availability. The MI300X has a measured score and rival comparisons, making it a quantifiable option. The MI308X offers compelling specifications but no evidence of actual performance. For users who rely on verified benchmarks, the MI300X is the only choice with recorded data. For those who can tolerate unverified specifications and need maximum FP16 or bandwidth, the MI308X presents a theoretical advantage that the database cannot yet confirm or deny. The MI300X's 100th percentile ranking versus the MI308X's 50th percentile placeholder, which is not based on any test, underscores the lack of empirical evidence for the latter. The verdict is that the MI300X is the proven workhorse, while the MI308X is an unproven specialist.