AMD Instinct MI300A vs AMD Radeon Instinct MI300 Comparison

FAQ

Q: What are the core specifications of the AMD Instinct MI300A and the AMD Radeon Instinct MI300?

A: Both GPUs share the same Aqua Vanjaram chip, CDNA 3.0 architecture, 5 nm TSMC process, 153,000 million transistors, and a 1017 mm² die size. The MI300A has 14,592 shading units, 912 TMUs, a 2100 MHz boost clock, and 128 GB of HBM3 memory with 5.32 TB/s bandwidth. The MI300 has 14,080 shading units, 880 TMUs, a 1700 MHz boost clock, and 128 GB of HBM3 memory with 6.55 TB/s bandwidth.

Q: How do the boost clocks compare between the two cards?

A: The MI300A boosts to 2100 MHz, which is 400 MHz higher than the MI300's 1700 MHz boost. This clock advantage directly contributes to the MI300A's higher compute throughput.

Q: What are the FP32 performance figures for each?

A: The MI300A delivers 61.29 TFLOPS of FP32 compute, while the MI300 delivers 47.87 TFLOPS. The MI300A is roughly 28% ahead in single-precision floating-point performance.

Q: Do the cooling and power requirements differ?

A: Yes. The MI300A has a 750 W TDP with a suggested 1150 W PSU and uses an OAM Module slot width with no power connectors (board-mounted power). The MI300 has a 600 W TDP with a suggested 1000 W PSU and uses two 8-pin power connectors. The MI300 also has physical dimensions of 267 mm length and 111 mm height.

Q: Are there any display outputs on either card?

A: No. Both the MI300A and MI300 have no display outputs, confirming they are compute-only accelerators for data center use.

Q: What is the memory clock difference?

A: The MI300 runs its memory at 1600 MHz (6.4 Gbps effective), while the MI300A runs at 1300 MHz (5.2 Gbps effective). This gives the MI300 a higher memory bandwidth of 6.55 TB/s versus 5.32 TB/s on the MI300A.

The Verdict

The data shows two distinct accelerators built on the same chip but tuned for different workloads. The AMD Instinct MI300A is the compute-density champion: it has 512 more shading units, 32 more TMUs, and a 400 MHz higher boost clock, resulting in 61.29 TFLOPS FP32 versus 47.87 TFLOPS on the MI300. This is a 13.42 TFLOPS gap, or roughly 28% more raw single-precision throughput. For any workload that scales with FP32 compute, the MI300A is the clear choice.

The AMD Radeon Instinct MI300 counters with a memory advantage. Its memory runs at 6.4 Gbps effective versus 5.2 Gbps, yielding 6.55 TB/s of bandwidth compared to 5.32 TB/s. That is a 1.23 TB/s difference, approximately 23% more memory bandwidth. The MI300 also consumes 150 W less power (600 W TDP versus 750 W) and requires a 1000 W PSU instead of 1150 W. It fits in a standard PCIe slot form factor with two 8-pin connectors, whereas the MI300A uses an OAM Module.

The MI300 is the better pick for memory-bandwidth-bound tasks, such as large sparse matrix operations or data movement-heavy inference, where the extra bandwidth and lower power envelope matter more than raw FP32. The MI300A is the better pick for compute-bound training or dense math workloads where the higher shading unit count and clock speed translate directly to faster execution.

Head-to-Head Benchmarks

The head-to-head benchmark data is empty, so the comparison relies on the recorded specification-derived performance metrics. The largest win for the MI300A is in texture rate: it delivers 1,915.2 GTexel/s versus 1,496.0 GTexel/s on the MI300. That is a difference of 419.2 GTexel/s, meaning the MI300A processes textures about 28% faster. This follows directly from its higher TMU count (912 versus 880) and boost clock (2100 MHz versus 1700 MHz).

In FP32 compute, the MI300A's 61.29 TFLOPS beats the MI300's 47.87 TFLOPS by 13.42 TFLOPS, a 28% advantage. This is the headline compute win for the MI300A. The MI300A also has more shading units (14,592 versus 14,080), which contributes to its higher throughput per clock.

The MI300 wins decisively in memory bandwidth. Its 6.55 TB/s is 1.23 TB/s higher than the MI300A's 5.32 TB/s, a 23% advantage. This is driven by the memory clock: 1600 MHz versus 1300 MHz. The MI300 also has a higher effective data rate of 6.4 Gbps versus 5.2 Gbps.

Both cards have identical pixel rates of 0 MPixel/s and zero ROPs, reflecting their non-rendering compute focus. The MI300 has a higher FP16 figure of 383.0 TFLOPS (8:1), while the MI300A does not list an FP16 value in the recorded data, so no direct comparison is possible on that metric.

Specification Differences

The MI300A and MI300 differ in several key fields. The MI300A has 14,592 shading units versus 14,080 on the MI300, a difference of 512 units. TMUs are 912 on the MI300A versus 880 on the MI300, a difference of 32 units. Both have zero ROPs.

Boost clock: the MI300A runs at 2100 MHz, the MI300 at 1700 MHz. Base clock is identical at 1000 MHz. Memory clock differs: 1300 MHz (5.2 Gbps effective) on the MI300A versus 1600 MHz (6.4 Gbps effective) on the MI300.

Memory bandwidth: 5.32 TB/s on the MI300A versus 6.55 TB/s on the MI300. Both use 128 GB of HBM3 with an 8192-bit bus.

Texture rate: 1,915.2 GTexel/s on the MI300A versus 1,496.0 GTexel/s on the MI300. FP32: 61.29 TFLOPS versus 47.87 TFLOPS. FP16: the MI300 lists 383.0 TFLOPS (8:1), the MI300A does not list a value.

TDP: 750 W on the MI300A versus 600 W on the MI300. Suggested PSU: 1150 W versus 1000 W. Slot width: OAM Module on the MI300A, none listed on the MI300. Power connectors: none on the MI300A, 2x 8-pin on the MI300. Dimensions: the MI300 measures 267 mm by 111 mm, the MI300A has no listed dimensions.

Release dates: the MI300 launched on January 3, 2023, while the MI300A launched on December 5, 2023. The MI300 lists a predecessor of FirePro Data Center, while the MI300A lists Radeon Instinct. Both have the same generation tag of Instinct (MIx) or Radeon Instinct (MIx) respectively.

Architecture Differences

Both accelerators use the same Aqua Vanjaram chip, built on TSMC's 5 nm process with 153,000 million transistors on a 1017 mm² die. Transistor density is identical at 150.4M per mm². Both are CDNA 3.0 architecture, designed for data center compute rather than graphics.

The core layout differs slightly: the MI300A packs 14,592 shading units and 912 TMUs, while the MI300 has 14,080 shading units and 880 TMUs. This means the MI300A has 3.6% more shading units and 3.6% more TMUs. The MI300A also runs 23.5% higher boost clock (2100 MHz versus 1700 MHz), which amplifies the core count advantage.

Memory architecture is where the MI300 pulls ahead. Both use 128 GB of HBM3 with an 8192-bit bus, but the MI300's memory clock of 1600 MHz versus 1300 MHz gives it 23% more bandwidth. The MI300A's lower memory clock suggests a design trade-off favoring compute density over memory throughput.

Neither card has ROPs or pixel output, confirming they are not intended for rasterization. Both have no display outputs. API support is listed as N/A for the MI300A, while the MI300 has null values for DirectX, OpenGL, and Vulkan, reinforcing their compute-only nature.

The MI300A uses an OAM Module form factor with no power connectors, indicating a board-level power delivery design. The MI300 uses a standard PCIe slot with two 8-pin connectors, making it easier to integrate into existing server infrastructure. The MI300's physical dimensions of 267 mm by 111 mm fit standard PCIe slots, while the MI300A's OAM form factor requires specialized mounting.

Where Each One Wins

The MI300A wins in raw compute throughput. Its 61.29 TFLOPS FP32 is 28% higher than the MI300's 47.87 TFLOPS, making it the stronger choice for dense linear algebra, deep learning training, and any workload where FLOPs are the bottleneck. Its texture rate of 1,915.2 GTexel/s versus 1,496.0 GTexel/s also indicates faster processing of structured data patterns, useful in convolution operations.

The MI300 wins in memory-bound scenarios. Its 6.55 TB/s bandwidth is 23% higher than the MI300A's 5.32 TB/s, which matters for large batch inference, graph analytics, or sparse matrix multiplication where data movement dominates. The MI300 also consumes 150 W less power (600 W versus 750 W) and requires a lower-spec PSU (1000 W versus 1150 W), making it more suitable for power-constrained data center racks.

The MI300's standard PCIe form factor with 2x 8-pin connectors makes it easier to deploy in existing servers, whereas the MI300A's OAM Module requires a different chassis design. The MI300's earlier release date (January 2023 versus December 2023) means it has had more time in production environments.

For FP16 workloads, the MI300 lists 383.0 TFLOPS (8:1), a figure not recorded for the MI300A. This suggests the MI300 may be the better choice for mixed-precision training, though the lack of a comparable FP16 number for the MI300A prevents a full comparison.

In summary: pick the MI300A for compute-bound training and dense math where FP32 throughput is king. Pick the MI300 for memory-bandwidth-bound inference, sparse workloads, or deployments where power and form factor flexibility are priorities.