AMD Instinct MI300 vs AMD Radeon Instinct MI300X Comparison

Head-to-Head Benchmarks

The database contains no recorded benchmark scores for either the AMD Instinct MI300 or the AMD Radeon Instinct MI300X. Both entries show an average benchmark score of zero and a percentile ranking of 50 out of all GPUs, which is a neutral placeholder rather than a measured result. The head-to-head benchmark array is empty, and the win counts for both parts stand at zero. This means direct performance comparisons must be derived from the architectural and specification data recorded in the database rather than from executed workloads.

The most significant measurable difference between the two accelerators appears in compute throughput. The MI300X records an FP32 output of 81.72 TFLOPS, which is 70.7% higher than the MI300's 47.87 TFLOPS. That gap is substantial for any single-precision workload, and it suggests the MI300X carries a meaningful advantage in scientific computing and simulation tasks that rely on FP32 math.

The FP16 comparison is more complex. The MI300 lists FP16 at 47.87 TFLOPS with a 1:1 ratio to FP32, meaning it processes half-precision at the same rate as single-precision. The MI300X lists FP16 at 653.7 TFLOPS with an 8:1 ratio, which represents a massive throughput advantage for mixed-precision and AI training workloads. The 8:1 ratio indicates the MI300X uses dedicated matrix or tensor-style acceleration for half-precision operations, while the MI300 appears to run FP16 through the same datapath as FP32. For neural network training or inference, the MI300X would process FP16 operations at a rate over 13 times higher than the MI300.

Memory capacity and bandwidth also diverge sharply. The MI300X carries 192 GB of HBM3 memory with 10.3 TB/s of bandwidth. The MI300 carries 128 GB of the same HBM3 type with 5.32 TB/s. The MI300X delivers nearly double the memory bandwidth, which is a critical factor for large language models and other memory-bound workloads. The capacity increase of 64 GB also allows larger model weights and datasets to reside on a single accelerator without spilling to host memory.

Texture rate shows a similar pattern. The MI300X records 2,553.6 GTexel/s against the MI300's 1,496.0 GTexel/s, a 70.7% improvement that mirrors the FP32 gain. Both cards report a pixel rate of 0 MPixel/s, which is consistent with their lack of display outputs. Neither part is designed to drive a monitor, and both list no display outputs in the database.

Clock behavior explains part of the performance gap. Both parts share a 1000 MHz base clock, but the boost clock differs: the MI300X reaches 2100 MHz while the MI300 tops out at 1700 MHz. That 400 MHz boost advantage, combined with a higher shader core count, accounts for the large FP32 delta. The MI300X also runs its memory at 2525 MHz (10.1 Gbps effective) versus 1300 MHz (5.2 Gbps effective) on the MI300, which explains the bandwidth doubling.

Power draw scales with performance. The MI300X has a TDP of 750 W and a suggested power supply of 1150 W. The MI300 has a TDP of 600 W and a suggested power supply of 1000 W. The MI300X consumes 150 W more under load, which is reasonable given the doubling of memory bandwidth and the substantial increase in shader resources. The power connector situation differs as well: the MI300 uses two 8-pin connectors, while the MI300X lists no connectors because it is an OAM module designed for board-level integration rather than standalone card mounting.

FAQ

Q: Which card has more memory bandwidth?

A: The AMD Radeon Instinct MI300X records 10.3 TB/s of bandwidth, while the AMD Instinct MI300 records 5.32 TB/s. The MI300X offers roughly double the memory bandwidth.

Q: What is the FP32 performance difference?

A: The MI300X achieves 81.72 TFLOPS in FP32, while the MI300 achieves 47.87 TFLOPS. The MI300X delivers approximately 70.7% more single-precision throughput.

Q: Why does the FP16 figure differ so much between the two?

A: The MI300 lists FP16 at 47.87 TFLOPS with a 1:1 ratio to FP32, meaning it uses the same datapath. The MI300X lists FP16 at 653.7 TFLOPS with an 8:1 ratio, indicating dedicated half-precision acceleration, likely through specialized matrix units.

Q: Do both cards use the same chip and manufacturing process?

A: Yes. Both use the Aqua Vanjaram chip, CDNA 3.0 architecture, a 5 nm process from TSMC, 153,000 million transistors, and a 1017 mm² die size.

Q: Are there any display outputs on either card?

A: No. Both the MI300 and MI300X list "No outputs" for display connections, and both record a pixel rate of 0 MPixel/s. They are compute-only accelerators.

Q: How does the power requirement differ?

A: The MI300 has a TDP of 600 W with a suggested power supply of 1000 W and two 8-pin connectors. The MI300X has a TDP of 750 W with a suggested power supply of 1150 W and no connectors because it uses an OAM module form factor.

Architecture Differences

Both accelerators share the same fundamental architecture. The chip is Aqua Vanjaram, built on CDNA 3.0, produced at TSMC on a 5 nm process. The transistor count is identical at 153,000 million, and the die size is the same at 1017 mm². The transistor density of 150.4M per mm² is therefore also identical. The memory type is HBM3 on both, and the bus width is 8192 bit on both.

The architectural divergence appears in resource allocation. The MI300X carries 19,456 shading units and 1,216 texture mapping units. The MI300 carries 14,080 shading units and 880 texture mapping units. That represents 5,376 additional shaders and 336 additional TMUs on the MI300X, a 38.2% increase in shader count and a 38.2% increase in texture units. The consistency of that ratio suggests the MI300X uses a larger configuration of the same compute blocks rather than a different design.

The FP16 implementation differs in a way that points to a structural change. The MI300's 1:1 FP16 to FP32 ratio means half-precision operations flow through the standard shader pipeline. The MI300X's 8:1 ratio indicates a separate, faster path for half-precision math, likely through matrix cores or tensor-like accelerators that are not exposed as separate tensor core fields in the database. Both entries list tensor cores as null, so the database does not provide a direct count, but the FP16 ratio is the observable evidence of this difference.

Both parts have zero raster operation units, consistent with their lack of display capabilities. The API support is listed as N/A for DirectX, OpenGL, and Vulkan on the MI300, while the MI300X lists null values for those fields. In practical terms, neither card exposes graphics APIs, reinforcing their role as pure compute accelerators. The ROP count of zero and pixel rate of 0 MPixel/s confirm that neither part rasterizes graphics.

The power delivery architecture differs. The MI300 uses two 8-pin power connectors, suggesting a pluggable card design. The MI300X lists no connectors and is classified as an OAM module, meaning it receives power through the carrier board. The MI300X also has no recorded dimensions, while the MI300 measures 267 mm in length and 111 mm in height.

Specification Differences

The two entries share many base specifications. Both use the Aqua Vanjaram chip, CDNA 3.0 architecture, 5 nm TSMC process, 153,000 million transistors, 1017 mm² die size, HBM3 memory, 8192 bit bus width, 1000 MHz base clock, PCIe 5.0 x16 interface, and no display outputs.

The differences are as follows. The MI300X boosts to 2100 MHz versus 1700 MHz on the MI300. The MI300X memory clock is 2525 MHz (10.1 Gbps effective) versus 1300 MHz (5.2 Gbps effective) on the MI300. The MI300X has 192 GB of memory versus 128 GB on the MI300. The MI300X has 19,456 shading units versus 14,080, and 1,216 TMUs versus 880. The MI300X texture rate is 2,553.6 GTexel/s versus 1,496.0 GTexel/s. The MI300X FP32 is 81.72 TFLOPS versus 47.87 TFLOPS. The MI300X FP16 is 653.7 TFLOPS with an 8:1 ratio, while the MI300 FP16 is 47.87 TFLOPS with a 1:1 ratio. The MI300X TDP is 750 W versus 600 W, and the suggested PSU is 1150 W versus 1000 W. The MI300X uses an OAM module slot width with no power connectors, while the MI300 uses two 8-pin connectors. The MI300X has no recorded dimensions, while the MI300 measures 267 mm by 111 mm. The release dates differ: the MI300 launched in 2023-01-03 and the MI300X in 2023-12-05. The generation field lists the MI300 as "Instinct (MIx)" and the MI300X as "Radeon Instinct (MIx)". The predecessor field lists the MI300 as "Radeon Instinct" and the MI300X as "FirePro Data Center". Both have the same launch MSRP of null, so no pricing information is recorded.

Where Each One Wins

The AMD Instinct MI300 wins in power efficiency relative to its workload envelope. Its 600 W TDP is lower than the MI300X's 750 W, and its suggested power supply of 1000 W is more modest. For deployments with limited power delivery or cooling capacity, the MI300 presents a lighter infrastructure burden. Its 267 mm length and 111 mm height also fit standard card slots, whereas the MI300X requires OAM module integration on a carrier board.

The MI300 also wins on launch timing, having appeared on 2023-01-03, roughly 11 months before the MI300X's 2023-12-05 release. For organizations that needed CDNA 3.0 compute in early 2023, the MI300 was the available option.

The AMD Radeon Instinct MI300X wins on every raw performance metric recorded. Its FP32 throughput of 81.72 TFLOPS exceeds the MI300 by 33.85 TFLOPS. Its FP16 throughput of 653.7 TFLOPS dwarfs the MI300's 47.87 TFLOPS by more than an order of magnitude. Its memory capacity of 192 GB exceeds the MI300's 128 GB by 64 GB. Its memory bandwidth of 10.3 TB/s nearly doubles the MI300's 5.32 TB/s. Its texture rate of 2,553.6 GTexel/s exceeds the MI300's 1,496.0 GTexel/s. Its boost clock of 2100 MHz surpasses the MI300's 1700 MHz.

For memory-bound workloads, the MI300X bandwidth advantage is decisive. Large language model inference and training typically scale with memory capacity and bandwidth, and the MI300X offers more of both. The 192 GB capacity allows larger model weights to stay resident on the accelerator, avoiding host memory transfers that would bottleneck the MI300's lower bandwidth.

For mixed-precision AI workloads, the MI300X FP16 8:1 ratio is the standout feature. The ability to process half-precision at 653.7 TFLOPS makes it suited for transformer training, recommendation systems, and other matrix-heavy operations. The MI300's 1:1 FP16 ratio offers no such acceleration, capping half-precision work at the same rate as FP32.

The Verdict

The database shows a clear performance hierarchy. The AMD Radeon Instinct MI300X is the stronger accelerator on every measured compute and memory metric. Its 81.72 TFLOPS FP32, 653.7 TFLOPS FP16, 192 GB memory, and 10.3 TB/s bandwidth make it the appropriate choice for workloads where raw throughput is the priority. The 750 W TDP and OAM module form factor indicate it is designed for dense, high-power server deployments where performance per accelerator matters more than power per accelerator.

The AMD Instinct MI300 is the more conservative option. Its 600 W TDP, two 8-pin connectors, and standard card dimensions allow integration into systems that cannot accommodate OAM modules. Its 128 GB memory and 5.32 TB/s bandwidth are still substantial, and its 47.87 TFLOPS FP32 is a capable figure for many compute tasks. The 1:1 FP16 ratio limits its appeal for AI training, but for FP32-centric scientific computing, it remains viable.

The choice depends on the workload profile. For AI training, mixed-precision inference, or any task that can exploit the 8:1 FP16 ratio, the MI300X is the clear selection. For FP32 scientific simulation, power-constrained environments, or standard PCIe card slots, the MI300 fits. The release date gap of 11 months also matters: the MI300 was available earlier, while the MI300X arrived later with higher specifications. Both parts share the same node, architecture, and chip, so the differences are purely in configuration, clock speeds, memory size, and power envelope. The data does not record any benchmark scores, so real-world performance ratios beyond the listed specifications cannot be confirmed. What the data does show is a consistent pattern: the MI300X trades higher power consumption and a specialized module form factor for substantially higher compute density, memory capacity, and bandwidth.