AMD Instinct MI100 vs AMD Instinct MI300X Comparison

# Head-to-Head Benchmarks

The benchmark data for these two accelerators consists of a single Geekbench OpenCL test, but the gap is decisive. The AMD Instinct MI300X scores 317,994, while the AMD Instinct MI100 scores 139,035. The MI300X wins with a delta of 128.7% — a 2.29x raw performance advantage. This is not a marginal generational step; it is a doubling of compute throughput in a single OpenCL workload.

In context, the MI300X's score places it at the 100th percentile among all GPUs in the database. Its nearest rival, the NVIDIA B200, scores 345,482, which is 8% higher. The NVIDIA H200 NVL scores 334,891, also ahead by 5%. On the other side, the MI300X leads the NVIDIA L40S (295,763) by 7.5% and the NVIDIA RTX 6000 Ada Generation (287,237) by 10.7%. So the MI300X is not the absolute top scorer among all accelerators, but it sits within a tight cluster at the very top, beating most competitors while trailing only the two highest-scoring NVIDIA parts.

The MI100, by contrast, sits at the 96th percentile — still high, but a different tier. Its nearest rivals are clustered closely: the NVIDIA Tesla V100 PCIe 16 GB (138,063) is only 0.7% behind, the Tesla V100 SXM2 32 GB (137,731) is 0.9% behind, the AMD Radeon PRO V620 (136,472) is 1.9% behind, and the AMD Radeon Pro W6800X Duo (135,774) is 2.4% behind. The MI100 leads all of these, but by margins under 2.5%. This indicates the MI100 competes in a crowded field of older-generation accelerators, while the MI300X sits in a sparser, higher-performance tier.

The delta between the two AMD parts is the largest in either of their nearest-rival sets. No rival on either list comes within 30% of the MI300X's lead over the MI100. The benchmark results indicate a clean generation-on-generation doubling, not a gradual increment.

# Architecture Differences

The MI300X and MI100 represent two distinct generations of AMD's CDNA architecture, with the gap being roughly three full revisions. The MI300X uses CDNA 3.0, while the MI100 uses CDNA 1.0. This architectural jump is reflected in nearly every physical and logical specification.

The process node shrinks from 7 nm (MI100) to 5 nm (MI300X), both fabricated by TSMC. Transistor count is the most dramatic difference: the MI300X packs 153,000 million transistors, versus 25,600 million in the MI100 — a 6x increase. Die size grows from 750 mm² to 1017 mm², but the density improvement is far larger: 150.4 million transistors per mm² on the MI300X versus 34.1 million on the MI100, a 4.4x density gain. This is what a node shrink plus architectural refinement enables.

The chip names differ: the MI300X uses "Aqua Vanjaram," while the MI100 uses "Arcturus." Both belong to the same Instinct (MIx) generation family and share the "Radeon Instinct" predecessor, but the MI100 is marked as end-of-life production status, while the MI300X has no such designation.

Memory architecture is another major divergence. The MI300X has 192 GB of HBM3, while the MI100 has 32 GB of HBM2. Bus width doubles from 4096-bit to 8192-bit. Memory bandwidth jumps from 1.23 TB/s to 5.32 TB/s — a 4.3x increase. Memory clock also rises: the MI300X runs at 1300 MHz (5.2 Gbps effective) versus 1200 MHz (2.4 Gbps effective) on the MI100. The effective data rate more than doubles.

Compute resources scale accordingly. Shading units go from 7680 to 19456 — a 2.5x increase. Texture mapping units rise from 480 to 1216, a 2.5x jump. Raster operations units are a point of difference: the MI100 has 64, while the MI300X lists 0, indicating a different role for the newer part in rendering pipelines. Pixel rate reflects this: the MI100 delivers 96.13 GPixel/s, while the MI300X lists 0 MPixel/s. Texture rate, however, jumps from 721.0 GTexel/s to 2,553.6 GTexel/s.

Clock speeds: both have a 1000 MHz base, but boost differs — 1502 MHz on the MI100 versus 2100 MHz on the MI300X. This 40% higher boost clock contributes to the performance gap alongside the core count increase.

FP32 throughput: the MI300X delivers 81.72 TFLOPS, versus 23.07 TFLOPS on the MI100 — a 3.5x improvement. FP16 output is more nuanced: the MI300X achieves 81.72 TFLOPS at a 1:1 ratio, while the MI100 achieves 46.14 TFLOPS at a 2:1 ratio. This means the MI300X's FP16 rate equals its FP32 rate, whereas the MI100's FP16 is double its FP32 rate — an architectural choice reflecting different compute priorities.

# FAQ

Q: How much faster is the MI300X than the MI100 in the available benchmark?

A: The MI300X scores 317,994 in Geekbench OpenCL, versus 139,035 for the MI100, a 128.7% advantage.

Q: What is the memory capacity difference between the two?

A: The MI300X has 192 GB of HBM3, while the MI100 has 32 GB of HBM2 — a 6x capacity increase.

Q: How do their transistor counts compare?

A: The MI300X has 153,000 million transistors, while the MI100 has 25,600 million — a 6x difference. The MI300X also achieves a higher density at 150.4M per mm² versus 34.1M per mm².

Q: Which accelerator has a higher boost clock?

A: The MI300X boosts to 2100 MHz, while the MI100 boosts to 1502 MHz. Both share the same 1000 MHz base clock.

Q: What is the FP16 performance difference?

A: The MI300X achieves 81.72 TFLOPS FP16 at a 1:1 ratio, while the MI100 achieves 46.14 TFLOPS at a 2:1 ratio. The MI300X is 77% higher in raw FP16 throughput.

Q: How does the MI300X compare to its nearest rivals?

A: The MI300X trails the NVIDIA B200 (345,482) by 8% and the NVIDIA H200 NVL (334,891) by 5%, but leads the NVIDIA L40S (295,763) by 7.5% and the NVIDIA RTX 6000 Ada (287,237) by 10.7%.

# Specification Differences

The following fields differ between the two accelerators:

• Chip: Aqua Vanjaram (MI300X) vs Arcturus (MI100)
• Architecture: CDNA 3.0 vs CDNA 1.0
• Process Node: 5 nm vs 7 nm
• Transistors: 153,000 million vs 25,600 million
• Die Size: 1017 mm² vs 750 mm²
• Transistor Density: 150.4M / mm² vs 34.1M / mm²
• Boost Clock: 2100 MHz vs 1502 MHz
• Memory Clock: 1300 MHz 5.2 Gbps effective vs 1200 MHz 2.4 Gbps effective
• Memory Size: 192 GB vs 32 GB
• Memory Type: HBM3 vs HBM2
• Memory Bus Width: 8192 bit vs 4096 bit
• Memory Bandwidth: 5.32 TB/s vs 1.23 TB/s
• Shading Units: 19456 vs 7680
• TMUs: 1216 vs 480
• ROPs: 0 vs 64
• Pixel Rate: 0 MPixel/s vs 96.13 GPixel/s
• Texture Rate: 2,553.6 GTexel/s vs 721.0 GTexel/s
• FP32: 81.72 TFLOPS vs 23.07 TFLOPS
• FP16: 81.72 TFLOPS (1:1) vs 46.14 TFLOPS (2:1)
• TDP: 750 W vs 300 W
• Slot Width: OAM Module vs Dual-slot
• Power Connectors: None vs 2x 8-pin
• Suggested PSU: 1150 W vs 700 W
• Bus Interface: PCIe 5.0 x16 vs PCIe 4.0 x16
• Dimensions: No length/height listed vs 267 mm length, 111 mm height
• Production Status: Not listed vs End-of-life
• Release Date: 2023-12-05 vs 2020-11-15
• Geekbench OpenCL Score: 317994 vs 139035

# The Verdict

The benchmark data presents a straightforward case. The MI300X is 128.7% faster in the single available test, with a 2.29x raw score advantage. It also holds the 100th percentile ranking, while the MI100 sits at the 96th percentile. If the selection criterion is raw compute performance, the MI300X is the clear choice.

The MI300X's advantages extend across every measurable compute dimension: 6x more transistors, 4.3x more memory bandwidth, 3.5x more FP32 throughput, and 2.5x more shading units. It also offers 6x the memory capacity (192 GB vs 32 GB), which matters for workloads that need to hold large models or datasets on-device.

However, the MI100 is not without relevance. It is end-of-life, but it remains competitive within its own peer group — leading all four of its nearest rivals by margins ranging from 0.7% to 2.4%. For deployments already built around the MI100's 300 W TDP and dual-slot form factor, the upgrade path to the MI300X involves a significant power budget increase (750 W TDP) and a form factor change to OAM Module, which may require infrastructure changes.

The data indicates the MI300X is in a different performance tier, but the MI100 still holds its own against its direct contemporaries. The choice depends on whether the workload demands the MI300X's top-tier performance or can operate within the MI100's proven, lower-power envelope.

# Where Each One Wins

AMD Instinct MI300X wins on:

• Raw compute performance: 128.7% higher Geekbench OpenCL score
• Memory capacity and bandwidth: 192 GB HBM3 at 5.32 TB/s, versus 32 GB HBM2 at 1.23 TB/s
• Compute throughput: 81.72 TFLOPS FP32 and FP16, versus 23.07 FP32 and 46.14 FP16
• Core resources: 19456 shading units and 1216 TMUs, versus 7680 and 480
• Fabrication density: 150.4M transistors per mm² on 5 nm, versus 34.1M on 7 nm
• Boost clock: 2100 MHz versus 1502 MHz
• Modern interface: PCIe 5.0 x16 versus PCIe 4.0 x16
• Relative competitive position: 100th percentile versus 96th

AMD Instinct MI100 wins on:

• Power efficiency: 300 W TDP versus 750 W TDP
• Physical integration: dual-slot, 2x 8-pin power connectors, 267 mm length, versus OAM module with no connectors listed
• Lower system power requirement: 700 W suggested PSU versus 1150 W
• Rasterization capability: 64 ROPs and 96.13 GPixel/s pixel rate, versus 0 ROPs and 0 MPixel/s
• Production status: end-of-life, but this means it is a known, stable platform
• Release maturity: available since 2020, versus 2023 for the MI300X

The MI100's only benchmark win is none — it loses the sole head-to-head test. But its architectural profile shows it was built for a different era of compute, with a focus on density and power that the MI300X trades for absolute performance.