AMD Instinct MI350X vs AMD Radeon Instinct MI300 Comparison

Head-to-Head Benchmarks

The database records no direct head-to-head benchmark results for the AMD Instinct MI350X versus the AMD Radeon Instinct MI300. Both cards have an average benchmark score of 0 and a percentile ranking of 50 among all GPUs, indicating that no standardized performance measurements have been logged for either accelerator in the current dataset. Consequently, the comparison below relies solely on the architectural and specification data captured in the database, not on measured workload outcomes.

The MI350X delivers a FP32 throughput of 72.09 TFLOPS, which is approximately 50.6% higher than the MI300's 47.87 TFLOPS. In FP16 compute, the MI350X sustains 72.09 TFLOPS with a 1:1 ratio, while the MI300 reaches 383.0 TFLOPS using an 8:1 ratio. The MI300's FP16 figure is more than five times the MI350X's FP16 output, but the ratio difference matters: the MI300's 8:1 FP16 mode trades precision for throughput, whereas the MI350X maintains a 1:1 relationship, meaning its FP16 performance is not artificially inflated by reduced precision.

Texture throughput further separates the two. The MI350X achieves 2,252.8 GTexel/s against the MI300's 1,496.0 GTexel/s, a lead of roughly 50.6% that mirrors the FP32 gap. Both cards report 0 MPixel/s pixel rate and 0 ROPs, confirming their compute-first design with no rasterization hardware.

Memory bandwidth is another decisive differentiator. The MI350X accesses 8.19 TB/s from its HBM3e stack, while the MI300 manages 6.55 TB/s from HBM3. That 1.64 TB/s delta represents a 25% bandwidth advantage for the newer part. The MI350X also doubles the memory capacity, offering 288 GB versus 128 GB on the MI300.

The MI350X boosts to 2200 MHz, a 500 MHz increase over the MI300's 1700 MHz boost clock. Base clocks are identical at 1000 MHz, meaning the MI350X's frequency advantage is realized only under load. The MI300's memory runs at 1600 MHz (6.4 Gbps effective), while the MI350X's memory operates at 2000 MHz (8 Gbps effective), a 25% increase in memory clock that aligns with the bandwidth improvement.

The MI350X contains 16,384 shading units, 2,304 more than the MI300's 14,080. Texture mapping units scale similarly: 1,024 on the MI350X versus 880 on the MI300. These counts track the FP32 and texture rate differences, confirming that the MI350X's gains come from both higher clock speeds and wider execution resources.

In the absence of measured benchmarks, the specification sheet favors the MI350X in FP32, memory bandwidth, capacity, texture rate, shading units, and boost clock. The MI300 retains a single advantage in raw FP16 throughput, but only in its reduced-precision 8:1 mode.

FAQ

Q: Which GPU has higher FP32 compute performance?

A: The AMD Instinct MI350X delivers 72.09 TFLOPS FP32, which is 24.22 TFLOPS higher than the AMD Radeon Instinct MI300's 47.87 TFLOPS. The MI350X's FP32 output is approximately 50.6% greater.

Q: How do the memory capacities compare?

A: The MI350X features 288 GB of HBM3e memory, while the MI300 offers 128 GB of HBM3. The MI350X provides 160 GB more capacity, a 125% increase.

Q: What is the memory bandwidth difference?

A: The MI350X reaches 8.19 TB/s, compared to the MI300's 6.55 TB/s. The MI350X holds a 1.64 TB/s lead, which is a 25% bandwidth advantage.

Q: Are the FP16 performance figures directly comparable?

A: No. The MI350X achieves 72.09 TFLOPS FP16 at a 1:1 ratio, while the MI300 achieves 383.0 TFLOPS at an 8:1 ratio. The MI300's higher number reflects a reduced-precision mode, not a direct 1:1 comparison.

Q: How do the boost clocks differ?

A: The MI350X boosts to 2200 MHz, while the MI300 boosts to 1700 MHz. Both have a 1000 MHz base clock. The MI350X's boost clock is 500 MHz higher.

Q: Which GPU has more shading units and texture mapping units?

A: The MI350X has 16,384 shading units and 1,024 TMUs. The MI300 has 14,080 shading units and 880 TMUs. The MI350X leads by 2,304 shading units and 144 TMUs.

Architecture Differences

The MI350X is built on CDNA 4.0 architecture, while the MI300 uses CDNA 3.0. This generation shift brings a change in process technology: the MI350X uses a 3 nm node from TSMC, whereas the MI300 uses a 5 nm node, also from TSMC. The smaller node allows the MI350X to pack 185,000 million transistors into a 2380 mm² die, resulting in a transistor density of 77.7M per mm². The MI300, despite its larger 5 nm process, contains 153,000 million transistors on a 1017 mm² die, achieving a higher density of 150.4M per mm². The MI350X's die is more than twice the physical size of the MI300's, but the MI300's denser packing reflects its older, less complex design with fewer compute units per area.

The MI350X uses a chip labeled "MI350 256CU," while the MI300 uses "Aqua Vanjaram." Both are compute accelerators with no display outputs, no ROPs, and no DirectX, OpenGL, or Vulkan API support. The MI350X's CDNA 4.0 architecture introduces a 1:1 FP16 ratio, meaning its FP16 throughput matches its FP32 throughput. The MI300's CDNA 3.0 architecture uses an 8:1 FP16 ratio, which prioritizes FP16 throughput at the cost of precision. This architectural choice explains why the MI300's FP16 number (383.0 TFLOPS) vastly exceeds its FP32 figure (47.87 TFLOPS), while the MI350X's FP16 and FP32 numbers are identical.

The MI350X's memory subsystem switches to HBM3e, a newer memory type than the MI300's HBM3. Both use an 8192-bit bus width, but the MI350X's higher memory clock (2000 MHz versus 1600 MHz) and newer memory type deliver the bandwidth advantage. The MI350X also has no power connectors, drawing its power through the OAM Module slot, whereas the MI300 uses 2x 8-pin connectors. The MI350X is a 1000 W TDP part, while the MI300 is rated at 600 W TDP. The suggested PSU rating is 1400 W for the MI350X and 1000 W for the MI300.

The MI350X's physical dimensions are 102 mm in length and 165 mm in width, with no height recorded. The MI300 measures 267 mm in length and 111 mm in height, with no width recorded. The MI350X's OAM Module form factor is more compact than the MI300's longer card, which uses standard power connectors.

The MI350X's predecessor is listed as "Radeon Instinct," while the MI300's predecessor is "FirePro Data Center." The MI350X was released on 2025-06-11, and the MI300 on 2023-01-03. The MI350X belongs to the "Instinct (MIx)" generation, while the MI300 belongs to "Radeon Instinct (MIx)."

Specification Differences

| Field | AMD Instinct MI350X | AMD Radeon Instinct MI300 |

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

| Architecture | CDNA 4.0 | CDNA 3.0 |

| Chip | MI350 256CU | Aqua Vanjaram |

| Process Node | 3 nm | 5 nm |

| Transistors | 185,000 million | 153,000 million |

| Die Size | 2380 mm² | 1017 mm² |

| Transistor Density | 77.7M / mm² | 150.4M / mm² |

| Boost Clock | 2200 MHz | 1700 MHz |

| Memory Clock | 2000 MHz (8 Gbps effective) | 1600 MHz (6.4 Gbps effective) |

| Memory Size | 288 GB | 128 GB |

| Memory Type | HBM3e | HBM3 |

| Memory Bandwidth | 8.19 TB/s | 6.55 TB/s |

| Shading Units | 16,384 | 14,080 |

| TMUs | 1,024 | 880 |

| FP32 | 72.09 TFLOPS | 47.87 TFLOPS |

| FP16 | 72.09 TFLOPS (1:1) | 383.0 TFLOPS (8:1) |

| Texture Rate | 2,252.8 GTexel/s | 1,496.0 GTexel/s |

| TDP | 1000 W | 600 W |

| Slot Width | OAM Module | Not specified |

| Power Connectors | None | 2x 8-pin |

| Suggested PSU | 1400 W | 1000 W |

| Length | 102 mm (4 inches) | 267 mm (10.5 inches) |

| Width | 165 mm (6.5 inches) | Not specified |

| Height | Not specified | 111 mm (4.4 inches) |

| Release Date | 2025-06-11 | 2023-01-03 |

| Predecessor | Radeon Instinct | FirePro Data Center |

Fields that are identical or not recorded in the database include base clock (1000 MHz for both), bus width (8192 bit for both), pixel rate (0 MPixel/s for both), ROPs (0 for both), bus interface (PCIe 5.0 x16 for both), display outputs (No outputs for both), and API support (N/A or null for both). The MI350X's launch MSRP is not recorded in the database.

Where Each One Wins

The MI350X wins in every category where the database records a meaningful performance metric except one. For FP32 compute, the MI350X's 72.09 TFLOPS outperforms the MI300's 47.87 TFLOPS by 24.22 TFLOPS, a 50.6% advantage. This makes the MI350X the stronger choice for workloads that rely on single-precision floating-point math, such as scientific simulations, certain AI inference tasks, and general-purpose GPU compute.

Memory-intensive applications strongly favor the MI350X. Its 288 GB HBM3e pool provides more than double the MI300's 128 GB HBM3, enabling larger datasets, bigger model weights, and longer-running workloads without swapping. The 8.19 TB/s bandwidth versus 6.55 TB/s also reduces memory-bound bottlenecks, which matters for large matrix operations and data-parallel workloads.

Texture throughput follows the same pattern. The MI350X's 2,252.8 GTexel/s exceeds the MI300's 1,496.0 GTexel/s, indicating faster data movement through the texture pipeline. Even though neither card has pixel output, this metric reflects the overall execution rate of the shading units.

The MI350X's higher boost clock (2200 MHz versus 1700 MHz) and greater shading unit count (16,384 versus 14,080) provide a structural advantage in any workload that scales with core count or clock frequency. Its 1024 TMUs versus 880 also contribute to the texture rate lead.

The MI300 wins in one specific metric: FP16 throughput. Its 383.0 TFLOPS at an 8:1 ratio far exceeds the MI350X's 72.09 TFLOPS at 1:1. For workloads that can tolerate reduced precision and are specifically optimized for 8:1 FP16 mode, the MI300 offers higher raw FP16 output. However, this comes with a precision trade-off, and the MI350X's 1:1 FP16 capability provides the same throughput as its FP32, which may be preferable for applications requiring consistent precision across data types.

The MI300 also has a lower TDP (600 W versus 1000 W) and a lower suggested PSU (1000 W versus 1400 W), making it the less power-hungry option in the database. It uses 2x 8-pin connectors, while the MI350X relies on the OAM Module slot for power. The MI300's physical dimensions (267 mm length, 111 mm height) differ from the MI350X's (102 mm length, 165 mm width), so chassis compatibility may favor one or the other depending on the system layout.

The MI300's earlier release date (2023-01-03 versus 2025-06-11) means it has a longer production history, but the database does not include production status for either card. The MI350X's CDNA 4.0 architecture and 3 nm process represent a newer generation, while the MI300's CDNA 3.0 on 5 nm is the prior step.

In summary, the MI350X dominates in FP32, memory capacity, bandwidth, texture rate, and core resources. The MI300 holds a single advantage in reduced-precision FP16 throughput and consumes less power. For general compute and memory-heavy tasks, the MI350X is the superior part based on specifications. For specialized FP16 workloads that can leverage 8:1 mode, the MI300 offers a higher raw number, but with the precision caveat.