AMD Instinct MI350P vs NVIDIA N1 20SM Comparison

FAQ

Q: What are the core architectural differences between the AMD Instinct MI350P and the NVIDIA N1 20SM?

A: The AMD Instinct MI350P uses the CDNA 4.0 architecture on a 3 nm TSMC process, while the NVIDIA N1 20SM uses the Blackwell 2.0 architecture on a 5 nm TSMC process. The MI350P is built around the MI350 128CU chip with 73,000 million transistors on a 1190 mm² die, whereas the N1 20SM uses the GB20B chip with an unknown transistor count on a 382 mm² die.

Q: How do the memory subsystems compare?

A: The MI350P features 144 GB of HBM3e memory on a 8192 bit bus with 8.19 TB/s bandwidth. The N1 20SM has 128 GB of LPDDR5X memory on a 256 bit bus with 273.2 GB/s bandwidth. The MI350P delivers substantially higher memory bandwidth, while the N1 20SM uses a lower-power memory type.

Q: What are the raw compute specifications for each device?

A: The MI350P has 8192 shading units, 512 TMUs, and 0 ROPs, delivering 36.04 TFLOPS FP32 and 36.04 TFLOPS FP16 (1:1). The N1 20SM has 2560 shading units, 160 TMUs, and 24 ROPs, delivering 12.01 TFLOPS FP32 and 12.01 TFLOPS FP16 (1:1).

Q: What is the clock speed profile of each device?

A: The MI350P has a base clock of 1000 MHz and a boost clock of 2200 MHz. The N1 20SM has a lower base clock of 741 MHz but a slightly higher boost clock of 2346 MHz.

Q: Do either of these devices support display outputs?

A: The MI350P has no display outputs. The N1 20SM includes 1x HDMI output.

Q: What is the physical form factor of each device?

A: The MI350P is a dual-slot card measuring 267 mm in length, 111 mm in height, and 40 mm in width, requiring a 1x 16-pin power connector and a 1000 W suggested PSU. The N1 20SM is an IGP (integrated graphics processor) with no power connectors and no listed dimensions.

Architecture Differences

The AMD Instinct MI350P and NVIDIA N1 20SM represent fundamentally different design philosophies. The MI350P is a dedicated accelerator built on the CDNA 4.0 architecture, manufactured on a 3 nm TSMC process. Its chip, the MI350 128CU, contains 73,000 million transistors packed into a 1190 mm² die, yielding a transistor density of 61.3M per mm². The N1 20SM uses the Blackwell 2.0 architecture on a 5 nm TSMC process, with its GB20B chip occupying a considerably smaller 382 mm² die. The transistor count for the N1 20SM is not recorded in the database.

The compute pipelines diverge sharply. The MI350P deploys 8192 shading units and 512 TMUs but reports 0 ROPs, which reflects its compute-optimized design. The N1 20SM carries 2560 shading units, 160 TMUs, and 24 ROPs, and additionally includes 20 ray tracing cores and 80 tensor cores. The MI350P lists no ray tracing or tensor core counts, indicating a focus on raw parallel compute rather than graphics or AI acceleration features.

Memory architecture highlights the different target workloads. The MI350P uses 144 GB of HBM3e across an 8192 bit bus, achieving 8.19 TB/s of bandwidth. The N1 20SM uses 128 GB of LPDDR5X across a 256 bit bus, achieving 273.2 GB/s. The MI350P's bandwidth is roughly 30 times higher, which suits memory-intensive compute tasks. The N1 20SM's lower bandwidth aligns with its integrated graphics role.

Clock behavior also differs. The MI350P runs at a 1000 MHz base and 2200 MHz boost. The N1 20SM starts at a 741 MHz base but boosts to 2346 MHz, giving it a higher top clock despite the lower idle speed. Memory clocks show the MI350P at 2000 MHz (8 Gbps effective) versus the N1 20SM at 1067 MHz (8.5 Gbps effective).

The power and physical profiles are opposites. The MI350P is a 600 W dual-slot card with a 1x 16-pin connector and a 1000 W suggested PSU, measuring 267 mm by 111 mm by 40 mm. The N1 20SM is an IGP with no power connectors, no suggested PSU, and no recorded dimensions. The MI350P belongs to the Instinct (MIx) generation with a Radeon Instinct predecessor, while the N1 20SM belongs to the Blackwell IGP (N1x) generation and has no recorded predecessor.

Head-to-Head Benchmarks

The database contains no recorded head-to-head benchmark results between the AMD Instinct MI350P and the NVIDIA N1 20SM. Both devices show zero benchmark entries, zero wins for either side, and no average benchmark scores. The percentile ranking for both is 50 percent against all GPUs, and no nearest rivals are listed for either device. As a result, direct performance comparisons must be derived from the specification data rather than measured results.

The most significant gap appears in FP32 compute. The MI350P delivers 36.04 TFLOPS, which is three times the 12.01 TFLOPS of the N1 20SM. The same ratio applies to FP16 performance, with both devices running at a 1:1 ratio relative to their FP32 figures. The MI350P's shading unit count of 8192 versus 2560 for the N1 20SM reinforces this threefold advantage in raw shader throughput.

Texture processing follows a similar pattern. The MI350P achieves a texture rate of 1,126.4 GTexel/s based on its 512 TMUs, while the N1 20SM reaches 375.4 GTexel/s from its 160 TMUs. This places the MI350P at roughly three times the texture fill rate of the N1 20SM.

The N1 20SM counters in pixel throughput. It records 56.30 GPixel/s from its 24 ROPs, while the MI350P lists 0 MPixel/s because it has no ROPs. This makes the N1 20SM the only one of the two capable of pixel output, which aligns with its HDMI display output and integrated graphics positioning.

Memory bandwidth presents the largest absolute difference. The MI350P's 8.19 TB/s dwarfs the N1 20SM's 273.2 GB/s, a factor of roughly 30. This bandwidth advantage is consistent with the MI350P's HBM3e implementation and 8192 bit bus, versus the N1 20SM's LPDDR5X and 256 bit bus.

Clock speeds tell a mixed story. The N1 20SM has a higher boost clock at 2346 MHz versus 2200 MHz for the MI350P, but the MI350P has a higher base clock at 1000 MHz versus 741 MHz. The N1 20SM also has a higher effective memory clock at 8.5 Gbps versus 8 Gbps for the MI350P, although this does little to close the bandwidth gap.

The Verdict

The recorded data positions the AMD Instinct MI350P as a high-throughput compute accelerator with massive memory bandwidth and three times the FP32 and FP16 throughput of the NVIDIA N1 20SM. Its 144 GB of HBM3e memory, 8192 shading units, and 8.19 TB/s bandwidth make it suited for workloads that demand sustained parallel computation and large data movement. The absence of ROPs, display outputs, and graphics APIs confirms its role as a non-rendering device.

The NVIDIA N1 20SM is an integrated graphics processor with a smaller die, lower compute throughput, and far lower memory bandwidth. Its advantages are confined to pixel processing, with 56.30 GPixel/s from its 24 ROPs, a single HDMI output, and ray tracing and tensor cores that the MI350P does not list. Its higher boost clock of 2346 MHz and lower power footprint, indicated by its IGP form factor with no power connectors, suit a different class of system.

Neither device has measured benchmark data, so the verdict rests on specifications. The MI350P is the clear choice for compute-heavy environments where memory bandwidth and FP32 throughput dominate. The N1 20SM is the only option for systems requiring display output, pixel rendering, or ray tracing capabilities. The two devices do not compete in the same segment, and the data supports selecting based on workload type rather than direct performance equivalence.

Specification Differences

The two devices differ across nearly every recorded specification. The MI350P uses the CDNA 4.0 architecture on a 3 nm process, while the N1 20SM uses Blackwell 2.0 on a 5 nm process. Die size is 1190 mm² for the MI350P versus 382 mm² for the N1 20SM. Transistor count is 73,000 million for the MI350P and unknown for the N1 20SM.

Base clocks are 1000 MHz for the MI350P and 741 MHz for the N1 20SM. Boost clocks are 2200 MHz versus 2346 MHz. Memory clocks are 2000 MHz (8 Gbps effective) versus 1067 MHz (8.5 Gbps effective). Memory size is 144 GB versus 128 GB. Memory type is HBM3e versus LPDDR5X. Bus width is 8192 bit versus 256 bit. Bandwidth is 8.19 TB/s versus 273.2 GB/s.

Shading units are 8192 versus 2560. TMUs are 512 versus 160. ROPs are 0 versus 24. The N1 20SM lists 20 ray tracing cores and 80 tensor cores, while the MI350P lists neither. Pixel rate is 0 MPixel/s versus 56.30 GPixel/s. Texture rate is 1,126.4 GTexel/s versus 375.4 GTexel/s. FP32 is 36.04 TFLOPS versus 12.01 TFLOPS. FP16 is 36.04 TFLOPS versus 12.01 TFLOPS.

TDP is 600 W for the MI350P and unknown for the N1 20SM. Slot width is dual-slot versus IGP. Power connectors are 1x 16-pin versus none. Suggested PSU is 1000 W versus none listed. Both use PCIe 5.0 x16. Display outputs are none versus 1x HDMI. The MI350P measures 267 mm by 111 mm by 40 mm, while the N1 20SM has no recorded dimensions. The MI350P's production status is not recorded, while the N1 20SM is marked active.

Where Each One Wins

The MI350P wins in every compute throughput category. Its FP32 and FP16 output of 36.04 TFLOPS is three times the N1 20SM's 12.01 TFLOPS. Its texture rate of 1,126.4 GTexel/s exceeds the N1 20SM's 375.4 GTexel/s by a factor of three. Its memory bandwidth of 8.19 TB/s is roughly 30 times the N1 20SM's 273.2 GB/s. Its 144 GB memory capacity exceeds the N1 20SM's 128 GB. Its base clock of 1000 MHz is higher than the N1 20SM's 741 MHz. These wins point to workloads in high-performance computing, large-scale data processing, and compute-intensive acceleration where memory bandwidth and shader throughput are the limiting factors.

The N1 20SM wins in pixel processing and integrated features. Its 56.30 GPixel/s pixel rate is the only pixel throughput recorded between the two, since the MI350P lists 0 MPixel/s. Its 24 ROPs enable this output, while the MI350P has none. The N1 20SM's single HDMI output provides display capability that the MI350P lacks entirely. Its 20 ray tracing cores and 80 tensor cores are features absent from the MI350P's recorded specifications. Its boost clock of 2346 MHz is higher than the MI350P's 2200 MHz. Its IGP form factor with no power connectors suggests deployment in systems where discrete power delivery is unavailable. These wins point to use cases involving graphics output, pixel rendering, ray tracing workloads, and AI acceleration through tensor cores, all within an integrated package.