NVIDIA CMP 30HX vs NVIDIA Quadro GP100 Comparison

FAQ

Q: Which GPU has the higher average benchmark score in the database?

A: The NVIDIA Quadro GP100 records an average benchmark score of 87,445, placing it in the 93rd percentile of all GPUs. The NVIDIA CMP 30HX averages 63,842, which sits in the 89th percentile.

Q: How much faster is the Quadro GP100 in the head-to-head OpenCL test?

A: In the Geekbench OpenCL test, the Quadro GP100 scored 87,445 against the CMP 30HX's 65,199. That is a 34.1% advantage for the Quadro part.

Q: What memory configurations do these cards use?

A: The Quadro GP100 uses 16 GB of HBM2 on a 4096-bit bus, delivering 732.2 GB/s of bandwidth. The CMP 30HX uses 6 GB of GDDR6 on a 192-bit bus, delivering 336.0 GB/s.

Q: Do both cards support the same DirectX version?

A: Yes, both list DirectX 12 (12_1) and OpenGL 4.6 in the database. They differ on Vulkan: the Quadro GP100 supports Vulkan 1.3, while the CMP 30HX supports Vulkan 1.4.

Q: Are these cards still in production?

A: No, both are marked as end-of-life in the database. The Quadro GP100 was released in September 2016, and the CMP 30HX was released in February 2021.

Q: Which card has a higher transistor density despite its smaller die?

A: The Quadro GP100 has 15,300 million transistors on a 610 mm² die, giving a density of 25.1 million per mm². The CMP 30HX has 6,600 million transistors on a 284 mm² die, which is 23.2 million per mm².

Architecture Differences

The two cards come from different NVIDIA architectures and eras. The Quadro GP100 is built on the Pascal architecture using the GP100 chip, fabricated on a 16 nm process at TSMC. The CMP 30HX uses the Turing architecture with the TU116 chip, also made by TSMC but on a 12 nm node. This generational gap shows up in several key specifications.

The Quadro GP100 packs 15,300 million transistors onto a 610 mm² die, a massive silicon area. The CMP 30HX is much smaller: 6,600 million transistors on a 284 mm² die. Despite the larger chip, the Quadro GP100's transistor density of 25.1 million per mm² is only slightly higher than the CMP 30HX's 23.2 million per mm², which reflects the denser 12 nm process used for the Turing part.

Compute resources differ substantially. The Quadro GP100 has 3,584 shading units, 224 texture mapping units, and 96 render output units. The CMP 30HX has 1,408 shading units, 88 TMUs, and 48 ROPs. Neither card includes dedicated ray tracing or tensor cores, so both rely on standard shader compute.

Memory architecture is a major divider. The Quadro GP100 uses 16 GB of HBM2 across a 4096-bit memory bus, which yields a bandwidth of 732.2 GB/s. The CMP 30HX uses 6 GB of GDDR6 on a 192-bit bus, providing 336.0 GB/s. The HBM2 implementation gives the Quadro part over twice the memory bandwidth.

Clock speeds favor the newer card. The CMP 30HX runs at a base of 1530 MHz and boosts to 1785 MHz, while the Quadro GP100 sits lower at 1304 MHz base and 1443 MHz boost. The CMP 30HX also has a faster memory clock at 1750 MHz (14 Gbps effective) compared to the Quadro's 715 MHz (1430 Mbps effective). However, the Quadro's wider bus completely overcomes this clock disadvantage in terms of total bandwidth.

The CMP 30HX has no display outputs, a design choice for mining workloads. The Quadro GP100 provides 1x DVI and 4x DisplayPort 1.4a outputs. The bus interface also differs: the Quadro uses PCIe 3.0 x16, while the CMP 30HX runs on PCIe 1.0 x4, a severely restricted connection.

Power requirements reflect the performance gap. The Quadro GP100 has a TDP of 235 W with a suggested PSU of 550 W, while the CMP 30HX draws 125 W and recommends a 300 W PSU. Both use a single 8-pin power connector and are dual-slot cards.

Where Each One Wins

The Quadro GP100 wins on raw compute throughput. Its FP32 performance is 10.34 TFLOPS, which is more than double the CMP 30HX's 5.027 TFLOPS. The FP16 numbers follow the same pattern: 20.69 TFLOPS for the Quadro versus 10.05 TFLOPS for the CMP part. This makes the Quadro GP100 the clear choice for general compute workloads, scientific simulation, or any task that scales with shader count.

The Quadro GP100 also dominates memory bandwidth with 732.2 GB/s versus 336.0 GB/s. For workloads that are memory-bound, such as large dataset processing or high-resolution rendering, this advantage is significant. The 16 GB frame buffer also allows working with larger models or scenes than the 6 GB on the CMP 30HX.

The CMP 30HX has its own advantages. It draws nearly half the power (125 W versus 235 W), which reduces cooling requirements and operating costs. Its smaller physical footprint (229 mm length versus 267 mm) makes it easier to fit in compact systems. The CMP 30HX also supports Vulkan 1.4, a newer API version than the Quadro's 1.3, which may matter for specific modern applications.

The CMP 30HX was designed for mining operations, as evidenced by its lack of display outputs and its PCIe 1.0 x4 interface, which is sufficient for compute tasks that do not need high host bandwidth. For its intended purpose, the lower power draw and compact size are practical benefits. However, the database shows zero wins for the CMP 30HX in the head-to-head benchmark, meaning the Quadro GP100 took the only recorded test.

Specification Differences

The following specifications differ between the two cards:

• Chip: GP100 (Pascal) versus TU116 (Turing)
• Process node: 16 nm versus 12 nm
• Transistors: 15,300 million versus 6,600 million
• Die size: 610 mm² versus 284 mm²
• Transistor density: 25.1M / mm² versus 23.2M / mm²
• Base clock: 1304 MHz versus 1530 MHz
• Boost clock: 1443 MHz versus 1785 MHz
• Memory clock: 715 MHz (1430 Mbps effective) versus 1750 MHz (14 Gbps effective)
• Memory size: 16 GB versus 6 GB
• Memory type: HBM2 versus GDDR6
• Memory bus width: 4096 bit versus 192 bit
• Memory bandwidth: 732.2 GB/s versus 336.0 GB/s
• Shading units: 3584 versus 1408
• TMUs: 224 versus 88
• ROPs: 96 versus 48
• Pixel rate: 138.5 GPixel/s versus 85.68 GPixel/s
• Texture rate: 323.2 GTexel/s versus 157.1 GTexel/s
• FP32: 10.34 TFLOPS versus 5.027 TFLOPS
• FP16: 20.69 TFLOPS (2:1) versus 10.05 TFLOPS (2:1)
• TDP: 235 W versus 125 W
• Suggested PSU: 550 W versus 300 W
• Bus interface: PCIe 3.0 x16 versus PCIe 1.0 x4
• Display outputs: 1x DVI, 4x DisplayPort 1.4a versus no outputs
• Vulkan support: 1.3 versus 1.4
• Length: 267 mm (10.5 inches) versus 229 mm (9 inches)
• Width: not specified versus 35 mm (1.4 inches)
• Release date: September 2016 versus February 2021

The cards share several traits: both are from NVIDIA, use TSMC as the foundry, have no tensor or RT cores, support DirectX 12 (12_1) and OpenGL 4.6, use a dual-slot form factor, and require a single 8-pin power connector.

Head-to-Head Benchmarks

The database records one head-to-head benchmark between these two cards: Geekbench OpenCL. The Quadro GP100 scored 87,445, while the CMP 30HX scored 65,199. The Quadro GP100 wins by 34.1%, which is the only comparison available in the head-to-head set.

This 34.1% lead is consistent with the theoretical compute difference. The Quadro GP100's FP32 throughput of 10.34 TFLOPS is roughly double the CMP 30HX's 5.027 TFLOPS, so a 34% real-world gap in OpenCL is plausible given memory bandwidth and driver overheads. The Quadro's 732.2 GB/s bandwidth versus 336.0 GB/s also helps in memory-heavy OpenCL workloads.

Looking at the nearest rivals in the database provides context. The Quadro GP100's closest competitor is the AMD Radeon PRO W7600, which scores 87,108, a mere 0.4% difference. The NVIDIA CMP 40HX trails by 2.1% at 85,637. On the other side, the NVIDIA RTX A4500 Mobile scores 91,134, which is 4% higher, and the NVIDIA RTX A4500 scores 91,671, which is 4.6% higher. The Quadro GP100 sits comfortably in the upper-middle tier of the database.

The CMP 30HX's nearest rivals cluster tightly around its score. The AMD Radeon RX 9060 XT LP at 63,830 is effectively identical (0% delta), the AMD Radeon RX 7600M at 63,775 is 0.1% behind, and the AMD Radeon Pro Vega 56 at 63,693 is 0.2% behind. The AMD Radeon Pro WX 9100 at 64,212 is 0.6% ahead. This tight grouping means the CMP 30HX sits in a very competitive performance band, with no large gaps to nearby cards.

The percentile rankings reinforce this picture. The Quadro GP100 at the 93rd percentile is well above the CMP 30HX at the 89th percentile, a meaningful separation in the overall distribution of GPU performance.

The Verdict

The data points to a clear performance hierarchy. The Quadro GP100 wins the only head-to-head benchmark by 34.1%, has more than double the FP32 throughput, and offers over twice the memory bandwidth. For any compute-heavy workload, the Quadro GP100 is the superior choice based on recorded measurements.

The Quadro GP100's 16 GB of HBM2 memory with 732.2 GB/s bandwidth makes it suitable for large datasets, high-resolution textures, or multi-GPU rendering setups. Its 3,584 shading units provide the raw shader throughput needed for demanding OpenCL or CUDA-style workloads. The 93rd percentile ranking places it above the vast majority of GPUs in the database.

The CMP 30HX has a different role. Its 125 W TDP and compact 229 mm length make it an efficient, low-power option for compute tasks that do not require massive memory or shader counts. Its lack of display outputs means it is not intended for desktop use, but the database shows it performs adequately in its class, sitting at the 89th percentile with tight competition around its score.

Users who need maximum compute performance, larger memory capacity, or display connectivity should choose the Quadro GP100. Users who prioritize power efficiency, smaller physical size, or a newer Vulkan API version might consider the CMP 30HX, provided their workload fits within 6 GB of memory and does not require host-side PCIe bandwidth beyond PCIe 1.0 x4.

Given that the CMP 30HX has zero wins in the head-to-head comparison and the Quadro GP100 leads by 34.1% in the recorded test, the recommendation from the database is straightforward: the Quadro GP100 is the stronger card for general compute performance. The CMP 30HX remains a niche product for specific low-power compute use cases where its efficiency advantages matter more than raw throughput.