NVIDIA CMP 170HX 8 GB

NVIDIA’s CMP 170HX 8 GB is a niche product built on the GA100 chip, the same silicon that powers data-center accelerators. It is a mining-specific card with no display outputs and a PCIe 1.0 x4 interface, which immediately sets expectations for its role. The benchmark data shows a percentile rank of 50 among all GPUs, indicating it sits exactly at the median of the overall performance distribution. However, this card lacks a benchmark score entry and has no nearest rivals listed, so its positioning must be interpreted through its raw architectural specifications rather than direct comparative testing.

Benchmark Performance

The CMP 170HX delivers 12.63 TFLOPS of FP32 compute, which is a substantial figure for a card of its class. To put this into perspective, this places it in the upper tier of Ampere-based products, though it is not a flagship consumer part. The FP16 performance is rated at 50.53 TFLOPS (4:1), which is four times the FP32 rate, a characteristic of the GA100 design that emphasizes tensor and mixed-precision workloads over traditional gaming rasterization.

With a texture rate of 394.8 GTexel/s and a pixel rate of 180.5 GPixel/s, the card’s throughput is balanced for compute-heavy tasks rather than frame rendering. The 4480 shading units and 280 TMUs are organized in a configuration that favors parallel processing. The 128 ROPs are modest relative to the shading unit count, which further suggests that the card is not optimized for pixel output. In practical terms, the data indicates that this card would excel in workloads that leverage raw FP32 or FP16 math, such as scientific simulation or machine learning inference, but it would not be the first choice for interactive 3D rendering.

The lack of benchmark scores means there is no direct performance percentage to cite against competitors. However, the 50th percentile rank implies that in a hypothetical mixed workload, it would outperform half of all GPUs in the database. This is a meaningful statement: it is not a low-end part, but it is also not in the top quartile of performance. The absence of nearestRivals data prevents a deltaPct comparison, so the analysis must rely on the architectural metrics. The 12.63 TFLOPS figure is the key number here; it is roughly in line with high-end consumer cards from the same era, though the GA100 chip’s design priorities differ significantly.

Ray Tracing and Feature Set

The CMP 170HX has no dedicated RT cores listed in the fact pack. This is a critical omission for any ray tracing workload. The card’s architecture is Ampere, which in other variants includes RT cores, but the data shows a null value for rtCores on this specific SKU. This means the card cannot accelerate ray-traced effects through dedicated hardware. Instead, it relies on 280 tensor cores, which are present and accounted for. These tensor cores are designed for matrix math, which is the foundation of AI-based features like DLSS, but without RT cores, the card is not equipped for hybrid ray tracing pipelines.

The API support is also notably absent: directx, opengl, and vulkan are all listed as null. This indicates that the card does not expose standard graphics APIs, which is consistent with its mining-focused design. It has no display outputs, so it cannot drive a monitor, and the software stack likely does not include graphics drivers that would support Vulkan or DirectX. The PCIe 1.0 x4 interface is another limiting factor for data transfer, but for mining workloads that primarily use the compute units, this is less of a bottleneck. The tensor cores, however, are fully functional and could be used for compute tasks that benefit from FP16 or INT8 precision, though the card’s primary market is clearly hash-based algorithms.

Memory Subsystem

The memory configuration is where the CMP 170HX diverges sharply from consumer cards. It has 8 GB of HBM2e memory on a 4096-bit bus, which yields a bandwidth of 1.49 TB/s. This is an enormous memory bandwidth figure, far exceeding what GDDR6 or GDDR6X offerings typically provide. The bus width of 4096 bits is the largest in the fact pack, and it is the primary reason for the high bandwidth. The memory clock is listed at 1458 MHz, with an effective data rate of 2.9 Gbps, but the sheer width compensates for the relatively modest clock speed.

For high-resolution workloads, this memory subsystem is a double-edged sword. The 8 GB capacity is on the lower side for modern 4K gaming or large dataset processing, but the bandwidth is exceptional. In a mining context, where memory bandwidth often dictates performance for algorithms like Ethash, the 1.49 TB/s is a strong asset. However, for gaming at 4K, the 8 GB VRAM could be a limiting factor in texture-heavy titles, even though the bandwidth would handle data streaming efficiently. The HBM2e type is also known for excellent power efficiency per byte transferred, which aligns with the card’s mining purpose. The 4096-bit bus is a physical design choice that increases the die size and complexity, but it provides a clear advantage in memory-intensive compute tasks.

How It Compares

Since the nearestRivals array is empty, there are no direct competitor scores or deltaPct values to reference. This means the comparison must be framed against the broader GPU landscape using the available data. The percentile rank of 50 places it in the middle of the database, but this is an aggregate measure that includes all GPU generations and types.

Against a typical Ampere consumer card, the CMP 170HX offers higher FP32 throughput per watt at its 250 W TDP, but it lacks the display outputs and RT cores that those cards possess. The 12.63 TFLOPS is competitive, but without a benchmark score, a direct percentage comparison is impossible. The memory bandwidth of 1.49 TB/s is likely superior to most consumer cards, but the 8 GB capacity is smaller than many high-end gaming GPUs from the same period. The lack of API support means it cannot run games at all, which is the most significant differentiator.

In a mining-specific context, the card’s design is straightforward: it uses the GA100 chip’s compute capabilities without the graphics pipeline. The absence of rivals in the fact pack suggests that the database does not classify it against other mining cards, or that its unique positioning makes direct comparison difficult. The 250 W TDP and 600 W suggested PSU indicate a power draw that is manageable for a dual-card setup, but the 2x 8-pin connectors are standard for this class. The production status is end-of-life, which means it is not a current product, but the specifications remain relevant for understanding what it could achieve.

FAQ

Q: What is the launch MSRP of the NVIDIA CMP 170HX 8 GB?

A: The launch MSRP is 4,299 USD, as stated in the fact pack.

Q: Does the CMP 170HX support ray tracing?

A: No, the fact pack lists rtCores as null, and the card has no display outputs, so it cannot render graphics with ray tracing or any other method.

Q: What is the memory bandwidth of this card?

A: The memory bandwidth is 1.49 TB/s, achieved through 8 GB of HBM2e on a 4096-bit bus.

Q: Can this card be used for gaming?

A: No, it has no display outputs and lacks directx, opengl, and vulkan API support, making it unsuitable for gaming.

Q: What is the FP32 performance in TFLOPS?

A: The FP32 performance is 12.63 TFLOPS, with FP16 at 50.53 TFLOPS (4:1).

Q: What power connectors does the card require?

A: It requires 2x 8-pin power connectors, and the suggested PSU is 600 W, with a TDP of 250 W.

Who Should Consider It

The CMP 170HX is not a product for the average PC builder. Its lack of display outputs and graphics API support immediately disqualifies it for any gaming or workstation use that requires a monitor. The data shows a card designed exclusively for compute workloads, specifically mining, where the 12.63 TFLOPS of FP32 and 1.49 TB/s of memory bandwidth are the primary assets. The 8 GB VRAM is sufficient for mining algorithms that fit within that footprint, but it is not expandable for larger datasets.

For users who are building a dedicated mining rig, the card’s 250 W TDP and 600 W PSU requirement are reasonable for a multi-GPU setup, though the PCIe 1.0 x4 interface is a bottleneck for data transfer, which is less critical for mining. The tensor cores could be leveraged for AI inference tasks, but the lack of standard software support makes this a niche application. The 50th percentile ranking suggests it is a mid-tier performer overall, but in its specific mining role, the architecture is tailored for efficiency. The end-of-life status means it is a legacy product, but for those who already have the infrastructure, the specifications indicate it can handle memory-intensive compute tasks at high throughput. The 4,299 USD launch MSRP is a high entry point, but the card’s unique memory subsystem and compute power justify it for specialized buyers. The recommendation is clear: only consider this card if your workflow is purely compute-based and does not require any display output or graphics API compatibility.