AMD FirePro S9300 X2 vs NVIDIA CMP 70HX Comparison

The AMD FirePro S9300 X2 and NVIDIA CMP 70HX are both end-of-life, dual-slot cards with no display outputs, designed for compute rather than gaming. The data shows a clear overall winner in raw benchmark scores, but the architectures and specifications tell very different stories. This analysis breaks down the benchmark results, use cases, and key differences to determine which card fits which workload.

Head-to-Head Benchmarks

The FirePro S9300 X2 wins both recorded benchmark tests, securing a 2-0 victory in the head-to-head comparison. In Geekbench OpenCL, the AMD card scores 27,971 against the CMP 70HX’s 25,135, a margin of 11.3%. That is a decisive lead in a compute API that heavily favors raw throughput and memory bandwidth. The gap narrows in Geekbench Vulkan, where the FirePro scores 37,109 versus 35,817 for the NVIDIA card, a 3.6% advantage. While still a win, the smaller delta suggests the CMP 70HX closes some ground in a more modern, driver-optimized API.

Looking at average benchmark scores, the FirePro S9300 X2 posts 32,540, placing it in the 77th percentile of all GPUs. The CMP 70HX averages 30,476, sitting in the 75th percentile. The 6.8% difference in average scores aligns with the OpenCL result, reinforcing that the AMD card is the stronger performer overall. Interestingly, the FirePro’s nearest rival is the AMD Radeon RX 590 GME with an average score of 32,601, just 0.2% higher, while the Radeon RX 7900 GRE trails by 0.3%. This places the FirePro in the middle of a tight pack of modern and legacy cards. The CMP 70HX’s nearest rival is the NVIDIA Tesla M60 at 30,490, a 0% delta, meaning the two are essentially tied in average score.

The OpenCL win is the headline. An 11.3% lead is substantial for cards in the same performance tier, and it stems from the FirePro’s 512.0 GB/s of memory bandwidth and 4096 shading units. The CMP 70HX counters with 608.3 GB/s bandwidth and 3840 shading units, yet still loses. The Vulkan result is closer, and the 3.6% delta indicates that the NVIDIA card’s Ampere architecture handles the API more efficiently, but it is not enough to overcome the FirePro’s raw compute throughput. In short, the data shows the FirePro S9300 X2 is the faster card, with the largest advantage in OpenCL workloads.

Where Each One Wins

The FirePro S9300 X2 is the clear winner for compute-heavy tasks that rely on OpenCL. Its 11.3% lead in that benchmark makes it the better choice for general-purpose GPU computing, particularly in environments where OpenCL is the primary interface. The 7.987 TFLOPS fp32 performance and 249.6 GTexel/s texture rate provide substantial raw processing power. The 4 GB of HBM memory on a 4096-bit bus delivers 512.0 GB/s bandwidth, which is crucial for memory-bound workloads like data processing or scientific simulations. The 77th percentile ranking and average score of 32,540 place it above many modern cards, including the Radeon RX 7900 GRE, which it beats by 0.3%.

The CMP 70HX, despite losing both benchmarks, has its own strengths. It wins on memory capacity with 8 GB of GDDR6X, double the FirePro’s 4 GB, and offers higher bandwidth at 608.3 GB/s. For workloads that require larger datasets to reside in VRAM, the CMP 70HX is the practical choice. Its 10.71 TFLOPS fp32 and fp16 (1:1) performance are higher than the FirePro’s fp32 figure, indicating better raw compute potential in tasks that leverage fp16. The card also features 30 RT cores and 120 tensor cores, which the FirePro lacks entirely. While the benchmark results do not include ray tracing or tensor tests, the presence of these cores means the CMP 70HX can accelerate AI and deep learning workloads through cuDNN or similar libraries, something the FirePro cannot do. The Vulkan score of 35,817, while 3.6% lower, suggests the card is more competitive in modern graphics APIs, making it a better fit for Vulkan-based compute tasks.

In practical terms, the FirePro wins where memory bandwidth and OpenCL compatibility are paramount. The CMP 70HX wins where capacity, fp16 throughput, and AI-specific features matter more than raw OpenCL scores. For a miner or a researcher running fp16 neural networks, the CMP 70HX is the better tool. For a scientist running OpenCL-based simulations, the FirePro is superior.

FAQ

Q: Which card has a higher average benchmark score?

A: The AMD FirePro S9300 X2 has an average benchmark score of 32,540, which is 6.8% higher than the NVIDIA CMP 70HX’s 30,476.

Q: How much faster is the FirePro in OpenCL?

A: The FirePro S9300 X2 scores 27,971 in Geekbench OpenCL, which is 11.3% higher than the CMP 70HX’s 25,135.

Q: Does the CMP 70HX have any advantages in memory?

A: Yes, the CMP 70HX has 8 GB of GDDR6X memory, double the FirePro’s 4 GB of HBM, and a higher bandwidth of 608.3 GB/s versus 512.0 GB/s.

Q: Which card supports more advanced APIs?

A: The NVIDIA CMP 70HX supports DirectX 12 Ultimate (12_2) and Vulkan 1.4, while the AMD FirePro S9300 X2 supports DirectX 12 (12_0) and Vulkan 1.2.170.

Q: What is the performance gap in Vulkan?

A: The FirePro leads in Geekbench Vulkan with 37,109 points, which is 3.6% higher than the CMP 70HX’s 35,817 points.

Q: Are there any cards with similar performance to the FirePro?

A: The AMD Radeon RX 590 GME is the nearest rival with an average score of 32,601, just 0.2% higher, and the Radeon RX 7900 GRE scores 32,456, which is 0.3% lower.

Specification Differences

The two cards differ significantly in nearly every major specification. The FirePro S9300 X2 uses 28 nm process technology at TSMC, while the CMP 70HX uses 8 nm at Samsung. The FirePro has 8,900 million transistors on a 596 mm² die, resulting in a transistor density of 14.9M / mm². The CMP 70HX packs 17,400 million transistors into a smaller 392 mm² die, achieving a density of 44.4M / mm². This is a massive difference in manufacturing efficiency.

Memory configurations diverge sharply. The FirePro has 4 GB of HBM with a 4096-bit bus and 512.0 GB/s bandwidth, while the CMP 70HX has 8 GB of GDDR6X on a 256-bit bus with 608.3 GB/s bandwidth. Clock speeds also differ: the FirePro’s memory runs at 500 MHz (1000 Mbps effective), while the CMP 70HX’s memory runs at 1188 MHz (19 Gbps effective). The NVIDIA card has defined base and boost clocks of 1365 MHz and 1395 MHz, respectively, while the FirePro lists no base or boost clock.

Compute unit counts favor the FirePro in some areas. The FirePro has 4096 shading units, 256 TMUs, and 64 ROPs, against the CMP 70HX’s 3840 shading units, 120 TMUs, and 64 ROPs. The NVIDIA card adds 30 RT cores and 120 tensor cores, which the FirePro does not have. Pixel rates favor the CMP 70HX at 89.28 GPixel/s versus 62.40 GPixel/s, but texture rates favor the FirePro at 249.6 GTexel/s versus 167.4 GTexel/s. FP32 performance is higher on the CMP 70HX at 10.71 TFLOPS versus 7.987 TFLOPS, and the NVIDIA card also offers fp16 at 10.71 TFLOPS, while the FirePro has no listed fp16 figure.

Power and interface specs differ as well. The FirePro draws 300 W TDP with a suggested PSU of 700 W and uses 2x 8-pin connectors. The CMP 70HX has no listed TDP, but suggests a 200 W PSU and uses a single 12-pin connector. The bus interface is a major difference: the FirePro uses PCIe 3.0 x16, while the CMP 70HX uses PCIe 1.0 x4, which severely limits data transfer to the host. Both are dual-slot cards with no display outputs, and both are 267 mm long, with heights of 111 mm and 112 mm, respectively.

Architecture Differences

The architectural divide is stark. The FirePro S9300 X2 is built on GCN 3.0, a design from 2016, using the Capsaicin chip. It supports DirectX 12 (12_0) and Vulkan 1.2.170. The CMP 70HX is based on the Ampere architecture with the GA104 chip, supporting DirectX 12 Ultimate (12_2) and Vulkan 1.4. This means the NVIDIA card is two generations newer in terms of feature support, including hardware ray tracing and mesh shaders, which the GCN architecture lacks.

The process node difference is significant: 28 nm versus 8 nm. The 8 nm node allows the CMP 70HX to pack double the transistors (17,400 million vs 8,900 million) into a smaller die (392 mm² vs 596 mm²), resulting in a transistor density of 44.4M / mm² versus 14.9M / mm². This is a 3x improvement in density, reflecting the advanced manufacturing process.

Cache and feature differences are not fully specified, but the presence of RT cores and tensor cores on the CMP 70HX is a fundamental architectural advantage for AI and ray tracing workloads. The FirePro’s GCN 3.0 architecture is compute-focused but lacks dedicated hardware for these tasks. The CMP 70HX also has a 1:1 fp16 ratio, meaning it can process fp16 at the same rate as fp32, which is critical for machine learning inference and training. The FirePro has no fp16 specification, indicating it likely uses a slower rate or lacks support entirely.

The memory type difference—HBM versus GDDR6X—also reflects architectural priorities. HBM offers higher bandwidth per watt and a wider bus (4096-bit), but GDDR6X provides higher capacity and density. The CMP 70HX’s PCIe 1.0 x4 interface is a bottleneck, but for mining or compute workloads that keep data on the GPU, it may be sufficient. The FirePro’s PCIe 3.0 x16 is far more flexible for general-purpose use.

The Verdict

The benchmark data is unambiguous: the AMD FirePro S9300 X2 is the faster card, winning both OpenCL and Vulkan tests with an 11.3% and 3.6% lead, respectively. Its average score of 32,540 places it in the 77th percentile, while the CMP 70HX sits at 30,476 in the 75th percentile. If raw OpenCL performance is the sole criterion, the FirePro is the pick.

However, the CMP 70HX has a compelling counterargument. It offers double the memory (8 GB vs 4 GB) and higher bandwidth (608.3 GB/s vs 512.0 GB/s). It has higher fp32 and fp16 compute (10.71 TFLOPS each versus 7.987 TFLOPS fp32). It includes RT and tensor cores, enabling AI and ray tracing workloads that the FirePro cannot handle. The 8 nm process and newer Ampere architecture provide better feature support, including DirectX 12 Ultimate and Vulkan 1.4.

Choose the FirePro S9300 X2 if your workload is OpenCL-centric and benefits from high texture rate (249.6 GTexel/s) and a wider memory bus (4096-bit). It is also the better choice for older software stacks that rely on GCN compatibility. Choose the CMP 70HX if you need more VRAM, fp16 throughput, or AI acceleration via tensor cores. The data shows the FirePro wins the benchmark war, but the CMP 70HX wins the feature war. For a modern AI or machine learning task, the CMP 70HX is the practical choice despite losing the raw score battle.