AMD FirePro S9300 X2

AMD FirePro S9300 X2 is a dual-GPU server accelerator built on the 28 nm GCN 3.0 architecture, featuring the Capsaicin chip with 8,900 million transistors on a 596 mm² die. It occupies the 76th percentile among all GPUs in the database, with an average benchmark score of 32,540 across Geekbench OpenCL and Vulkan tests. The card is end-of-life, launched on March 30, 2016, with a launch MSRP of 5,999 USD, and is positioned as a compute-focused product with no display outputs, targeting server workloads rather than consumer gaming.

Memory Subsystem

The FirePro S9300 X2 pairs 4 GB of HBM memory with a 4096-bit bus width, yielding a bandwidth of 512.0 GB/s. This configuration was unusual for its time, as HBM offered a massive advantage in memory parallelism compared to traditional GDDR5 designs. The memory clock is listed at 500 MHz, with an effective data rate of 1000 Mbps, which is modest per-pin but multiplied across the extremely wide bus to achieve the stated bandwidth.

For high-resolution workloads, the 4 GB capacity is the primary constraint. At 4K or above, frame buffers for textures, geometry, and post-processing effects can easily exceed this limit, causing the GPU to spill into system memory or drop assets. However, the 512.0 GB/s bandwidth ensures that whatever fits within the 4 GB budget is accessed with exceptionally low latency and high throughput. This makes the card well-suited for compute tasks with smaller working sets, such as certain scientific simulations or AI inference models, rather than large-scale rendering scenes.

The 4096-bit bus width is a double-edged sword: it delivers unmatched bandwidth per gigabyte of VRAM, but it also means the memory subsystem is optimized for throughput over capacity. In synthetic benchmarks, this shows up as strong memory-bound scores, but real-world application performance will hinge on whether the workload can be partitioned to fit within 4 GB. For multi-GPU server configurations, this limitation can be mitigated by distributing data across cards, but the single-card analysis clearly indicates that memory capacity, not bandwidth, is the bottleneck for high-resolution graphical tasks.

Who Should Consider It

This card targets server-side compute rather than interactive graphics. The absence of display outputs means it cannot drive a monitor directly, so it is unsuitable for desktop gaming or workstation use where a physical display is required. The data suggests it is intended for rack-mounted systems where rendering, simulation, or machine learning tasks are offloaded to the GPU.

For compute workloads that fit within 4 GB of VRAM, the FirePro S9300 X2 delivers a strong performance profile. Its OpenCL score of 27,971 indicates solid general-purpose compute capability, while the Vulkan score of 37,109 shows particular strength in lower-level API workloads. Users running batch rendering, data processing, or physics simulations that are memory-resident under 4 GB will find the card competitive with modern mid-range GPUs, despite its age.

At high resolutions with large texture sets, the 4 GB limit will cause failures or severe performance degradation. The card is not recommended for 4K gaming, virtual reality, or any workload where a large working set is unavoidable. Instead, it is best suited for high-throughput, low-footprint compute tasks where the 512.0 GB/s bandwidth and 7.987 TFLOPS FP32 performance can be fully utilized without hitting the memory ceiling. Server administrators with existing HBM-aware codebases or those running multi-GPU clusters will see the most value.

Benchmark Performance

The average benchmark score of 32,540 places the FirePro S9300 X2 in a tight cluster with its nearest rivals, all within a 1% margin. The Geekbench OpenCL score of 27,971 is significantly lower than the Vulkan score of 37,109, indicating that the card’s performance is highly API-dependent. This suggests that the GCN 3.0 architecture is more efficient when accessed through modern, low-overhead interfaces like Vulkan, while legacy OpenCL paths leave performance on the table.

Comparing to the AMD Radeon RX 7800 XT, which scores 32,619, the FirePro S9300 X2 trails by only 0.2%. This is a remarkable result for a card from 2016 against a 2023-era GPU, highlighting that raw compute throughput in GCN 3.0 remains competitive in synthetic benchmarks. The 0.2% delta is within measurement noise, meaning the two cards are effectively tied in aggregate performance, despite the RX 7800 XT having far more modern features and memory capacity.

Against the NVIDIA P104-100, which scores 32,747, the FirePro S9300 X2 is 0.6% slower. This is a negligible margin, but it shows that NVIDIA’s mining-oriented card holds a slight edge in raw compute. The NVIDIA T600 Mobile scores 32,849, putting the FirePro S9300 X2 0.9% behind. The T600 Mobile is a low-power laptop GPU, making this closeness surprising and proof of the FirePro’s dual-GPU design efficiency. Across all rivals, the performance spread is under 1%, so the FirePro S9300 X2 should be considered statistically equivalent to these cards in average benchmark terms.

How It Compares

AMD Radeon RX 7800 XT: The FirePro S9300 X2 is 0.2% behind the RX 7800 XT in average score. This near-parity is striking given the generational gap, but the RX 7800 XT offers vastly more VRAM and modern features. In compute-only benchmarks, the old dual-GPU design holds its own, but the RX 7800 XT will dominate in any workload requiring more than 4 GB or leveraging newer instruction sets.

AMD FirePro S10000: The S9300 X2 leads its predecessor by 0.5%, with an average score of 32,540 versus 32,388. This is a modest generational improvement, reflecting the shift from older GCN variants to GCN 3.0 and the adoption of HBM. The small delta suggests that the S10000 was already well-optimized for compute, and the S9300 X2 mainly refines memory bandwidth rather than raw compute.

NVIDIA P104-100: The NVIDIA card is 0.6% faster, scoring 32,747 versus 32,540. The P104-100 is a mining-derived product, so its compute profile is similar, but the FirePro S9300 X2 has the advantage of HBM bandwidth. In practice, the 0.6% delta is trivial, and the deciding factor between these two will be software ecosystem support and driver maturity rather than raw scores.

NVIDIA T600 Mobile: The T600 Mobile leads by 0.9%, scoring 32,849. This is the largest gap among the rivals, but still under 1%. The T600 Mobile achieves this with far lower power consumption, demonstrating that modern efficiency improvements can offset the FirePro’s raw throughput. For server deployments where power is a concern, the T600 Mobile may be a better choice despite the FirePro’s higher peak performance.

Ray Tracing and Feature Set

The FirePro S9300 X2 has no dedicated ray tracing cores or tensor cores, as these are not listed in its specifications. This means ray tracing workloads, if attempted, would be handled entirely by the 4,096 shading units, resulting in poor performance compared to modern GPUs with dedicated RT hardware. The card’s compute capability is rooted in traditional shader-based processing, with 256 TMUs and 64 ROPs providing texture and pixel throughput of 249.6 GTexel/s and 62.40 GPixel/s, respectively.

API support includes DirectX 12 (12_0), OpenGL 4.6, and Vulkan 1.2.170. The Vulkan support is notably strong, as evidenced by the higher Vulkan benchmark score, and this makes the card viable for modern compute APIs that bypass legacy driver overhead. DirectX 12_0 support means it can run current DirectX titles, but the lack of RT and tensor cores will limit it to rasterization-based workloads.

The card supports PCIe 3.0 x16, which is adequate for server integration, though newer PCIe 4.0 or 5.0 interfaces would reduce data transfer bottlenecks. Power requirements are substantial, with a 300 W TDP, dual-slot cooling, and two 8-pin power connectors, plus a suggested PSU of 700 W. The physical dimensions are 267 mm in length and 111 mm in height, making it a standard dual-slot server card. There are no display outputs, reinforcing its compute-only positioning. The FP32 throughput of 7.987 TFLOPS is the headline compute figure, but without FP16 or tensor cores, it is limited to FP32 workloads, which may be less relevant in modern AI applications that favor mixed precision.