NVIDIA Quadro GP100
NVIDIA graphics card specifications and benchmark scores
At a Glance
NVIDIANVIDIA Quadro GP100 Specifications
GPU Core
Shader units and compute resources
The NVIDIA Quadro GP100 GPU core specifications define its raw processing power for graphics and compute workloads. Shading units (also called CUDA cores, stream processors, or execution units depending on manufacturer) handle the parallel calculations required for rendering. TMUs (Texture Mapping Units) process texture data, while ROPs (Render Output Units) handle final pixel output. Higher shader counts generally translate to better GPU benchmark performance, especially in demanding games and 3D applications.
Quadro GP100 Clock Speeds
GPU and memory frequencies
Clock speeds directly impact the Quadro GP100's performance in GPU benchmarks and real-world gaming. The base clock represents the minimum guaranteed frequency, while the boost clock indicates peak performance under optimal thermal conditions. Memory clock speed affects texture loading and frame buffer operations. The Quadro GP100 by NVIDIA dynamically adjusts frequencies based on workload, temperature, and power limits to maximize performance while maintaining stability.
NVIDIA's Quadro GP100 Memory
VRAM capacity and bandwidth
VRAM (Video RAM) is dedicated memory for storing textures, frame buffers, and shader data. The Quadro GP100's memory capacity determines how well it handles high-resolution textures and multiple displays. Memory bandwidth, measured in GB/s, affects how quickly data moves between the GPU and VRAM. Higher bandwidth improves performance in memory-intensive scenarios like 4K gaming. The memory bus width and type (GDDR6, GDDR6X, HBM) significantly influence overall GPU benchmark scores.
Quadro GP100 by NVIDIA Cache
On-chip cache hierarchy
On-chip cache provides ultra-fast data access for the Quadro GP100, reducing the need to fetch data from slower VRAM. L1 and L2 caches store frequently accessed data close to the compute units. AMD's Infinity Cache (L3) dramatically increases effective bandwidth, improving GPU benchmark performance without requiring wider memory buses. Larger cache sizes help maintain high frame rates in memory-bound scenarios and reduce power consumption by minimizing VRAM accesses.
Quadro GP100 Theoretical Performance
Compute and fill rates
Theoretical performance metrics provide a baseline for comparing the NVIDIA Quadro GP100 against other graphics cards. FP32 (single-precision) performance, measured in TFLOPS, indicates compute capability for gaming and general GPU workloads. FP64 (double-precision) matters for scientific computing. Pixel and texture fill rates determine how quickly the GPU can render complex scenes. While real-world GPU benchmark results depend on many factors, these specifications help predict relative performance levels.
Pascal Architecture & Process
Manufacturing and design details
The NVIDIA Quadro GP100 is built on NVIDIA's Pascal architecture, which defines how the GPU processes graphics and compute workloads. The manufacturing process node affects power efficiency, thermal characteristics, and maximum clock speeds. Smaller process nodes pack more transistors into the same die area, enabling higher performance per watt. Understanding the architecture helps predict how the Quadro GP100 will perform in GPU benchmarks compared to previous generations.
Power & Thermal
TDP and power requirements
Power specifications for the NVIDIA Quadro GP100 determine PSU requirements and thermal management needs. TDP (Thermal Design Power) indicates the heat output under typical loads, guiding cooler selection. Power connector requirements ensure adequate power delivery for stable operation during demanding GPU benchmarks. The suggested PSU wattage accounts for the entire system, not just the graphics card. Efficient power delivery enables the Quadro GP100 to maintain boost clocks without throttling.
Quadro GP100 by NVIDIA Physical & Connectivity
Dimensions and outputs
Physical dimensions of the NVIDIA Quadro GP100 are critical for case compatibility. Card length, height, and slot width determine whether it fits in your chassis. The PCIe interface version affects bandwidth for communication with the CPU. Display outputs define monitor connectivity options, with modern cards supporting multiple high-resolution displays simultaneously. Verify these specifications against your case and motherboard before purchasing to ensure a proper fit.
NVIDIA API Support
Graphics and compute APIs
API support determines which games and applications can fully utilize the NVIDIA Quadro GP100. DirectX 12 Ultimate enables advanced features like ray tracing and variable rate shading. Vulkan provides cross-platform graphics capabilities with low-level hardware access. OpenGL remains important for professional applications and older games. CUDA (NVIDIA) and OpenCL enable GPU compute for video editing, 3D rendering, and scientific applications. Higher API versions unlock newer graphical features in GPU benchmarks and games.
Quadro GP100 Product Information
Release and pricing details
The NVIDIA Quadro GP100 is manufactured by NVIDIA as part of their graphics card lineup. Release date and launch pricing provide context for comparing GPU benchmark results with competing products from the same era. Understanding the product lifecycle helps evaluate whether the Quadro GP100 by NVIDIA represents good value at current market prices. Predecessor and successor information aids in tracking generational improvements and planning future upgrades.
About NVIDIA Quadro GP100
The NVIDIA Quadro GP100 is a professional graphics card built on the Pascal architecture, fabricated on a 16 nm process at TSMC. It integrates 15,300 million transistors on a 610 mm² die, yielding a transistor density of 25.1 million per square millimeter. Released on September 30, 2016, it is now end-of-life, positioned between the Quadro Maxwell and Quadro Volta generations. In the benchmark database, it holds a 95th percentile rank among all GPUs, with an average benchmark score of 88,528 in Geekbench OpenCL.
Benchmark Performance
The GP100’s Geekbench OpenCL score of 88,528 places it in the 95th percentile of all GPUs, meaning it outperforms 95% of the database entries. Its nearest rivals reveal a tightly clustered competitive field. Against the AMD Radeon Pro VII, which scores 88,961, the GP100 trails by a mere 0.5%. This delta is within measurement noise, effectively a statistical tie. The NVIDIA RTX A4500 Mobile leads the GP100 by 2.9%, scoring 91,134; that gap reflects a newer mobile architecture with higher efficiency. Conversely, the GP100 holds a 3.1% advantage over the AMD Radeon PRO W7600 (85,851) and a 3.4% lead over the NVIDIA CMP 40HX (85,637). These deltas show that the GP100 sits in the middle of its immediate competitive set, neither dominating nor being dominated.
The raw compute throughput supports this positioning. The FP32 rate is 10.34 TFLOPS, while FP16 reaches 20.69 TFLOPS at a 2:1 ratio. Such half-precision capability is valuable for machine learning inference and scientific workloads, though the absence of dedicated tensor cores means the card relies on general-purpose shaders. The 95th percentile rank underscores that despite its 2016 vintage, the GP100 remains a strong compute performer relative to the entire GPU landscape, even if its closest rivals are within a few percentage points.
Memory Subsystem
The GP100 is equipped with 16 GB of HBM2 memory on a 4096-bit bus, delivering a bandwidth of 732.2 GB/s. The memory clock is 715 MHz, which translates to an effective speed of 1430 Mbps. This configuration is exceptionally wide; the 4096-bit interface is among the largest ever produced, allowing the memory subsystem to feed the 3584 shading units and 224 texture mapping units without contention. For high-resolution workloads, the combination of 16 GB capacity and 732.2 GB/s bandwidth provides ample headroom for large textures, complex geometry, and multi-frame buffers. The pixel rate of 138.5 GPixel/s and texture rate of 323.2 GTexel/s further indicate the card’s ability to sustain high-resolution output. While modern cards may use compression techniques to extend effective bandwidth, the raw figures here are substantial for the era and remain competitive in the database.
Ray Tracing and Feature Set
The GP100 does not include dedicated ray tracing or tensor cores; the specification lists no values for these units. Instead, it relies on the Pascal architecture’s standard compute pipelines. API support includes DirectX 12 (12_1), OpenGL 4.6, and Vulkan 1.3, covering the major graphics and compute interfaces. The shading units number 3584, with 224 TMUs and 96 ROPs. The FP32 throughput of 10.34 TFLOPS and FP16 throughput of 20.69 TFLOPS (2:1 ratio) demonstrate a strong compute focus. The lack of hardware RT cores means real-time ray tracing is not a hardware-accelerated feature, but the card’s compute power can still handle software-based ray tracing tasks. Display outputs include one DVI port and four DisplayPort 1.4a connectors, supporting high refresh rates and multi-monitor configurations.
How It Compares
AMD Radeon Pro VII: The GP100 scores 88,528, which is 0.5% lower than the Radeon Pro VII’s 88,961. This is a negligible difference, effectively a tie in OpenCL compute. Both cards target professional workloads, and the performance parity suggests that either would deliver similar compute throughput.
NVIDIA RTX A4500 Mobile: The RTX A4500 Mobile leads with 91,134, putting the GP100 2.9% behind. This mobile part outperforms the older desktop card, reflecting architectural improvements and possibly higher clock speeds. The gap is modest but consistent, indicating that the A4500 Mobile offers a slight edge in compute-intensive tasks.
AMD Radeon PRO W7600: The GP100 is 3.1% ahead of the W7600’s 85,851. The W7600 is a more recent card, but the GP100’s wider memory bus and higher shading unit count contribute to its lead. The GP100’s advantage is clear, though not overwhelming.
NVIDIA CMP 40HX: The CMP 40HX, a mining-oriented card, scores 85,637, which is 3.4% lower than the GP100. The GP100’s professional feature set and larger memory capacity give it a measurable edge in compute benchmarks, despite the CMP’s similar architecture lineage.
Power and Cooling
The GP100 has a TDP of 235 W. The suggested power supply is 550 W, and the card requires a single 8-pin power connector. It occupies a dual-slot form factor, with dimensions of 267 mm in length and 111 mm in height. The 550 W PSU recommendation provides headroom for the rest of the system, and the single 8-pin connector is standard for this power class. The dual-slot design allows for a capable air cooler, though the exact cooling solution is not specified. The bus interface is PCIe 3.0 x16, which is compatible with most modern motherboards. Given the TDP, the card’s cooling demands are moderate, and the dual-slot footprint ensures adequate heat dissipation.
FAQ
Q: What is the memory configuration of the NVIDIA Quadro GP100?
A: It has 16 GB of HBM2 memory on a 4096-bit bus, with a bandwidth of 732.2 GB/s and an effective memory speed of 1430 Mbps.
Q: Does the GP100 support hardware ray tracing?
A: No, the specification does not list dedicated ray tracing cores. It relies on the Pascal architecture’s compute units for any ray tracing tasks.
Q: What is the FP16 performance of the GP100?
A: The FP16 throughput is 20.69 TFLOPS, which is exactly twice the FP32 rate of 10.34 TFLOPS, indicating a 2:1 ratio.
Q: How does the GP100 compare to the AMD Radeon Pro VII in Geekbench OpenCL?
A: The GP100 scores 88,528, which is 0.5% lower than the Radeon Pro VII’s 88,961.
Q: What are the display outputs on the GP100?
A: It has one DVI port and four DisplayPort 1.4a outputs.
Q: What is the recommended power supply for the GP100?
A: The suggested PSU is 550 W, and the card itself has a TDP of 235 W with a single 8-pin connector.
Detailed benchmark scores and charts for the NVIDIA Quadro GP100 are below.
Benchmark Scores
geekbench_openclSource
Geekbench OpenCL tests GPU compute performance using the cross-platform OpenCL API. This shows how NVIDIA Quadro GP100 handles parallel computing tasks like video encoding and scientific simulations. OpenCL is widely supported across different GPU vendors and platforms. Higher scores benefit applications that leverage GPU acceleration for non-graphics workloads.
Top 5 Performers
Top 5 PerformersNearby Performers
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