NVIDIA Quadro 6000
NVIDIA graphics card specifications and benchmark scores
At a Glance
NVIDIANVIDIA Quadro 6000 Specifications
GPU Core
Shader units and compute resources
The NVIDIA Quadro 6000 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 6000 Clock Speeds
GPU and memory frequencies
Clock speeds directly impact the Quadro 6000'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 6000 by NVIDIA dynamically adjusts frequencies based on workload, temperature, and power limits to maximize performance while maintaining stability.
NVIDIA's Quadro 6000 Memory
VRAM capacity and bandwidth
VRAM (Video RAM) is dedicated memory for storing textures, frame buffers, and shader data. The Quadro 6000'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 6000 by NVIDIA Cache
On-chip cache hierarchy
On-chip cache provides ultra-fast data access for the Quadro 6000, 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 6000 Theoretical Performance
Compute and fill rates
Theoretical performance metrics provide a baseline for comparing the NVIDIA Quadro 6000 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.
Fermi Architecture & Process
Manufacturing and design details
The NVIDIA Quadro 6000 is built on NVIDIA's Fermi 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 6000 will perform in GPU benchmarks compared to previous generations.
Power & Thermal
TDP and power requirements
Power specifications for the NVIDIA Quadro 6000 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 6000 to maintain boost clocks without throttling.
Quadro 6000 by NVIDIA Physical & Connectivity
Dimensions and outputs
Physical dimensions of the NVIDIA Quadro 6000 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 6000. 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 6000 Product Information
Release and pricing details
The NVIDIA Quadro 6000 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 6000 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 6000
Launched in December 2010 and now end-of-life, the NVIDIA Quadro 6000 is a Fermi-generation professional GPU built on TSMC's 40 nm process. It targets workstation tasks with a 6 GB GDDR5 frame buffer and a 384-bit memory interface, positioning it as a legacy option with a Geekbench OpenCL score of 9850, placing it at the 46th percentile among all GPUs. The following analysis examines how this aging card stacks up against its immediate rivals in the benchmark database, what workloads it can still handle, and where its architectural limitations become decisive.
Benchmark Performance
The Quadro 6000’s Geekbench OpenCL score of 9850 places it in a remarkably tight cluster of competitors, with performance deltas of less than 1.5% across all four nearest rivals. Against the NVIDIA Quadro K5100M, the 6000 trails by a negligible 0.1%, meaning the two are effectively identical in raw compute throughput for OpenCL workloads. The AMD Radeon Pro 5300M leads the Quadro 6000 by 0.3%, a margin so small it falls within typical run-to-run variance. Conversely, the Quadro 6000 edges out the NVIDIA Quadro M2000M by 0.2% and beats the AMD Radeon Pro WX 3100 by a more substantial 1.2%. This tight grouping suggests that for general compute tasks, the Quadro 6000 remains competitive with much newer mobile and entry-level workstation parts, despite its 2010 origin.
The FP32 performance of 1,027.7 GFLOPS, combined with a texture rate of 32.14 GTexel/s and a pixel rate of 16.07 GPixel/s, explains why the OpenCL score lands where it does. The 448 shading units operating at the memory clock-derived frequency deliver consistent throughput, but the architecture lacks any FP16 support, which modern workloads increasingly leverage. The 46th percentile ranking indicates the card sits in the lower half of all GPUs tested, though within its specific niche of professional OpenCL compute, it holds its own against direct successors. The data implies the Quadro 6000’s compute capability is not its bottleneck; rather, its age shows in feature support and power efficiency, not raw shader throughput.
Who Should Consider It
Benchmark results indicate the Quadro 6000 is suitable for legacy workstation builds where OpenCL compute is the primary task and modern API features are unnecessary. The 1.2% advantage over the Radeon Pro WX 3100 suggests it can handle similar entry-level compute workloads, such as basic physics simulations or data processing in OpenCL frameworks. For users running software that relies purely on OpenGL 4.6 or DirectX 12 (11_0) — both of which the card supports — the Quadro 6000 remains functional, though its dual-slot design and 248 mm length require a roomy chassis.
At 1080p resolution, the 6 GB VRAM and 143.4 GB/s bandwidth are sufficient for texture-heavy applications, but the 46th percentile score indicates it is not a gaming card. The 48 ROPs cap pixel fill at 16.07 GPixel/s, which limits high-detail rendering at higher resolutions. Users with modest 1080p workloads, particularly those in CAD or scientific visualization that leverage OpenCL, will find the performance acceptable. However, the lack of modern API support (no Vulkan listed) and the absence of ray tracing or tensor cores make it unsuitable for contemporary 3D rendering or AI-accelerated tasks. The data suggests a narrow but real use case: professional environments with fixed software stacks that still rely on Fermi-era compute.
Power and Cooling
The Quadro 6000 carries a TDP of 204 W, a figure that demands serious thermal management. The dual-slot cooler is mandatory given this power draw, and the card’s 248 mm length (9.8 inches) and 111 mm height (4.4 inches) mean it will occupy significant space in a workstation case. Power delivery requires both a 6-pin and an 8-pin PCIe power connector, a configuration that some modern power supplies may not offer if they are older or lower-wattage units. The suggested PSU rating is 550 W, which provides headroom for the card’s peak draw plus a typical CPU and peripherals.
The 40 nm process node and 3,100 million transistors on a 529 mm² die contribute to the high power consumption; transistor density is a modest 5.9M / mm², reflecting the older manufacturing technology. For context, the 204 W TDP is substantial for a card of this performance tier, meaning system builders must prioritize airflow. The PCIe 2.0 x16 interface is adequate for the card’s bandwidth needs, though newer generations offer more headroom. The data shows that power efficiency is not a strength here — the card consumes more power than its nearest rivals likely do, though exact rival TDPs are not in the data. Users with 550 W or higher PSUs and good case ventilation will keep thermals in check, but this is not a silent or low-power solution.
How It Compares
Against the NVIDIA Quadro K5100M, the Quadro 6000 is effectively a tie, trailing by just 0.1% in average OpenCL score. The K5100M is a mobile part, so the comparison highlights how desktop architecture advantages are offset by newer process technology and memory efficiency in the laptop chip. For workstation users, the 0.1% gap is meaningless; either card delivers the same compute throughput, and the choice comes down to form factor and system compatibility.
The NVIDIA Quadro M2000M presents a different story: the Quadro 6000 leads it by 0.2%, a marginal win. The M2000M is a Maxwell-generation mobile GPU, yet the older Fermi card still edges it out in raw OpenCL compute. This suggests that the Quadro 6000’s higher shading unit count (448) and wider memory bus (384-bit) compensate for architectural age, though the M2000M likely offers better efficiency and feature support — facts not in the data but implied by the close scores and generational gap.
AMD’s Radeon Pro 5300M beats the Quadro 6000 by 0.3%, a slim margin that puts the two in the same performance class. The Pro 5300M is a much newer part with modern features, yet the Quadro 6000’s compute density keeps it competitive. For OpenCL-only workflows, this is a realistic alternative, but the Quadro 6000’s lack of Vulkan support and absence of RT/tensor cores make it less future-proof.
The AMD Radeon Pro WX 3100 is the only rival the Quadro 6000 clearly beats, with a 1.2% advantage. The WX 3100 is an entry-level Polaris-based card, and the Quadro 6000’s lead confirms that its high-end Fermi design still outperforms low-end modern parts in compute. However, the WX 3100 likely offers better power characteristics and newer API support, making it a more practical choice for new builds despite the slight compute deficit.
Memory Subsystem
The Quadro 6000’s memory subsystem is its most enduring asset: 6 GB of GDDR5 on a 384-bit bus, delivering 143.4 GB/s of bandwidth. This configuration was generous for 2010 and remains adequate for many workstation tasks today. The memory clock runs at 747 MHz (3 Gbps effective), which is modest by modern standards, but the wide bus compensates, yielding a bandwidth figure that supports high-resolution textures and large datasets without immediate starvation.
For 4K workloads, the 143.4 GB/s bandwidth is a limiting factor — the pixel rate of 16.07 GPixel/s suggests the card cannot sustain high-fill-rate scenarios at ultra-high resolutions. However, the 6 GB capacity means memory capacity is rarely the bottleneck; rather, the bandwidth and compute throughput cap performance. The 384-bit bus also enables efficient multi-GPU configurations, though no SLI or NVLink data is provided. The memory subsystem is balanced for its era, but modern GPUs with similar bandwidth often use faster memory on narrower buses, which changes the power and thermal profile. The data shows a card that can hold large working sets but cannot move data quickly enough for demanding real-time rendering at high resolutions.
FAQ
Q: How does the Quadro 6000 compare to the Quadro K5100M in OpenCL performance?
A: The Quadro 6000 scores 9850, which is 0.1% lower than the K5100M’s 9860 average score. The difference is negligible, making them performance equivalents in OpenCL compute.
Q: What is the card’s memory bandwidth and does it support modern APIs?
A: The memory bandwidth is 143.4 GB/s over a 384-bit GDDR5 interface. The card supports DirectX 12 (11_0) and OpenGL 4.6, but Vulkan is not listed, and it has no RT or tensor cores.
Q: What power supply is recommended for this GPU?
A: The suggested PSU is 550 W, and the card requires both a 6-pin and an 8-pin PCIe power connector. Its TDP is 204 W, so adequate cooling is essential.
Q: Is the Quadro 6000 suitable for modern gaming at high resolutions?
A: No. Its 46th percentile score and lack of modern features limit it to older or lighter titles. The 16.07 GPixel/s pixel rate and 143.4 GB/s bandwidth are insufficient for demanding 1440p or 4K gaming.
Q: What is the release date and production status of this GPU?
A: It was released on December 9, 2010, and is now end-of-life. Its predecessor is the Quadro FX Tesla, and its successor is the Quadro Kepler series.
Q: How many shading units and texture units does it have?
A: It has 448 shading units, 56 texture mapping units, and 48 ROPs, which produce a texture rate of 32.14 GTexel/s and a pixel rate of 16.07 GPixel/s.
Ray Tracing and Feature Set
The Quadro 6000 is a Fermi-generation card, and as such, it has no ray tracing cores or tensor cores — the data lists both as null. This absence is definitive: hardware-accelerated ray tracing and AI-based features like DLSS are entirely unsupported. The feature set is limited to the compute and graphics capabilities of the GF100 chip, which are substantial for OpenCL but obsolete for modern workloads. The card’s API support includes DirectX 12 (11_0), which is a compatibility level rather than full DirectX 12 Ultimate, and OpenGL 4.6, which remains useful for professional CAD and scientific visualization.
The display outputs — 1x DVI, 2x DisplayPort, and 1x S-Video — reflect the 2010 era, with S-Video being particularly dated. The absence of Vulkan support further narrows its modern usability, as many current games and compute frameworks rely on Vulkan. The PCIe 2.0 x16 interface is another legacy limitation, though it does not bottleneck the card’s compute throughput given its age. For users needing pure OpenCL compute without ray tracing or tensor acceleration, the Quadro 6000 remains functional, but its feature set is firmly rooted in the Fermi era. The data shows a card that excels at narrow, specific tasks but cannot adapt to the modern GPU feature landscape.
Detailed benchmark scores and charts for the NVIDIA Quadro 6000 are below.
Benchmark Scores
geekbench_openclSource
Geekbench OpenCL tests GPU compute performance using the cross-platform OpenCL API. This shows how NVIDIA Quadro 6000 handles parallel computing tasks like video encoding and scientific simulations.
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