NVIDIA Tesla M2070-Q
NVIDIA graphics card specifications and benchmark scores
At a Glance
NVIDIANVIDIA Tesla M2070-Q Specifications
Tesla M2070-Q GPU Core
Shader units and compute resources
The NVIDIA Tesla M2070-Q 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.
Tesla M2070-Q Clock Speeds
GPU and memory frequencies
Clock speeds directly impact the Tesla M2070-Q'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 Tesla M2070-Q by NVIDIA dynamically adjusts frequencies based on workload, temperature, and power limits to maximize performance while maintaining stability.
NVIDIA's Tesla M2070-Q Memory
VRAM capacity and bandwidth
VRAM (Video RAM) is dedicated memory for storing textures, frame buffers, and shader data. The Tesla M2070-Q'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.
Tesla M2070-Q by NVIDIA Cache
On-chip cache hierarchy
On-chip cache provides ultra-fast data access for the Tesla M2070-Q, 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.
Tesla M2070-Q Theoretical Performance
Compute and fill rates
Theoretical performance metrics provide a baseline for comparing the NVIDIA Tesla M2070-Q 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 Tesla M2070-Q 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 Tesla M2070-Q will perform in GPU benchmarks compared to previous generations.
NVIDIA's Tesla M2070-Q Power & Thermal
TDP and power requirements
Power specifications for the NVIDIA Tesla M2070-Q 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 Tesla M2070-Q to maintain boost clocks without throttling.
Tesla M2070-Q by NVIDIA Physical & Connectivity
Dimensions and outputs
Physical dimensions of the NVIDIA Tesla M2070-Q 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 Tesla M2070-Q. 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.
Tesla M2070-Q Product Information
Release and pricing details
The NVIDIA Tesla M2070-Q 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 Tesla M2070-Q by NVIDIA represents good value at current market prices. Predecessor and successor information aids in tracking generational improvements and planning future upgrades.
Tesla M2070-Q Benchmark Scores
No benchmark data available for this GPU.
About NVIDIA Tesla M2070-Q
The NVIDIA Tesla M2070-Q is a professional-grade compute accelerator built on the Fermi architecture, targeting high-performance computing workloads rather than consumer gaming. With a 40 nm process node from TSMC and 3,100 million transistors packed into a 529 mm² die, this card represents a specific era of GPU design. The M2070-Q is an end-of-life product, released on July 24, 2011, and the data indicates a middling overall standing with a 50th percentile ranking against all GPUs, though its benchmark scores are listed as zero, suggesting it is not evaluated in standard gaming metrics.
Benchmark Performance
The benchmark results for the M2070-Q are notably sparse, with no direct scores or nearest rival data available for comparison. The average benchmark score is recorded as zero, and the percentile rank of 50 indicates that it sits exactly at the midpoint of the GPU performance distribution. This neutral percentile is a direct consequence of the M2070-Q’s design focus: it is not a rasterization-focused card, but a compute-oriented accelerator. The raw computational figures from the FACT PACK tell a clearer story: the card delivers 1,027.7 GFLOPS of FP32 performance, a substantial number for its generation, sourced from 448 shading units operating across 56 texture mapping units and 48 render output units.
The memory subsystem is equally important for compute tasks, and the M2070-Q features 6 GB of GDDR5 memory on a 384-bit bus, yielding a bandwidth of 150.3 GB/s. The memory clock is listed as 783 MHz, translating to 3.1 Gbps effective. These specifications create a balanced profile for double-precision and integer workloads typical of scientific simulation, but they do not translate into competitive gaming scores. Given the lack of benchmark entries, the data suggests that the M2070-Q was never intended to be measured against consumer gaming GPUs, and its 50th percentile is a default placement rather than a performance verdict. In practical terms, the FP32 throughput of over 1 teraflop is the key metric, placing it well ahead of typical mid-range cards of its era in raw compute, but the absence of rival data prevents precise percentage deltas.
How It Compares
Without nearest rival data, the comparison must rely on architectural context from the FACT PACK. The M2070-Q’s predecessor is listed simply as "Tesla," while its successor is "Tesla Kepler," indicating a direct lineage within NVIDIA’s professional lineup. The Fermi architecture, embodied by the GF100 chip, is a first-generation unified design that introduced features like concurrent kernel execution and ECC memory support, both of which are critical for compute reliability. Compared to its successor, Tesla Kepler, the Fermi-based M2070-Q lacks the power efficiency and higher throughput per watt that Kepler later introduced, but the FACT PACK does not provide specific performance figures for either predecessor or successor, so any quantitative delta is unavailable.
The M2070-Q’s 40 nm process node is a key differentiator from later cards, as it limits clock speeds and thermal headroom. In the context of its own generation, the 448 shading units and 48 ROPs are respectable, but the card’s lack of display outputs—listed as "No outputs"—clearly separates it from any consumer GPU. This is a compute-only accelerator, and its comparison to rivals would logically involve other Tesla cards or professional compute GPUs, none of which are present in the nearestRivals array. The data shows that the M2070-Q is a standalone entry in the benchmark database, and its performance positioning must be inferred from its raw specs: the 16.07 GPixel/s pixel rate and 32.14 GTexel/s texture rate are modest by gaming standards, reinforcing that this card’s value lies in FP32 compute, not graphics rendering.
Power and Cooling
The M2070-Q carries a thermal design power (TDP) of 225 W, which is substantial for a dual-slot card. The FACT PACK specifies that the suggested power supply unit is 550 W, meaning a typical desktop build with a mid-range CPU should have sufficient headroom, but a high-end multi-GPU setup would require careful planning. The power connectors are a 1x 6-pin plus 1x 8-pin configuration, which is a common requirement for high-power cards of this era, and it is imperative that the PSU provides both connectors natively to avoid adapter complications. The dual-slot design is standard for a 225 W card, as it allows for a larger heatsink and fan assembly to dissipate the generated heat effectively.
The card’s physical dimensions are 248 mm in length, or 9.8 inches, which is a manageable size for most full-tower cases but may pose a clearance issue in smaller mid-tower chassis. The lack of display outputs means that no monitor can be connected directly, which also eliminates the need for any video output power considerations. Cooling is a critical factor, as the Fermi architecture is known for high heat generation; the dual-slot cooler is presumably adequate for the 225 W TDP, but the data does not specify the cooler type or fan design. For a system builder, the key takeaway is that a 550 W PSU with the correct connectors is mandatory, and the card must be mounted in a case with sufficient airflow to handle the thermal load. The production status is end-of-life, so new units are not available, and used units should be tested for thermal performance under sustained load.
FAQ
Q: What is the launch MSRP of the NVIDIA Tesla M2070-Q?
A: The launch MSRP is 5,489 USD.
Q: What is the memory size and type of the M2070-Q?
A: The card features 6 GB of GDDR5 memory on a 384-bit bus, providing a bandwidth of 150.3 GB/s.
Q: Does the M2070-Q support DirectX 12?
A: Yes, the API support list includes DirectX 12 (11_0), as well as OpenGL 4.6, but Vulkan support is not listed.
Q: What power supply wattage is recommended for this card?
A: The suggested PSU wattage is 550 W, and the card requires a 1x 6-pin and 1x 8-pin power connector configuration.
Q: Can I connect a monitor to the M2070-Q?
A: No, the card has no display outputs, making it strictly a compute accelerator for tasks like scientific simulation or data processing.
Q: What is the FP32 performance of the M2070-Q?
A: The card delivers 1,027.7 GFLOPS of FP32 compute performance, derived from 448 shading units.
Ray Tracing and Feature Set
The M2070-Q does not include dedicated ray tracing cores or tensor cores, as the FACT PACK lists both as null. This is expected for a Fermi-generation card, which predates the introduction of hardware ray tracing acceleration by several years. The feature set is therefore focused on compute and graphics APIs: the card supports DirectX 12 (11_0), which is a feature level rather than full hardware support for the latest DirectX 12 features, and OpenGL 4.6. Vulkan support is not listed, which is notable for a card of this era, as Vulkan was not yet widely adopted at the time of its release.
The lack of RT and tensor cores means that the M2070-Q is unsuitable for modern workloads that rely on these accelerators, such as real-time ray tracing in games or AI inference using tensor operations. However, the card’s 1,027.7 GFLOPS of FP32 performance is relevant for traditional compute tasks, including physics simulations, financial modeling, and scientific research. The Fermi architecture’s strength lies in its double-precision throughput, though the FACT PACK does not list a separate FP64 figure. The pixel rate of 16.07 GPixel/s and texture rate of 32.14 GTexel/s are also present, but these are secondary to the compute capabilities. For a builder considering this card today, the absence of modern feature support is a clear limitation, but the raw compute numbers remain usable for legacy or specialized workloads that do not require RT or tensor functionality.
Who Should Consider It
The M2070-Q is a niche product for specific use cases, not a general-purpose graphics card. Given the absence of benchmark scores and display outputs, it is unsuitable for gaming at any resolution or settings level; the data simply does not support any gaming recommendation. Instead, this card is for users who need raw FP32 compute in a legacy environment. The 1,027.7 GFLOPS of FP32 performance and 6 GB of GDDR5 memory make it viable for compute tasks that are not heavily dependent on memory bandwidth—the 150.3 GB/s figure is modest by modern standards but was adequate for its time. The 50th percentile ranking reinforces that this is a mid-pack performer, not a high-end accelerator.
Specific use cases include running older scientific software that is coded for Fermi architecture, or utilizing the card as a secondary compute node in a cluster where its 225 W TDP and 550 W PSU requirement are manageable. The dual-slot design and 248 mm length are physically compatible with many workstations. However, the end-of-life production status and lack of modern API support (no Vulkan, limited DirectX 12 feature level) mean that it is not a forward-looking investment. Builders with legacy compute workloads that specifically require Fermi compatibility might consider it, but for any modern task, the data indicates that the M2070-Q is outclassed. The recommended resolution and settings are irrelevant here, as the card has no rendering capabilities; the focus is purely on compute throughput. In summary, this is a card for a very narrow professional audience, and even then, only for software that explicitly supports its architecture.
The AMD Equivalent of Tesla M2070-Q
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