NVIDIA Tesla X2070

Benchmark Performance

The NVIDIA Tesla X2070 occupies a distinctive middle-ground position in the historical GPU landscape, registering at the 50th percentile among all GPUs tracked in the database. With a computed average benchmark score of zero in the current dataset, performance characterization relies on architectural specifications rather than direct comparative runs. The GF100 chip at its core, built on TSMC's 40 nm process, delivers 1,165.7 GFLOPS of FP32 compute, placing it firmly in the professional compute tier of its era.

The shading array consists of 448 shading units, paired with 56 texture mapping units and 48 raster operation pipelines. This configuration yields a pixel rate of 18.23 GPixel/s and a texture rate of 36.46 GTexel/s. These figures indicate balanced throughput for compute-oriented workloads, where the texture rate notably exceeds what consumer-focused cards of the same generation typically provided. The FP32 throughput of 1,165.7 GFLOPS represents the card's primary compute metric, and the data suggests this was designed for double-precision-adjacent workloads common in scientific simulation, though FP16 capabilities are not specified in the available records.

The absence of nearestRivals data in the benchmark database means direct percentage comparisons against contemporaries cannot be quantified from this fact pack. However, the 50th percentile ranking implies the X2070 sits at the exact median of all tracked GPUs, neither a top-tier performer nor a low-end part. This positioning aligns with its Tesla product family lineage, where compute reliability matters more than raw gaming frame rates. The architectural relationship to the GF100 die, shared with other Fermi-generation parts, suggests a design optimized for sustained throughput rather than burst performance.

Ray Tracing and Feature Set

The Tesla X2070 predates dedicated ray tracing hardware entirely. The fact pack lists no RT cores and no tensor cores, confirming this is a pre-RTX architecture. For ray tracing workloads, the card would rely entirely on compute shaders executing through the 448 shading units, a method that was computationally expensive even in the early 2010s. The DirectX 12 (11_0) API support provides a baseline for modern graphics features, but the 11_0 feature level indicates this GPU cannot leverage the full DirectX 12 Ultimate feature set, including hardware-accelerated ray tracing or mesh shaders.

OpenGL 4.6 support is present, which is noteworthy for a 2011 product, this suggests driver maturity and ongoing compatibility for legacy scientific visualization tools that rely on OpenGL compute or geometry shaders. Vulkan support is absent from the fact pack, meaning the card cannot access modern cross-platform low-overhead graphics APIs. The display output configuration of "No outputs" definitively categorizes this as a compute-only accelerator, not a rendering device. This aligns with the Tesla branding, which historically targeted datacenter and workstation compute nodes where visual output is handled by separate hardware.

The feature set is therefore minimal by modern standards: no tensor operations, no RT acceleration, no display capability. What the card does offer is a mature compute pipeline with 448 shaders and full-rate FP32 execution. For machine learning inference tasks that predate tensor cores, the FP32 throughput would serve as the sole compute resource. The absence of FP16 support in the fact pack further constrains its utility for contemporary AI workloads, which typically require reduced-precision arithmetic for efficiency.

Power and Cooling

Thermal design power is rated at 225 W, a figure that places the Tesla X2070 in the mid-to-high power consumption range for its generation. The suggested power supply is 550 W, which provides a reasonable headroom margin above the card's own TDP. The power connector field reads "None," which is atypical for a 225 W GPU, this suggests the card draws all power through the MXM Module slot interface rather than requiring auxiliary PCIe power connectors. This design choice is consistent with the MXM form factor, where the module connector carries both data and power.

The slot width is specified as "MXM Module," and the bus interface is MXM-B (3.0). This indicates a mobile or embedded form factor, not a standard PCIe add-in card. The MXM-B revision 3.0 interface provides the mechanical and electrical connection for the module. For system integrators, this means the X2070 is not a user-swappable desktop component but rather a soldered or socketed module on a carrier board. The absence of display outputs reinforces the compute-only nature, no power is diverted to display controllers or output drivers.

The 225 W TDP within an MXM form factor is notable, as MXM modules typically operate at lower power envelopes due to thermal constraints in laptops and compact workstations. The fact pack does not specify cooling solutions, so the thermal management approach remains unspecified. The 40 nm process node, with 3,100 million transistors on a 529 mm² die, explains the power draw, this is a large, dense chip by 2011 standards, with a transistor density of 5.9 million transistors per square millimeter. The 225 W figure represents the sustained thermal load that any cooling solution must dissipate, and the 550 W PSU recommendation accounts for the rest of the system's demands.

FAQ

Q: What is the memory capacity and type of the Tesla X2070?

A: The card ships with 6 GB of GDDR5 memory across a 384-bit bus, delivering 177.4 GB/s of bandwidth.

Q: Does the Tesla X2070 support ray tracing?

A: No. The fact pack lists no RT cores or tensor cores, and the DirectX 12 (11_0) feature level does not include hardware-accelerated ray tracing.

Q: What power supply is recommended for this GPU?

A: A 550 W power supply is suggested. The card itself has a 225 W TDP and requires no auxiliary power connectors, drawing power through the MXM module interface.

Q: Can this card be used for display output?

A: No. The display outputs field is marked "No outputs," making this a compute-only accelerator.

Q: What is the manufacturing process and die size?

A: The GF100 chip is fabricated on TSMC's 40 nm process, with a die size of 529 mm² containing 3,100 million transistors.

Q: What APIs are supported?

A: The card supports DirectX 12 (11_0) and OpenGL 4.6. Vulkan support is not listed.

How It Compares

The nearestRivals array in the fact pack is empty, so direct comparative data against specific competing GPUs is unavailable from the provided information. The 50th percentile ranking across all GPUs in the database provides the only positional reference. This places the X2070 exactly at the median of the performance distribution, half of all tracked GPUs perform better, and half perform worse. For a 2011 compute card, this median position reflects its specialized nature: it was not designed for the gaming workloads that dominate most GPU benchmark databases, yet its raw FP32 throughput keeps it from falling into the lower quartiles.

The predecessor is listed as "Tesla" and the successor as "Tesla Kepler," indicating the X2070 belongs to the first-generation Tesla family based on Fermi architecture. The production status is end-of-life, meaning this is a legacy product with no active manufacturing or driver development priority. Compared to its successor in the Tesla Kepler line, the X2070's Fermi architecture lacks the newer Kepler's efficiency improvements, though specific performance deltas are not quantified in the fact pack. The architectural generational leap from Fermi to Kepler typically brought better performance-per-watt, but without benchmark scores for either card, such claims remain qualitative.

Within the broader GPU ecosystem, the X2070's 448 shading units and 1,165.7 GFLOPS FP32 throughput suggest it was positioned below flagship compute parts of its era but above entry-level accelerators. The 6 GB VRAM capacity was generous for 2011, allowing larger datasets to reside on-card. The lack of any direct rival data means the comparison section must rely on the percentile field and architectural context, which collectively paint a picture of a mid-pack compute workhorse that has been superseded by two subsequent Tesla generations.

Memory Subsystem

The memory configuration consists of 6 GB of GDDR5 across a 384-bit memory bus, operating at a memory clock of 924 MHz, which translates to 3.7 Gbps effective data rate. The resulting memory bandwidth is 177.4 GB/s. This bandwidth figure is a critical determinant of compute performance, particularly for memory-bound workloads such as large matrix operations or data analytics. The 384-bit bus width provides a wide data path that, combined with the GDDR5 signaling, achieves the 177.4 GB/s throughput.

For high-resolution workloads, the 6 GB capacity is the more salient feature than raw bandwidth. In 2011, 6 GB was an unusually large framebuffer, exceeding what most consumer GPUs offered. For compute applications, this capacity allows large datasets to be resident on the card, avoiding costly PCIe transfers. The 384-bit bus width ensures that the shading units can be fed with data efficiently, while the 48 ROPs handle memory write operations. The pixel rate of 18.23 GPixel/s and texture rate of 36.46 GTexel/s are consistent with the memory bandwidth, the card can sustain these rates without memory starvation in most workloads.

The memory clock of 924 MHz (3.7 Gbps effective) is modest by modern standards, but the wide bus compensates. The 177.4 GB/s bandwidth places this card in a specific performance envelope: sufficient for compute kernels that access memory sequentially, but potentially limiting for random-access patterns. The 6 GB capacity, however, provides a significant advantage for out-of-core processing, where the entire working set fits on the card. For high-resolution rendering (if such a task were possible given the lack of display outputs), the memory subsystem would support large textures and geometry buffers without swapping. The combination of 6 GB capacity and 177.4 GB/s bandwidth defines the X2070's compute ceiling, memory-bound tasks will hit the bandwidth limit before the 1,165.7 GFLOPS compute limit becomes the bottleneck.