NVIDIA Tesla T4G

The NVIDIA Tesla T4G is a Turing-architecture accelerator designed for server deployment, built on a 12 nm process at TSMC with 13,600 million transistors on a 545 mm² die. It is a single-slot, 70 W part with no display outputs and no power connectors, relying entirely on its PCIe 3.0 x16 slot for power, with a suggested system power supply of 250 W. Its production status is end-of-life, having been released on September 12, 2018, positioned between the Tesla Volta and Server Ampere generations. This analysis uses the available data to interpret its benchmark standing, memory configuration, competitive positioning, and feature set.

Benchmark Performance

The Tesla T4G holds a percentile rank of 50 among all GPUs, placing it exactly at the median of the database's performance distribution. Its average benchmark score is recorded as 0, which provides no direct numerical reference point; consequently, its standing is best understood through its percentile placement and architectural characteristics rather than absolute scores. The data shows that this is a mid-pack performer, neither a top-tier compute monster nor a low-end entry point, which aligns with its design as a power-efficient inference and virtualized workstation accelerator.

The compute capabilities are defined by its 2,560 shading units, 160 texture mapping units, and 64 raster output units. The FP32 throughput is 8.141 TFLOPS, a figure that establishes its general-purpose compute ceiling. In contrast, the FP16 performance is 65.13 TFLOPS with an 8:1 ratio, meaning the tensor cores are heavily leveraged for mixed-precision workloads, but the raw FP32 rate is modest by modern standards. The pixel rate is 101.8 GPixel/s, and the texture rate is 254.4 GTexel/s, which are moderate figures that reflect the card's orientation toward throughput in AI inference rather than raw rasterization.

The base clock is 585 MHz, which is notably low, but the boost clock reaches 1,590 MHz, indicating a wide dynamic range that allows the card to scale up significantly under load while maintaining idle efficiency. This clock behavior, combined with the 70 W TDP, suggests that the T4G is designed for sustained operation in dense server environments where thermal and power budgets are constrained. The benchmark results indicate that while it does not lead in raw FLOPS, its efficiency profile is a key attribute, making it a viable option for specific workloads rather than a general-purpose gaming or rendering card.

Memory Subsystem

The memory configuration is a critical strength for the Tesla T4G. It is equipped with 16 GB of GDDR6 memory on a 256-bit bus, yielding a bandwidth of 320.0 GB/s. The memory clock operates at 1,250 MHz, translating to 10 Gbps effective data rate. This capacity and bandwidth combination is substantial for its power envelope, allowing the card to handle large model footprints in inference tasks or substantial frame buffers in virtualized environments.

For high-resolution workloads, the 16 GB capacity is more decisive than the raw bandwidth. At 4K or higher resolutions in a virtual desktop infrastructure (VDI) context, the memory bus width of 256 bits is adequate, but the 320.0 GB/s bandwidth may become a limiting factor compared to higher-end accelerators with wider buses. However, the data suggests that for the T4G's intended use cases—such as AI inference and cloud rendering—the capacity to hold larger datasets in VRAM reduces the need for frequent host-device transfers, which is often more important than peak bandwidth. The lack of display outputs means this memory is not used for direct frame presentation but rather for compute buffers, making the capacity and bandwidth trade-off aligned with server-side processing.

The 10 Gbps effective memory speed is a conservative figure for GDDR6, prioritizing stability and lower power draw over peak throughput. This aligns with the 70 W TDP, as faster memory would increase thermal output. Consequently, the memory subsystem is balanced for sustained, high-density deployment rather than burst performance, and the 16 GB size ensures that multi-tenant workloads can coexist without exhausting VRAM.

How It Compares

The nearest rivals list is empty in the data, which means no direct comparative scores or deltaPct values are available. Therefore, formal comparisons against specific competitor GPUs cannot be quantified. However, its position in the product stack can be inferred from its generation and specifications.

Relative to its predecessor, the Tesla Volta, the T4G introduces Turing architecture features, notably hardware ray tracing cores and enhanced tensor cores, though the data does not provide performance deltas. The shift to GDDR6 from Volta's HBM2 indicates a cost and power optimization, trading bandwidth for capacity and availability.

Compared to its successor, Server Ampere, the T4G is older and likely slower in raw compute, but the data does not offer metrics to substantiate this. The T4G's 50th percentile standing suggests it is roughly average among all GPUs in the database, which positions it below enthusiast-class cards but above entry-level integrated solutions.

Without rival scores, the assessment must rely on architectural merits: the 40 RT cores and 320 tensor cores are present, but their effectiveness is unquantified here. The FP16 ratio of 8:1 indicates that tensor core performance is heavily weighted toward AI workloads, which is a differentiator from general-purpose cards that may have lower FP16 throughput. The 12 nm process, while mature, is less dense than newer nodes, but the 70 W TDP remains a compelling advantage for server density.

Who Should Consider It

The Tesla T4G is not a consumer-oriented card, given its lack of display outputs and server form factor. Its target audience is organizations deploying AI inference at scale, particularly where power efficiency is paramount. The 70 W TDP allows for high-density configurations in servers without extensive cooling or power infrastructure, and the 16 GB memory capacity is sufficient for many natural language processing or computer vision models that do not exceed that footprint.

For high-resolution rendering tasks, the 8.141 TFLOPS FP32 performance is modest, so it is not recommended for heavy 3D rendering or simulation workloads where compute throughput is the primary bottleneck. The data suggests it is better suited for batch inference or light virtualized workloads rather than real-time, high-fidelity graphics. The 101.8 GPixel/s pixel rate and 254.4 GTexel/s texture rate confirm that it is not designed for high-refresh-rate gaming or professional visualization.

The 50th percentile rank indicates that it offers average performance relative to all GPUs, which means it will not excel in compute-heavy tasks but will provide acceptable results for its power draw. Organizations with existing Turing-based software stacks for AI inference will find the T4G compatible, but those seeking leading-edge compute should look elsewhere. It is also an end-of-life product, so procurement should consider long-term availability and support.

Ray Tracing and Feature Set

The Tesla T4G includes 40 ray tracing cores and 320 tensor cores, reflecting its Turing architecture heritage. The presence of RT cores enables hardware-accelerated ray tracing, but the data does not provide specific ray tracing performance metrics, so its efficacy remains qualitative. Given the 8.141 TFLOPS FP32 rate, ray tracing workloads would likely be limited compared to higher-end Turing or newer cards, but the capability is present for applications that require it.

The tensor cores are a major feature, with FP16 performance reaching 65.13 TFLOPS at an 8:1 ratio. This indicates that the card is optimized for AI inference and training acceleration, where tensor core utilization is high. The API support includes DirectX 12 Ultimate (12_2), OpenGL 4.6, and Vulkan 1.4, ensuring compatibility with modern graphics and compute APIs. DirectX 12 Ultimate support confirms feature-level 12_2, which includes variable rate shading and mesh shaders, though these are more relevant for gaming than server workloads.

The feature set is rounded out by PCIe 3.0 x16 connectivity, which is sufficient for data transfer but not as fast as newer PCIe 4.0 or 5.0 interfaces. The card has no display outputs, reinforcing its role as a compute-only accelerator. The 12 nm process and TSMC foundry are standard for the 2018 era, and the transistor density of 25.0M per mm² is a reflection of that node's capabilities. Overall, the T4G's feature set is targeted at AI and virtualized environments, with ray tracing as a secondary capability, and its API support ensures broad software compatibility.