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NVIDIA Tesla T4G

NVIDIA graphics card specifications and benchmark scores

16 GB
VRAM
1590
MHz Boost
70W
TDP
256
Bus Width
Ray Tracing 🤖Tensor Cores

NVIDIA Tesla T4G Specifications

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Tesla T4G GPU Core

Shader units and compute resources

The NVIDIA Tesla T4G 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.

Shading Units
2,560
Shaders
2,560
TMUs
160
ROPs
64
SM Count
40
⏱️

Tesla T4G Clock Speeds

GPU and memory frequencies

Clock speeds directly impact the Tesla T4G'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 T4G by NVIDIA dynamically adjusts frequencies based on workload, temperature, and power limits to maximize performance while maintaining stability.

Base Clock
585 MHz
Base Clock
585 MHz
Boost Clock
1590 MHz
Boost Clock
1,590 MHz
Memory Clock
1250 MHz 10 Gbps effective
GDDR GDDR 6X 6X

NVIDIA's Tesla T4G Memory

VRAM capacity and bandwidth

VRAM (Video RAM) is dedicated memory for storing textures, frame buffers, and shader data. The Tesla T4G'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.

Memory Size
16 GB
VRAM
16,384 MB
Memory Type
GDDR6
VRAM Type
GDDR6
Memory Bus
256 bit
Bus Width
256-bit
Bandwidth
320.0 GB/s
💾

Tesla T4G by NVIDIA Cache

On-chip cache hierarchy

On-chip cache provides ultra-fast data access for the Tesla T4G, 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.

L1 Cache
64 KB (per SM)
L2 Cache
4 MB
📈

Tesla T4G Theoretical Performance

Compute and fill rates

Theoretical performance metrics provide a baseline for comparing the NVIDIA Tesla T4G 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.

FP32 (Float)
8.141 TFLOPS
FP64 (Double)
254.4 GFLOPS (1:32)
FP16 (Half)
65.13 TFLOPS (8:1)
Pixel Rate
101.8 GPixel/s
Texture Rate
254.4 GTexel/s

Tesla T4G Ray Tracing & AI

Hardware acceleration features

The NVIDIA Tesla T4G includes dedicated hardware for ray tracing and AI acceleration. RT cores handle real-time ray tracing calculations for realistic lighting, reflections, and shadows in supported games. Tensor cores (NVIDIA) or XMX cores (Intel) accelerate AI workloads including DLSS, FSR, and XeSS upscaling technologies. These features enable higher visual quality without proportional performance costs, making the Tesla T4G capable of delivering both stunning graphics and smooth frame rates in modern titles.

RT Cores
40
Tensor Cores
320
🏗️

Turing Architecture & Process

Manufacturing and design details

The NVIDIA Tesla T4G is built on NVIDIA's Turing 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 T4G will perform in GPU benchmarks compared to previous generations.

Architecture
Turing
GPU Name
TU104
Process Node
12 nm
Foundry
TSMC
Transistors
13,600 million
Die Size
545 mm²
Density
25.0M / mm²
🔌

NVIDIA's Tesla T4G Power & Thermal

TDP and power requirements

Power specifications for the NVIDIA Tesla T4G 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 T4G to maintain boost clocks without throttling.

TDP
70 W
TDP
70W
Power Connectors
None
Suggested PSU
250 W
📐

Tesla T4G by NVIDIA Physical & Connectivity

Dimensions and outputs

Physical dimensions of the NVIDIA Tesla T4G 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.

Slot Width
Single-slot
Length
168 mm 6.6 inches
Bus Interface
PCIe 3.0 x16
Display Outputs
No outputs
Display Outputs
No outputs
🎮

NVIDIA API Support

Graphics and compute APIs

API support determines which games and applications can fully utilize the NVIDIA Tesla T4G. 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.

DirectX
12 Ultimate (12_2)
DirectX
12 Ultimate (12_2)
OpenGL
4.6
OpenGL
4.6
Vulkan
1.4
Vulkan
1.4
OpenCL
3.0
CUDA
7.5
Shader Model
6.8
📦

Tesla T4G Product Information

Release and pricing details

The NVIDIA Tesla T4G 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 T4G by NVIDIA represents good value at current market prices. Predecessor and successor information aids in tracking generational improvements and planning future upgrades.

Manufacturer
NVIDIA
Release Date
Sep 2018
Production
End-of-life
Predecessor
Tesla Volta
Successor
Server Ampere

Tesla T4G Benchmark Scores

📊

No benchmark data available for this GPU.

About NVIDIA Tesla T4G

Yo, the NVIDIA Tesla T4G straight-up dominates professional workloads like AI inference and machine learning tasks with its Turing architecture firing on all cylinders at 1590 MHz boost. That 16 GB GDDR6 VRAM lets you load massive models without breaking a sweat, perfect for data centers crunching numbers 24/7. Lowkey efficient at just 70W TDP, it sips power while delivering tensor core magic for HPC sims. Content creators can vibe with it for GPU-accelerated video encoding or 3D rendering in apps like Blender, though it's not a full-on creative beast. PCIe 3.0 x16 interface slots right into workstation racks for seamless deployment. Released back in 2018, this card still holds up in enterprise setups pushing boundaries. Overall, it's a solid pick if you're scaling inference pipelines without the power bill drama.

The T4G from NVIDIA boasts rock-solid driver support through enterprise-grade NVIDIA software, ensuring stability in production environments no crashes during long renders or training sessions. Multi-GPU setups? It scales like a champ in server configs via PCIe, letting you link multiples for distributed computing without NVLink drama. That 12 nm process keeps thermals chill, ideal for dense racks. For content creation suitability, pair it with CUDA-optimized tools for faster exports, but expect it to shine more in batch processing than real-time edits. Driver updates keep it fresh with Turing optimizations, low bug rates for pros. NVIDIA's Tesla T4G accelerator handles mixed workloads effortlessly, from virtual desktops to edge AI. Pro tip: stack 'em for 100+ TFLOPS in clusters, but watch PCIe bandwidth in older boards.

  • 16 GB GDDR6 VRAM for heavy datasets
  • 70W TDP for energy-efficient deploys
  • Turing tensor cores crush ML inference
  • PCIe 3.0 x16 for easy workstation integration
  • Enterprise drivers = zero-downtime stability

The AMD Equivalent of Tesla T4G

Looking for a similar graphics card from AMD? The AMD Radeon RX 580 2048SP offers comparable performance and features in the AMD lineup.

AMD Radeon RX 580 2048SP

AMD • 4 GB VRAM

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