NVIDIA Tesla M40
NVIDIA graphics card specifications and benchmark scores
NVIDIA Tesla M40 Specifications
Tesla M40 GPU Core
Shader units and compute resources
The NVIDIA Tesla M40 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 M40 Clock Speeds
GPU and memory frequencies
Clock speeds directly impact the Tesla M40'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 M40 by NVIDIA dynamically adjusts frequencies based on workload, temperature, and power limits to maximize performance while maintaining stability.
NVIDIA's Tesla M40 Memory
VRAM capacity and bandwidth
VRAM (Video RAM) is dedicated memory for storing textures, frame buffers, and shader data. The Tesla M40'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 M40 by NVIDIA Cache
On-chip cache hierarchy
On-chip cache provides ultra-fast data access for the Tesla M40, 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 M40 Theoretical Performance
Compute and fill rates
Theoretical performance metrics provide a baseline for comparing the NVIDIA Tesla M40 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.
Maxwell 2.0 Architecture & Process
Manufacturing and design details
The NVIDIA Tesla M40 is built on NVIDIA's Maxwell 2.0 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 M40 will perform in GPU benchmarks compared to previous generations.
NVIDIA's Tesla M40 Power & Thermal
TDP and power requirements
Power specifications for the NVIDIA Tesla M40 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 M40 to maintain boost clocks without throttling.
Tesla M40 by NVIDIA Physical & Connectivity
Dimensions and outputs
Physical dimensions of the NVIDIA Tesla M40 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 M40. 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 M40 Product Information
Release and pricing details
The NVIDIA Tesla M40 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 M40 by NVIDIA represents good value at current market prices. Predecessor and successor information aids in tracking generational improvements and planning future upgrades.
Tesla M40 Benchmark Scores
geekbench_openclSource
Geekbench OpenCL tests GPU compute performance using the cross-platform OpenCL API. This shows how NVIDIA Tesla M40 handles parallel computing tasks like video encoding and scientific simulations. OpenCL is widely supported across different GPU vendors and platforms. Higher scores benefit applications that leverage GPU acceleration for non-graphics workloads.
geekbench_vulkanSource
Geekbench Vulkan tests GPU compute using the modern low-overhead Vulkan API. This shows how NVIDIA Tesla M40 performs with next-generation graphics and compute workloads.
About NVIDIA Tesla M40
The NVIDIA Tesla M40 delivers a solid compute-to-dollar ratio for deep‑learning workloads that demand raw throughput without the premium of newer Ampere cards. With 12 GB of GDDR5 memory and a 948 MHz base clock, its Maxwell 2.0 architecture still punches above its weight in FP32 performance. At a TDP of 250 W, the card fits comfortably in most workstation power budgets, keeping operational costs manageable. Benchmarks show a Vulkan score of 44,602 and an OpenCL score of 39,192, numbers that translate into respectable training times for medium‑sized models. When you factor in the typical resale value of older Tesla GPUs, the effective cost per teraflop can be lower than many contemporary consumer GPUs. This makes the Tesla M40 a compelling choice for budget‑conscious research labs that need stable, PCIe 3.0 x16 connectivity.
In terms of competition, the Tesla M40 sits between the older Tesla K80 and the newer RTX A6000, offering better power efficiency than the K80 while costing a fraction of the A6000. For users focused on longevity, the 28 nm process and mature driver stack mean fewer firmware surprises and longer support windows from NVIDIA. Pairing the card with a high‑bandwidth CPU like an AMD EPYC 7542 and fast NVMe storage can help mitigate the memory bandwidth ceiling of GDDR5. Additionally, a robust cooling solution preferably a dual‑fan or liquid‑cooled bracket will keep the 250 W draw within safe thermal limits for sustained training runs. The NVIDIA Tesla M40’s PCIe 3.0 interface also ensures compatibility with most server motherboards, simplifying upgrades and scaling. Overall, the card remains a cost‑effective workhorse for inference workloads and legacy model pipelines that don’t require the latest Tensor Cores.
- Consider the Tesla K80 for an even lower entry price.
- Upgrade to an RTX A6000 if you need cutting‑edge Tensor Core performance.
- Pair with an AMD EPYC 7542 or Intel Xeon Scalable CPU for balanced throughput.
- Use dual‑fan or liquid‑cooling solutions to maintain optimal temperatures.
- Install NVMe SSDs to reduce data bottlenecks during large dataset training.
The AMD Equivalent of Tesla M40
Looking for a similar graphics card from AMD? The AMD Radeon RX 480 offers comparable performance and features in the AMD lineup.
Popular NVIDIA Tesla M40 Comparisons
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