NVIDIA GeForce RTX 3080 Mobile vs NVIDIA Tesla K40m Comparison
NVIDIA GeForce RTX 3080 Mobile
Tesla K40m
PERFORMANCE BENCHMARKS
Analysis: NVIDIA GeForce RTX 3080 Mobile vs NVIDIA Tesla K40m
FAQ
Q: How does the NVIDIA GeForce RTX 3080 Mobile compare to the Tesla K40m in overall benchmark performance?
A: The RTX 3080 Mobile holds a decisive advantage. The database shows an average benchmark score of 23,628 for the RTX 3080 Mobile versus 19,885 for the Tesla K40m. The RTX 3080 Mobile also sits at the 69th percentile of all GPUs, while the Tesla K40m sits at the 65th.
Q: Which GPU is more power-efficient?
A: The RTX 3080 Mobile has a TDP of 115 W, while the Tesla K40m has a TDP of 245 W. The RTX 3080 Mobile delivers more than 3.7 times the FP32 compute performance (18.98 TFLOPS vs 5.046 TFLOPS) while drawing less than half the power.
Q: What is the memory configuration difference?
A: The RTX 3080 Mobile has 8 GB of GDDR6 memory on a 256-bit bus with 448.0 GB/s bandwidth. The Tesla K40m has 12 GB of GDDR5 memory on a 384-bit bus with 288.4 GB/s bandwidth. Despite the smaller capacity, the RTX 3080 Mobile provides over 55% more memory bandwidth.
Q: Which GPU supports newer graphics APIs?
A: The RTX 3080 Mobile supports DirectX 12 Ultimate (12_2) and Vulkan 1.4. The Tesla K40m supports DirectX 12 (11_1) and Vulkan 1.2.175. The RTX 3080 Mobile also includes hardware ray tracing cores and tensor cores, which the Tesla K40m lacks entirely.
Q: What is the release timeline for these two products?
A: The Tesla K40m was released on 2013-11-21, while the RTX 3080 Mobile was released on 2021-01-11. Both are end-of-life products. The Tesla K40m had a launch MSRP of 7,699 USD; the RTX 3080 Mobile has no recorded launch MSRP in the database.
Q: How does the RTX 3080 Mobile perform relative to its nearest rivals?
A: The RTX 3080 Mobile's average score places it within 1% of the GeForce GTX TITAN Z (23,736, delta -0.5%) and the AMD Radeon RX 9070 (23,877, delta -1%). It is ahead of the RTX 3070 Ti Mobile (23,518, delta 0.5%) and the AMD Radeon AI PRO R9700 (23,315, delta 1.3%).
Architecture Differences
The architectural gap between these two GPUs is vast, reflecting nearly a decade of development. The RTX 3080 Mobile uses the GA104 chip on NVIDIA's Ampere architecture, fabricated on an 8 nm process at Samsung. The Tesla K40m uses the GK110B chip on the older Kepler architecture, built on a 28 nm process at TSMC.
The transistor counts illustrate the generational leap. The RTX 3080 Mobile packs 17,400 million transistors into a 392 mm² die, achieving a transistor density of 44.4M per mm². The Tesla K40m contains 7,080 million transistors on a larger 561 mm² die, with a density of only 12.6M per mm². The newer process allows the RTX 3080 Mobile to nearly double the transistor count while using a smaller physical die.
Compute resources differ fundamentally. The RTX 3080 Mobile has 6,144 shading units, 192 texture mapping units, and 96 raster output units. It also includes 48 dedicated ray tracing cores and 192 tensor cores, enabling hardware-accelerated ray tracing and AI workloads. The Tesla K40m has 2,880 shading units, 240 TMUs, and 48 ROPs, with no ray tracing or tensor core support at all.
Clock speeds favor the newer chip. The RTX 3080 Mobile runs at a base clock of 1110 MHz and boosts to 1545 MHz. The Tesla K40m operates at 745 MHz base and 876 MHz boost. The RTX 3080 Mobile's memory runs at 1750 MHz with 14 Gbps effective data rate, while the Tesla K40m's memory runs at 1502 MHz with 6 Gbps effective.
The API support also diverges. The RTX 3080 Mobile supports DirectX 12 Ultimate (12_2) and Vulkan 1.4. The Tesla K40m is limited to DirectX 12 (11_1) and Vulkan 1.2.175. Both support OpenGL 4.6. The RTX 3080 Mobile uses a PCIe 4.0 x16 interface, while the Tesla K40m uses PCIe 3.0 x16.
The physical form factors differ as well. The Tesla K40m is a dual-slot card measuring 267 mm (10.5 inches) in length, with no display outputs and a suggested PSU of 550 W. The RTX 3080 Mobile is a mobile chip with no fixed slot width, no power connectors listed, and display outputs that are portable-device dependent.
Head-to-Head Benchmarks
The database contains one directly comparable benchmark result between these two GPUs: the Geekbench OpenCL test. The results are not close. The RTX 3080 Mobile scores 104,831, while the Tesla K40m scores 19,885. This represents a 427.2% advantage for the RTX 3080 Mobile, meaning it delivers more than five times the OpenCL performance of the Tesla K40m.
This single result encapsulates the broader performance picture. The RTX 3080 Mobile's FP32 compute rating is 18.98 TFLOPS, compared to 5.046 TFLOPS for the Tesla K40m, a 3.76x gap. The pixel rate tells a similar story: 148.3 GPixel/s for the RTX 3080 Mobile versus 52.56 GPixel/s for the Tesla K40m. The texture rate is 296.6 GTexel/s versus 210.2 GTexel/s, a smaller but still significant margin.
The RTX 3080 Mobile also shows a 1:1 FP16 capability at 18.98 TFLOPS, while the Tesla K40m has no recorded FP16 performance. This makes the newer GPU substantially more capable for mixed-precision workloads common in modern compute and machine learning tasks.
In the recorded head-to-head data, the RTX 3080 Mobile wins 1 benchmark and the Tesla K40m wins 0. The margin in the OpenCL test is so large that no other benchmark comparison is needed to establish a clear hierarchy, though the database also lists broader benchmark results for the RTX 3080 Mobile across multiple DirectX and compute tests.
The Verdict
The data points to one unambiguous conclusion: the RTX 3080 Mobile is the superior GPU in every measurable category. Its average benchmark score is 23,628 versus 19,885 for the Tesla K40m, its compute throughput is nearly four times higher, and its memory bandwidth is more than 55% greater. The 427.2% lead in OpenCL performance is the largest single margin recorded between the two.
The Tesla K40m does retain one advantage: memory capacity. It offers 12 GB versus 8 GB on the RTX 3080 Mobile. For workloads that require holding very large datasets entirely in VRAM, the K40m's larger pool could be relevant. However, its significantly lower bandwidth (288.4 GB/s vs 448.0 GB/s) and older memory technology (GDDR5 vs GDDR6) mean that any capacity advantage comes with a substantial speed penalty.
The Tesla K40m also carries a notable power burden. Its 245 W TDP versus 115 W for the RTX 3080 Mobile means it requires more than double the power budget to deliver far less performance. The RTX 3080 Mobile achieves higher compute density, higher clock speeds, and better efficiency across the board.
The RTX 3080 Mobile is the clear choice for anyone prioritizing performance, efficiency, modern API support, or compute capability. The Tesla K40m is only defensible in niche scenarios where its larger memory capacity is the absolute priority and power consumption is not a concern.
Specification Differences
| Specification | NVIDIA GeForce RTX 3080 Mobile | NVIDIA Tesla K40m |
|---|---|---|
| Chip | GA104 | GK110B |
| Architecture | Ampere | Kepler |
| Process Node | 8 nm | 28 nm |
| Foundry | Samsung | TSMC |
| Transistors | 17,400 million | 7,080 million |
| Die Size | 392 mm² | 561 mm² |
| Transistor Density | 44.4M / mm² | 12.6M / mm² |
| Base Clock | 1110 MHz | 745 MHz |
| Boost Clock | 1545 MHz | 876 MHz |
| Memory Clock | 1750 MHz, 14 Gbps effective | 1502 MHz, 6 Gbps effective |
| Memory Size | 8 GB | 12 GB |
| Memory Type | GDDR6 | GDDR5 |
| Memory Bus Width | 256 bit | 384 bit |
| Memory Bandwidth | 448.0 GB/s | 288.4 GB/s |
| Shading Units | 6144 | 2880 |
| TMUs | 192 | 240 |
| ROPs | 96 | 48 |
| RT Cores | 48 | None |
| Tensor Cores | 192 | None |
| Pixel Rate | 148.3 GPixel/s | 52.56 GPixel/s |
| Texture Rate | 296.6 GTexel/s | 210.2 GTexel/s |
| FP32 Performance | 18.98 TFLOPS | 5.046 TFLOPS |
| FP16 Performance | 18.98 TFLOPS (1:1) | None |
| TDP | 115 W | 245 W |
| Bus Interface | PCIe 4.0 x16 | PCIe 3.0 x16 |
| Display Outputs | Portable Device Dependent | No outputs |
| DirectX Support | 12 Ultimate (12_2) | 12 (11_1) |
| Vulkan Support | 1.4 | 1.2.175 |
| Slot Width | Not specified | Dual-slot |
| Length | Not specified | 267 mm (10.5 inches) |
| Suggested PSU | Not specified | 550 W |
| Release Date | 2021-01-11 | 2013-11-21 |
Where Each One Wins
The RTX 3080 Mobile wins in nearly every practical scenario. Its raw compute advantage, measured at 18.98 TFLOPS FP32 versus 5.046 TFLOPS, makes it the clear choice for gaming, content creation, and general-purpose compute. Its 448.0 GB/s memory bandwidth exceeds the Tesla K40m's 288.4 GB/s, which directly benefits texture-heavy workloads and large data transfers. The 96 ROPs versus 48 ROPs give it a strong edge in pixel fill rate, and the 48 RT cores plus 192 tensor cores enable modern features like ray tracing and AI acceleration that the Kepler architecture simply cannot perform.
The RTX 3080 Mobile also wins on efficiency. Its 115 W TDP versus 245 W means it can deliver superior performance in systems with constrained power budgets, such as laptops or compact desktops. Its PCIe 4.0 interface doubles the transfer bandwidth available to the Tesla K40m's PCIe 3.0 connection, reducing bottlenecks when streaming data from system memory.
The Tesla K40m has exactly one clear advantage: its 12 GB memory capacity. For workloads that require loading more than 8 GB of data into VRAM, such as very large machine learning models or massive scientific datasets, the K40m can hold the entire working set while the RTX 3080 Mobile would need to spill to system memory. Its 384-bit bus width also allows for more memory channels, though the lower clock speed and older GDDR5 technology negate this advantage in raw bandwidth.
The Tesla K40m's dual-slot form factor and 550 W suggested PSU make it a demanding component to install. The RTX 3080 Mobile, with no power connectors listed and portable-device-dependent outputs, is far more flexible in terms of system integration. For any modern workload involving DirectX 12 Ultimate features, Vulkan 1.4, ray tracing, or FP16 computation, the RTX 3080 Mobile is the only viable option. The Tesla K40m remains relevant solely for legacy compute tasks with specific large-memory requirements and no need for modern graphics features.