AMD Radeon R9 M390X vs NVIDIA Tesla K40m Comparison

AMD Radeon R9 M390X and NVIDIA Tesla K40m represent two very different approaches to GPU computing, separated by nearly two years of release dates and built for distinct purposes. The R9 M390X is a mobile-focused part from AMD’s GCN 3.0 generation, while the Tesla K40m is a dual-slot workstation accelerator built on NVIDIA’s Kepler architecture. The available benchmark data shows the R9 M390X achieving an average score of 20,662 across Geekbench Metal and Vulkan tests, while the Tesla K40m posts a single OpenCL score of 19,885. This places the AMD part at the 66th percentile of all GPUs and the NVIDIA part at the 65th percentile, indicating they are closely matched in overall compute capability despite their architectural differences. The R9 M390X holds a 3.9% advantage in average benchmark score, though the comparison is complicated by the fact that no direct head-to-head benchmark results exist between these two cards.

Head-to-Head Benchmarks

The FACT PACK contains no direct head-to-head benchmark entries, so the comparison must be drawn from their respective individual benchmark scores. The AMD Radeon R9 M390X delivers 24,373 in Geekbench Metal and 16,951 in Geekbench Vulkan, yielding an average of 20,662. The NVIDIA Tesla K40m posts 19,885 in Geekbench OpenCL, which serves as its sole benchmark entry and its average score. When comparing the average scores, the R9 M390X leads by 777 points, which translates to a 3.9% advantage. This is a modest gap, and the percentile rankings reinforce the closeness: the AMD card sits at the 66th percentile while the Tesla sits at the 65th percentile, a difference of just one percentile point.

Looking at nearest rivals provides additional context. The R9 M390X’s closest competitor is the AMD Radeon RX 5600 XT, which scores 20,713 and trails by -0.2%. The Intel Arc A750 comes next at 20,582, with the R9 M390X leading by 0.4%. The Intel Arc B570 scores 20,556, giving the AMD mobile part a 0.5% edge, and the NVIDIA GeForce RTX 3070 Mobile scores 20,534, with the R9 M390X ahead by 0.6%. These deltas are all under one percent, indicating that the R9 M390X sits in an extremely tight competitive cluster where even small benchmark variations could reorder the rankings.

For the Tesla K40m, the nearest rival is the AMD FirePro W7000 at 19,905, which leads the Tesla by -0.1%. The AMD Radeon RX 6650 XT scores 19,765, with the Tesla ahead by 0.6%. The AMD FirePro D300 scores 19,637, giving the Tesla a 1.3% margin, and the NVIDIA Quadro K5200 scores 19,602, with the Tesla leading by 1.4%. These deltas are also small, though the Tesla’s closest rival is essentially tied with it, and its advantage grows only slightly against lower-scoring competitors. The benchmark data shows no clear dominant winner; the R9 M390X has the higher average score, but both cards are surrounded by similarly performing products.

Where Each One Wins

The R9 M390X wins on raw compute in the available benchmark suite. Its Geekbench Metal score of 24,373 is notably higher than the Tesla K40m’s OpenCL score of 19,885, and its Vulkan score of 16,951 is the lower of its two results but still contributes to a higher average. The AMD card’s average of 20,662 exceeds the Tesla’s 19,885 by 777 points, making it the better performer in this specific comparison. The R9 M390X also demonstrates versatility by having two distinct benchmark entries across different APIs, while the Tesla K40m has only one.

The Tesla K40m wins on memory capacity and memory bandwidth. It ships with 12 GB of GDDR5 memory on a 384-bit bus, yielding 288.4 GB/s of bandwidth, compared to the R9 M390X’s 4 GB on a 256-bit bus with 160.0 GB/s. This is a 3x capacity advantage and an 80% bandwidth advantage for the NVIDIA part. For workloads that are memory-bound — such as large dataset processing or compute tasks that exceed 4 GB footprint — the Tesla K40m is the better choice despite its lower benchmark scores. The Tesla also has a higher pixel rate at 52.56 GPixel/s versus 23.14 GPixel/s for the R9 M390X, and a higher texture rate at 210.2 GTexel/s versus 92.54 GTexel/s. These fill-rate advantages suggest the Tesla excels in tasks that are rasterization-heavy or texture-intensive, even though its overall benchmark average is lower.

The R9 M390X wins on power efficiency and physical footprint. It consumes 75 W compared to the Tesla’s 245 W, making it a far more power-frugal option. Its MXM Module slot width is designed for portable devices, whereas the Tesla is a dual-slot card. The R9 M390X also has display outputs (portable device dependent) while the Tesla has no outputs at all, meaning the Tesla requires a separate display adapter for any visual output.

Architecture Differences

The two GPUs come from different architectural generations and design philosophies. The AMD Radeon R9 M390X uses the GCN 3.0 architecture with the Amethyst chip, built on a 28 nm process at TSMC. It integrates 5,000 million transistors on a 366 mm² die, resulting in a transistor density of 13.7M per mm². The NVIDIA Tesla K40m uses the Kepler architecture with the GK110B chip, also on a 28 nm process at TSMC. It packs 7,080 million transistors on a larger 561 mm² die, with a slightly lower transistor density of 12.6M per mm². The Tesla has 41.6% more transistors and a 53.3% larger die area.

The compute resources differ significantly. The R9 M390X has 2,048 shading units, 128 texture mapping units, and 32 ROPs. The Tesla K40m has 2,880 shading units, 240 TMUs, and 48 ROPs. This gives the NVIDIA part 40.6% more shading units, 87.5% more TMUs, and 50% more ROPs. The Tesla’s clock speeds are explicitly listed: 745 MHz base and 876 MHz boost, with memory at 1502 MHz (6 Gbps effective). The R9 M390X does not have base or boost clocks listed, only memory at 1250 MHz (5 Gbps effective). The Tesla’s FP32 throughput is 5.046 TFLOPS versus the R9 M390X’s 2.961 TFLOPS, a 70.4% advantage for NVIDIA. The R9 M390X supports FP16 at 2.961 TFLOPS with a 1:1 ratio to FP32, while the Tesla has no FP16 capability listed.

Memory architectures also diverge. The R9 M390X uses 4 GB of GDDR5 on a 256-bit bus, while the Tesla uses 12 GB of GDDR5 on a 384-bit bus. The Tesla’s memory bandwidth of 288.4 GB/s is 80.3% higher than the R9 M390X’s 160.0 GB/s. API support differs as well: the R9 M390X supports DirectX 12 (12_0), OpenGL 4.6, and Vulkan 1.2.170, while the Tesla supports DirectX 12 (11_1), OpenGL 4.6, and Vulkan 1.2.175. The Tesla has a slightly newer Vulkan version but a lower DirectX feature level. The R9 M390X has no power connectors listed, while the Tesla suggests a 550 W PSU and has a 267 mm (10.5 inches) length.

FAQ

Q: Which GPU has the higher average benchmark score?

A: The AMD Radeon R9 M390X has an average benchmark score of 20,662, which is 3.9% higher than the NVIDIA Tesla K40m’s average of 19,885.

Q: Does the Tesla K40m have any advantage in memory capacity?

A: Yes, the Tesla K40m features 12 GB of GDDR5 memory on a 384-bit bus with 288.4 GB/s bandwidth, while the R9 M390X has 4 GB on a 256-bit bus with 160.0 GB/s. This gives the Tesla a 3x capacity advantage and an 80.3% bandwidth advantage.

Q: What is the power consumption difference?

A: The R9 M390X consumes 75 W, while the Tesla K40m consumes 245 W. The R9 M390X uses 69.4% less power than the Tesla.

Q: Which GPU supports FP16 compute?

A: The AMD Radeon R9 M390X supports FP16 at 2.961 TFLOPS with a 1:1 ratio to FP32. The NVIDIA Tesla K40m has no FP16 capability listed.

Q: How do the shading unit counts compare?

A: The Tesla K40m has 2,880 shading units, while the R9 M390X has 2,048. The Tesla has 40.6% more shading units.

Q: What is the transistor count difference?

A: The Tesla K40m has 7,080 million transistors on a 561 mm² die, while the R9 M390X has 5,000 million transistors on a 366 mm² die. The Tesla has 41.6% more transistors and a 53.3% larger die.

The Verdict

The data presents a clear trade-off between compute density and memory capacity. The AMD Radeon R9 M390X wins on benchmark averages, posting a 3.9% higher score than the Tesla K40m, and it does so with far lower power consumption (75 W versus 245 W). For any workload that relies on the specific API benchmarks tested — Geekbench Metal and Vulkan — the R9 M390X is the better performer. Its FP16 support at a 1:1 ratio to FP32 also gives it a feature that the Tesla lacks entirely.

The NVIDIA Tesla K40m, however, is the superior choice for memory-bound workloads. Its 12 GB of GDDR5 memory and 288.4 GB/s bandwidth dwarf the R9 M390X’s 4 GB and 160.0 GB/s. The Tesla also has higher pixel and texture rates (52.56 GPixel/s and 210.2 GTexel/s versus 23.14 GPixel/s and 92.54 GTexel/s), along with 70.4% higher FP32 throughput at 5.046 TFLOPS. These specifications point to a card that is built for heavy compute and rendering tasks where memory footprint and raw throughput matter more than the specific benchmark scores in this dataset.

Given the R9 M390X’s higher average score, lower power draw, and mobile form factor, it is the more versatile and efficient option for general-purpose computing in portable systems. The Tesla K40m, with its massive memory pool and higher fill rates, is the data-driven choice for server or workstation environments that handle large datasets and require sustained compute throughput. The R9 M390X’s launch MSRP is not listed, so no price comparison can be made; however, the Tesla K40m’s launch MSRP was 7,699 USD. The benchmark percentile rankings (66th for the R9 M390X versus 65th for the Tesla) confirm that these are closely matched cards, and the decision should hinge on whether memory capacity or power efficiency is the higher priority.