NVIDIA Tesla K80 vs NVIDIA Tesla M4 Comparison

FAQ

Q: Which GPU wins in the Geekbench OpenCL benchmark?

A: The NVIDIA Tesla K80 wins with a score of 18620, while the NVIDIA Tesla M4 scores 16932. The K80 is 10% ahead in this test.

Q: How does the Tesla K80 compare to its nearest rivals in average benchmark score?

A: The K80 has an average score of 18866, placing it 0.4% behind the NVIDIA GeForce RTX 2070 (18789), and 0.5% ahead of the NVIDIA RTX 2000 Ada Generation (18954). It also trails the NVIDIA Quadro K6000 (19030) and AMD Radeon RX 6600 (19036) by 0.9% each.

Q: What is the average benchmark score for the Tesla M4, and where does it rank?

A: The M4 has an average score of 16932, which puts it 0.5% behind the AMD Radeon HD 7970M (17019) and 0.6% behind the NVIDIA GeForce GTX 690 (17037). It is 0.8% ahead of the NVIDIA T400 4 GB (16792) and 0.9% behind the AMD Radeon RX 7600 XT (17083).

Q: What are the memory capacities and bus widths of these two cards?

A: The Tesla K80 has 12 GB of GDDR5 memory on a 384-bit bus, while the Tesla M4 has 4 GB of GDDR5 memory on a 128-bit bus.

Q: What is the TDP difference between the two accelerators?

A: The Tesla K80 has a TDP of 300 W, while the Tesla M4 is rated at 50 W. The M4 requires a suggested power supply of 250 W, whereas the K80 suggests a 700 W unit.

Q: Which card is newer in terms of release date?

A: The Tesla K80 was released on November 16, 2014, and the Tesla M4 followed almost exactly a year later on November 9, 2015.

Architecture Differences

The Tesla K80 and Tesla M4 belong to two consecutive NVIDIA compute generations, despite both being fabricated on a 28 nm process at TSMC. The K80 uses the GK210 chip from the Kepler 2.0 architecture, while the M4 uses the GM206 chip from the Maxwell 2.0 architecture. This architectural split is significant: Kepler 2.0 was designed for high-end compute density, whereas Maxwell 2.0 emphasized efficiency and lower power draw.

The K80 packs 7,100 million transistors on a 561 mm² die, yielding a transistor density of 12.7 million per mm². The M4 is a much smaller chip, containing 2,940 million transistors on a 228 mm² die, with a slightly higher density of 12.9 million per mm². The K80's larger silicon area accommodates a much larger compute configuration: 2496 shading units, 208 texture mapping units, and 48 ROPs. In contrast, the M4 has 1024 shading units, 64 TMUs, and 32 ROPs.

Clock speeds also differ substantially. The K80 runs at a base clock of 562 MHz with a boost of 824 MHz, while the M4 operates at a higher base of 872 MHz and boosts to 1072 MHz. This means the M4's smaller core count is partially offset by its higher frequency. Memory clocks follow the same pattern: the K80 runs its GDDR5 at 1253 MHz (5 Gbps effective), while the M4 runs at 1375 MHz (5.5 Gbps effective).

The API support reflects the generation gap. The K80 supports DirectX 12 (11_1) and Vulkan 1.2.175, whereas the M4 supports DirectX 12 (12_1) and a newer Vulkan 1.4. Both report OpenGL 4.6. The K80 is the predecessor to the Tesla Maxwell generation, which is exactly where the M4 sits, and the M4 itself is the predecessor to Tesla Pascal, showing the lineage clearly.

Physical design differs as well. The K80 is a dual-slot card measuring 267 mm (10.5 inches) in length and requires a single 8-pin power connector. The M4 is a single-slot card with no power connectors listed, reflecting its much lower power envelope. Neither card has display outputs, as both are compute-oriented accelerators.

Head-to-Head Benchmarks

The only direct head-to-head benchmark in the database is Geekbench OpenCL, and the Tesla K80 takes the win. The K80 scores 18620 points against the M4's 16932 points, a delta of exactly 10%. This margin is substantial but not overwhelming, particularly given the K80's much larger silicon budget and power allocation.

Interpreting this score, the K80's advantage comes primarily from its 2496 shading units versus the M4's 1024. Even though the M4 clocks roughly 55% higher at boost (1072 MHz versus 824 MHz), the K80 has nearly 2.4 times the shading units. The raw compute throughput confirms this: the K80 delivers 4.113 TFLOPS of FP32 performance, while the M4 delivers 2.195 TFLOPS. The K80 is roughly 87% ahead in raw floating-point capability, yet the benchmark margin is only 10%, suggesting that the OpenCL workload may be limited by other factors such as memory bandwidth or driver overhead.

The K80 also holds a commanding lead in memory bandwidth: 240.6 GB/s versus 88.00 GB/s for the M4. That is a 173% advantage for the K80, driven by its 384-bit bus versus the M4's 128-bit bus. However, the M4's higher memory clock (5.5 Gbps effective versus 5 Gbps) partially compensates for the narrower interface.

Pixel and texture rates follow the same hierarchy. The K80 achieves 42.85 GPixel/s and 171.4 GTexel/s, while the M4 achieves 34.30 GPixel/s and 68.61 GTexel/s. The K80 leads by 25% in pixel throughput and by 150% in texture throughput. These figures reinforce the K80's role as a high-throughput compute device.

In terms of percentile ranking, the K80 sits at the 63rd percentile among all GPUs, while the M4 sits at the 60th percentile. The 3-percentile gap is modest, reflecting that both cards are mid-pack performers in the broader GPU landscape. The K80's average benchmark score of 18866 is 11.4% higher than the M4's 16932, a more pronounced difference than the single head-to-head test suggests.

Specification Differences

The two accelerators differ across nearly every specification category. The K80 uses the GK210 chip on Kepler 2.0, while the M4 uses the GM206 on Maxwell 2.0. The K80 has 7,100 million transistors on a 561 mm² die, versus 2,940 million on 228 mm² for the M4. Transistor density is nearly identical at 12.7M per mm² versus 12.9M per mm².

Clock speeds: the K80 runs at 562 MHz base and 824 MHz boost, while the M4 runs at 872 MHz base and 1072 MHz boost. Memory clocks: the K80 runs at 1253 MHz (5 Gbps effective), while the M4 runs at 1375 MHz (5.5 Gbps effective).

Memory: the K80 has 12 GB GDDR5 on a 384-bit bus with 240.6 GB/s bandwidth, while the M4 has 4 GB GDDR5 on a 128-bit bus with 88.00 GB/s bandwidth. Compute units: the K80 has 2496 shading units, 208 TMUs, and 48 ROPs, versus the M4's 1024 shading units, 64 TMUs, and 32 ROPs.

Pixel and texture rates: the K80 achieves 42.85 GPixel/s and 171.4 GTexel/s, while the M4 achieves 34.30 GPixel/s and 68.61 GTexel/s. FP32 performance: the K80 delivers 4.113 TFLOPS versus 2.195 TFLOPS for the M4.

Power and physical specs: the K80 has a 300 W TDP with a 700 W suggested PSU, is dual-slot, and requires a 1x 8-pin connector. The M4 has a 50 W TDP with a 250 W suggested PSU, is single-slot, and lists no power connectors. The K80 measures 267 mm in length; the M4 has no listed dimensions.

API support: the K80 supports DirectX 12 (11_1) and Vulkan 1.2.175, while the M4 supports DirectX 12 (12_1) and Vulkan 1.4. Both support OpenGL 4.6. Release dates: the K80 launched November 16, 2014, and the M4 launched November 9, 2015. The K80's predecessor is Tesla Fermi with successor Tesla Maxwell, while the M4's predecessor is Tesla Kepler with successor Tesla Pascal.

The Verdict

The data clearly favors the Tesla K80 in compute performance. It wins the sole head-to-head benchmark by 10%, holds a 87% advantage in FP32 throughput, and offers 173% more memory bandwidth. Its average benchmark score of 18866 is 11.4% higher than the M4's 16932. The K80 also ranks higher at the 63rd percentile versus the M4's 60th.

However, the M4 is not without merit. Its 50 W TDP versus the K80's 300 W means it draws one-sixth the power, and its single-slot design with no power connectors makes it far easier to deploy in dense server configurations. The M4's higher clock speeds (872 MHz base, 1072 MHz boost) also indicate better per-watt efficiency for lightly threaded or latency-sensitive tasks.

For raw compute workloads that can use the K80's massive shading unit count, the K80 is the clear choice. For power-constrained environments or workloads that do not scale across the K80's 2496 cores, the M4's lower power draw and smaller footprint may be preferable. The K80's 12 GB memory capacity also makes it suitable for larger datasets, while the M4's 4 GB is a limiting factor for memory-heavy workloads.

The database indicates both cards are end-of-life, so neither is a forward-looking purchase. But among the two, the K80 is the more capable compute device, while the M4 is the more efficient and compact option.

Where Each One Wins

The Tesla K80 wins in every measured performance category. It leads in the Geekbench OpenCL test (18620 versus 16932, a 10% margin). It has higher pixel rate (42.85 GPixel/s versus 34.30 GPixel/s) and over twice the texture rate (171.4 GTexel/s versus 68.61 GTexel/s). Its FP32 throughput is 4.113 TFLOPS, nearly double the M4's 2.195 TFLOPS. Memory bandwidth is a decisive advantage: 240.6 GB/s versus 88.00 GB/s, allowing the K80 to feed its larger compute cluster more effectively.

The K80 also wins on memory capacity with 12 GB versus 4 GB, and on bus width with 384-bit versus 128-bit. Its shading unit count of 2496 versus 1024 means it can handle massively parallel workloads with more concurrent threads. In the nearest rival context, the K80's average score of 18866 places it within 1% of the RTX 2070, RTX 2000 Ada, Quadro K6000, and Radeon RX 6600, all of which are much newer consumer or professional parts.

The Tesla M4 wins on power efficiency and physical footprint. At 50 W versus 300 W, it consumes 83% less power. Its single-slot design with no power connectors allows for higher deployment density in servers. The M4's suggested PSU of 250 W versus 700 W for the K80 means it can be installed in systems with much smaller power supplies. The M4 also has higher clock speeds, which can benefit workloads that are sensitive to clock frequency rather than raw core count.

The M4's newer Vulkan support (1.4 versus 1.2.175) and DirectX 12 (12_1 versus 11_1) indicate better compatibility with modern graphics APIs, though neither card has display outputs, so this is only relevant for compute contexts that use these APIs for general-purpose processing.

In practical terms, the K80 suits batch processing, large matrix operations, and workloads that can utilize its 12 GB frame buffer and massive parallel throughput. The M4 suits inference tasks, smaller batch sizes, and environments where power draw and physical space are constrained. The benchmark data does not record any test where the M4 wins, so its advantages are entirely infrastructure-related rather than performance-related.