NVIDIA Quadro K4000M vs NVIDIA Quadro M500M Comparison

# Head-to-Head Benchmarks

The benchmark data shows a single head-to-head comparison between these two mobile workstation GPUs, and the result is remarkably close. In the Geekbench OpenCL test, the NVIDIA Quadro K4000M and NVIDIA Quadro M500M both score exactly 5986 points, producing a deltaPct of 0. The K4000M is credited with the win, but this is effectively a tie—neither card demonstrates a measurable performance advantage in raw compute throughput.

Looking at the broader benchmark picture, the K4000M's average benchmark score sits at 5986, placing it in the 34th percentile of all GPUs. Its nearest rivals include the AMD FirePro W4100 at 5987 (0% delta), the NVIDIA Quadro K4000 at 5982 (0.1% delta), and the NVIDIA GeForce GTX 770M at 6000 (-0.2% delta). These numbers show the K4000M is tightly clustered with its contemporaries, with performance differences of less than one percent across the board.

The M500M, by contrast, has two benchmark results: 5986 in Geekbench OpenCL and 5222 in Geekbench Vulkan. Its average benchmark score is 5604, reflecting the Vulkan result pulling the average down. This places the M500M in the 32nd percentile of all GPUs. Its nearest rivals include the AMD FirePro M4000 at 5537 (1.2% delta in favor of the M500M), the AMD Radeon HD 8790M at 5691 (-1.5% delta), the NVIDIA GeForce MX130 at 5508 (1.7% delta), and the NVIDIA GeForce GTX 765M at 5501 (1.9% delta). The M500M leads its most direct competitors by roughly 1-2%, a modest but consistent margin.

The key takeaway from the benchmark data is that in OpenCL compute workloads, these two cards are effectively identical despite their generational gap. The K4000M from the Kepler era and the M500M from the Maxwell era land on the same score, suggesting that architectural efficiency improvements in the newer chip are offset by the older card's larger resource pool. However, the M500M's Vulkan score of 5222 indicates that modern API support gives it a separate capability the K4000M simply cannot match—the older card has no Vulkan benchmark result at all.

# Architecture Differences

The K4000M and M500M represent two different generations of NVIDIA mobile workstation silicon, and their architectural differences are substantial. The K4000M uses the GK104 chip based on the Kepler architecture, fabricated on a 28 nm process at TSMC. It packs 3,540 million transistors onto a 294 mm² die, yielding a transistor density of 12.0M per mm². The M500M uses the GM108S chip based on the Maxwell architecture, also on a 28 nm TSMC process, but with only 1,020 million transistors on a much smaller 77 mm² die, achieving a higher density of 13.2M per mm².

The compute resource disparity is significant. The K4000M features 960 shading units, 80 texture mapping units, and 32 raster output pipelines. The M500M has 384 shading units, 16 TMUs, and 8 ROPs. That means the K4000M has 2.5 times more shaders, 5 times more TMUs, and 4 times more ROPs than the M500M. Despite this massive resource advantage, the K4000M only edges out the M500M in OpenCL by a negligible margin—proof of the Maxwell architecture's improved efficiency per core.

Clock speeds tell the opposite story. The K4000M runs at a fixed 601 MHz for both base and boost, with memory at 700 MHz (2.8 Gbps effective). The M500M runs at 1029 MHz base and 1124 MHz boost, with memory at 900 MHz (1800 Mbps effective). The M500M's clocks are roughly 70-87% higher than the K4000M's, partially compensating for its smaller core configuration.

Memory configurations differ substantially. The K4000M uses 4 GB of GDDR5 on a 256-bit bus, delivering 89.60 GB/s of bandwidth. The M500M uses 2 GB of DDR3 on a 64-bit bus, delivering only 14.40 GB/s. The K4000M offers 6.2 times more memory bandwidth, which matters significantly for texture-heavy workloads and large datasets.

The K4000M's fill rates reflect its resource advantage: 12.02 GPixel/s pixel rate and 48.08 GTexel/s texture rate versus the M500M's 8.992 GPixel/s and 17.98 GTexel/s. In FP32 compute, the K4000M delivers 1,153.9 GFLOPS while the M500M delivers 863.2 GFLOPS—a 34% advantage for the older card. Neither card supports FP16, ray tracing cores, or tensor cores.

Power consumption shows the generational efficiency gain: the K4000M is rated at 100 W TDP, while the M500M draws only 30 W. That is a 70% reduction in power draw for nearly equivalent OpenCL performance. Both use MXM modules, but the K4000M uses the MXM-B (3.0) interface while the M500M uses the smaller MXM-A (3.0) form factor. Neither requires external power connectors, and both have portable-device-dependent display outputs.

# Where Each One Wins

The K4000M wins in scenarios where raw compute resources and memory bandwidth dominate. Its 89.60 GB/s of GDDR5 bandwidth versus the M500M's 14.40 GB/s of DDR3 makes it the clear choice for workloads that stream large amounts of data—think high-resolution texture mapping, large frame buffers, or compute tasks with massive working sets. Its 4 GB memory capacity also allows larger datasets to reside on the GPU without spilling to system memory. The K4000M's 1,153.9 GFLOPS FP32 throughput is 34% higher than the M500M's 863.2 GFLOPS, giving it an edge in pure compute-bound operations like FEA simulations or dense linear algebra.

The M500M wins in efficiency and modern API support. Its 30 W TDP versus the K4000M's 100 W makes it suitable for thinner, lighter mobile workstations with less thermal headroom. The M500M supports Vulkan 1.4 while the K4000M is limited to Vulkan 1.2.175, and the M500M's 5222 Geekbench Vulkan score demonstrates it can leverage modern graphics APIs effectively. The K4000M has no Vulkan benchmark result, indicating its Vulkan capabilities are either absent or too weak to be meaningful.

The M500M also wins in clock speed headroom. Its 1124 MHz boost clock versus the K4000M's fixed 601 MHz means the Maxwell chip can respond to transient workload demands more aggressively. In bursty, latency-sensitive tasks, the M500M may feel more responsive even when its aggregate throughput is lower.

The benchmark tie in OpenCL means that for general-purpose compute, neither card offers a tangible advantage. The deciding factors come down to memory capacity, power envelope, and API support. The K4000M is the better choice for memory-hungry workloads; the M500M is the better choice for modern API compatibility and battery-conscious mobile workstations.

# FAQ

Q: Which GPU is faster in OpenCL compute?

A: Neither. Both the NVIDIA Quadro K4000M and NVIDIA Quadro M500M score exactly 5986 in Geekbench OpenCL, resulting in a 0% delta. The K4000M is credited with the win, but the scores are identical.

Q: Does the M500M support Vulkan?

A: Yes. The M500M supports Vulkan 1.4 and scores 5222 in Geekbench Vulkan. The K4000M supports Vulkan 1.2.175, but no Vulkan benchmark result is recorded for it.

Q: How much more memory bandwidth does the K4000M have?

A: The K4000M has 89.60 GB/s of bandwidth from its 256-bit GDDR5 memory, while the M500M has 14.40 GB/s from its 64-bit DDR3 memory—a 6.2-fold difference.

Q: Which card consumes less power?

A: The M500M is rated at 30 W TDP, while the K4000M is rated at 100 W TDP. The M500M draws 70% less power.

Q: How do these cards compare to their nearest rivals?

A: The K4000M sits within 0.2% of the AMD FirePro W4100 (5987), NVIDIA Quadro K4000 (5982), and NVIDIA GeForce GTX 770M (6000). The M500M leads the AMD FirePro M4000 by 1.2%, the NVIDIA GeForce MX130 by 1.7%, and the NVIDIA GeForce GTX 765M by 1.9%, but trails the AMD Radeon HD 8790M by 1.5%.

Q: How much VRAM does each card have?

A: The K4000M has 4 GB of GDDR5 memory, while the M500M has 2 GB of DDR3 memory.

# Specification Differences

| Specification | NVIDIA Quadro K4000M | NVIDIA Quadro M500M |

|---|---|---|

| Architecture | Kepler | Maxwell |

| Chip | GK104 | GM108S |

| Process Node | 28 nm | 28 nm |

| Transistors | 3,540 million | 1,020 million |

| Die Size | 294 mm² | 77 mm² |

| Transistor Density | 12.0M / mm² | 13.2M / mm² |

| Base Clock | 601 MHz | 1029 MHz |

| Boost Clock | 601 MHz | 1124 MHz |

| Memory Clock | 700 MHz (2.8 Gbps effective) | 900 MHz (1800 Mbps effective) |

| Memory Size | 4 GB | 2 GB |

| Memory Type | GDDR5 | DDR3 |

| Memory Bus Width | 256 bit | 64 bit |

| Memory Bandwidth | 89.60 GB/s | 14.40 GB/s |

| Shading Units | 960 | 384 |

| TMUs | 80 | 16 |

| ROPs | 32 | 8 |

| Pixel Rate | 12.02 GPixel/s | 8.992 GPixel/s |

| Texture Rate | 48.08 GTexel/s | 17.98 GTexel/s |

| FP32 | 1,153.9 GFLOPS | 863.2 GFLOPS |

| TDP | 100 W | 30 W |

| Bus Interface | MXM-B (3.0) | MXM-A (3.0) |

| Vulkan API | 1.2.175 | 1.4 |

| Release Date | 2012-05-31 | 2016-04-26 |

| Predecessor | Quadro Fermi-M | Quadro Kepler-M |

| Successor | Quadro Maxwell-M | Quadro Pascal-M |

| Average Benchmark Score | 5986 | 5604 |

| Percentile vs All GPUs | 34 | 32 |

# The Verdict

The data presents a straightforward trade-off. The NVIDIA Quadro K4000M and M500M deliver identical OpenCL performance—both score 5986—so raw compute throughput should not be the deciding factor. The K4000M offers 4 GB of GDDR5 with 89.60 GB/s bandwidth, 960 shading units, and 1,153.9 GFLOPS of FP32 compute. The M500M offers 2 GB of DDR3 with 14.40 GB/s bandwidth, 384 shading units, and 863.2 GFLOPS, but does so at 30 W TDP versus the K4000M's 100 W.

For anyone working with large datasets, high-resolution textures, or memory-bandwidth-intensive compute tasks, the K4000M is the clear choice. Its 6.2 times more bandwidth and 34% higher FP32 throughput make it the more capable compute resource, despite its older Kepler architecture. The 4 GB frame buffer also provides headroom for larger workloads.

For anyone prioritizing power efficiency, modern API support, or working in a thin-and-light mobile chassis, the M500M wins. Its 70% lower TDP, higher clock speeds (1124 MHz boost versus 601 MHz), and Vulkan 1.4 support with a 5222 Vulkan score make it a more future-proof option for software that leverages modern graphics APIs.

The K4000M's 34th percentile ranking versus the M500M's 32nd percentile reflects the slight overall edge of the older card in the benchmark database. The M500M's average score of 5604 is dragged down by its Vulkan result, which is not a compute-identical workload to OpenCL. The nearest-rival data shows both cards are competitive within their respective peer groups.

The verdict: if you need memory bandwidth and raw compute density, take the K4000M. If you need efficiency, modern API support, and lower power draw, take the M500M. There is no universal winner—only the right tool for the specific workload.