NVIDIA A10G vs NVIDIA RTX 4000 Ada Generation Comparison

The NVIDIA A10G and NVIDIA RTX 4000 Ada Generation occupy different corners of the professional GPU market, yet their benchmark results reveal a closer contest than their architectural generations might suggest. The A10G, built on the older Ampere architecture, posts higher raw scores in both available tests, while the RTX 4000 Ada counters with superior efficiency metrics and a newer design. This analysis examines what the data shows, where each card excels, and which workloads favor which hardware.

Head-to-Head Benchmarks

The Geekbench OpenCL test shows the A10G scoring 158,063 against the RTX 4000 Ada’s 146,593, a delta of 7.8% in favor of the older card. This is a notable margin, but not a decisive one. The A10G’s lead here likely stems from its larger memory bus and higher bandwidth: 600.2 GB/s on a 384-bit interface versus 360.0 GB/s on a 160-bit bus for the RTX 4000 Ada. Memory-bound compute tasks often reward such bandwidth advantages, and OpenCL workloads frequently stress memory throughput alongside raw compute.

The Vulkan test widens the gap considerably. The A10G scores 145,863, while the RTX 4000 Ada manages 123,842 — a 17.8% advantage for the A10G. This is a substantial difference. Vulkan’s lower-level API can expose architectural inefficiencies, and the A10G’s 9,216 shading units versus 6,144 on the RTX 4000 Ada likely contribute to this result. The A10G also carries 288 TMUs and 96 ROPs, compared to 192 TMUs and 64 ROPs on the newer card. More texture and pixel processing hardware generally translates to better rasterization throughput, which Vulkan tests often stress.

Worth noting is the A10G’s FP32 performance of 31.52 TFLOPS against the RTX 4000 Ada’s 26.73 TFLOPS. Both cards deliver FP16 at a 1:1 ratio with FP32, so the raw compute advantage holds across precision formats. The A10G’s higher boost clock of 1710 MHz cannot match the RTX 4000 Ada’s 2175 MHz, but the older card compensates with a much wider execution pipeline. Pixel rate tells a similar story: 164.2 GPixel/s for the A10G versus 139.2 GPixel/s for the RTX 4000 Ada, a 17.9% difference that mirrors the Vulkan delta.

However, context matters. The A10G’s average benchmark score of 151,963 places it at the 97th percentile of all GPUs, while the RTX 4000 Ada’s 135,218 average sits at the 95th percentile. Both are elite performers, but the A10G’s nearest rivals include the NVIDIA Tesla V100 PCIe 32 GB (150,305, only 1.1% behind) and the AMD Radeon Pro W6800X (160,671, 5.4% ahead). The RTX 4000 Ada, by contrast, trades blows with the NVIDIA A10M (135,230, 0% delta) and the AMD Radeon PRO W6800 (135,396, 0.1% behind). The A10G competes in a higher absolute performance tier, even if its architectural age shows in other ways.

Where Each One Wins

The A10G wins decisively in raw compute throughput. Its 31.52 TFLOPS FP32 output, 288 tensor cores, and 72 RT cores give it a clear edge for AI inference, scientific simulation, and any workload that saturates the GPU with math operations. The 24 GB of GDDR6 memory on a 384-bit bus also provides 600.2 GB/s of bandwidth, which is critical for large dataset processing — think big-batch machine learning or high-resolution rendering where memory capacity and speed are co-equal requirements.

The RTX 4000 Ada wins on efficiency and practicality. Its 130 W TDP versus the A10G’s 150 W means less heat and lower power draw in dense server environments. The suggested PSU rating of 300 W versus 450 W for the A10G reinforces this efficiency story. The newer 5 nm TSMC process (versus 8 nm Samsung) enables this power reduction while still delivering competitive, if lower, performance. The RTX 4000 Ada also includes 4x DisplayPort 1.4a outputs, making it usable in workstation configurations where the A10G’s lack of display outputs is a non-starter.

For memory capacity, the A10G’s 24 GB exceeds the RTX 4000 Ada’s 20 GB, but the newer card’s GDDR6 runs at 18 Gbps effective versus 12.5 Gbps on the A10G. The RTX 4000 Ada’s memory clock is higher per pin, though the narrower 160-bit bus limits total bandwidth. In scenarios where capacity matters more than bandwidth — such as holding a large model in VRAM without frequent swaps — the A10G has the edge. For latency-sensitive tasks that fit within 20 GB, the RTX 4000 Ada’s faster memory clock might prove beneficial.

FAQ

Q: Why does the older A10G outperform the newer RTX 4000 Ada in both benchmarks?

A: The A10G’s GA102 chip packs 9,216 shading units, 288 TMUs, and 96 ROPs, compared to 6,144, 192, and 64 on the RTX 4000 Ada’s AD104. This larger execution footprint yields 31.52 TFLOPS FP32 versus 26.73 TFLOPS, and a 600.2 GB/s memory bandwidth advantage (versus 360.0 GB/s) that helps in both OpenCL and Vulkan tests.

Q: Does the RTX 4000 Ada have any performance advantage at all?

A: The data shows no benchmark wins for the RTX 4000 Ada — the A10G takes both Geekbench OpenCL and Vulkan tests. However, the RTX 4000 Ada’s higher boost clock (2175 MHz vs 1710 MHz) and faster memory clock (2250 MHz vs 1563 MHz) suggest it could excel in latency-bound, low-occupancy workloads that don’t saturate the full GPU.

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

A: The A10G (avg score 151,963) sits 1.1% above the Tesla V100 PCIe 32 GB (150,305) but trails the AMD Radeon Pro W6800X (160,671) by 5.4% and the A100 PCIe 40 GB (162,504) by 6.5%. The RTX 4000 Ada (135,218) is essentially tied with the A10M (135,230, 0% delta) and sits 0.1% behind the Radeon PRO W6800 (135,396).

Q: Which card has better memory bandwidth per watt?

A: The A10G delivers 600.2 GB/s at 150 W (4.0 GB/s per watt), while the RTX 4000 Ada provides 360.0 GB/s at 130 W (2.8 GB/s per watt). The A10G is more bandwidth-efficient, but the RTX 4000 Ada’s lower total power could enable denser server packing.

Q: Can the RTX 4000 Ada replace the A10G in existing server deployments?

A: Not directly. The A10G uses an 8-pin EPS power connector and has no display outputs, while the RTX 4000 Ada uses a 1x 16-pin connector and offers 4x DisplayPort 1.4a. The physical dimensions also differ: 267 mm length for the A10G versus 245 mm for the RTX 4000 Ada. Both are single-slot and PCIe 4.0 x16, so slot compatibility is similar, but power and output differences matter.

Q: What does the 1:1 FP16 ratio mean for AI workloads on both cards?

A: Both the A10G and RTX 4000 Ada deliver FP16 at the same rate as FP32 (31.52 TFLOPS and 26.73 TFLOPS respectively). This means neither card offers a dedicated FP16 boost, which is common for Ampere and Ada Lovelace server/workstation parts. For AI training that uses FP16, the A10G’s higher absolute throughput gives it an edge.

Specification Differences

| Specification | NVIDIA A10G | NVIDIA RTX 4000 Ada Generation |

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

| Chip | GA102 | AD104 |

| Architecture | Ampere | Ada Lovelace |

| Generation | Server Ampere (Axx) | Workstation Ada (x000A) |

| Process Node | 8 nm (Samsung) | 5 nm (TSMC) |

| Transistors | 28,300 million | 35,800 million |

| Die Size | 628 mm² | 294 mm² |

| Transistor Density | 45.1M / mm² | 121.8M / mm² |

| Base Clock | 1320 MHz | 1500 MHz |

| Boost Clock | 1710 MHz | 2175 MHz |

| Memory Clock | 1563 MHz (12.5 Gbps effective) | 2250 MHz (18 Gbps effective) |

| Memory Size | 24 GB | 20 GB |

| Memory Bus Width | 384 bit | 160 bit |

| Memory Bandwidth | 600.2 GB/s | 360.0 GB/s |

| Shading Units | 9216 | 6144 |

| TMUs | 288 | 192 |

| ROPs | 96 | 64 |

| RT Cores | 72 | 48 |

| Tensor Cores | 288 | 192 |

| Pixel Rate | 164.2 GPixel/s | 139.2 GPixel/s |

| Texture Rate | 492.5 GTexel/s | 417.6 GTexel/s |

| FP32 | 31.52 TFLOPS | 26.73 TFLOPS |

| FP16 | 31.52 TFLOPS (1:1) | 26.73 TFLOPS (1:1) |

| TDP | 150 W | 130 W |

| Power Connectors | 8-pin EPS | 1x 16-pin |

| Suggested PSU | 450 W | 300 W |

| Display Outputs | No outputs | 4x DisplayPort 1.4a |

| Length | 267 mm (10.5 inches) | 245 mm (9.6 inches) |

| Height | 112 mm (4.4 inches) | 112 mm (4.4 inches) |

| Production Status | End-of-life | Active |

| Release Date | 2021-04-11 | 2023-08-08 |

| Predecessor | Tesla Turing | Workstation Ampere |

| Successor | Server Ada | Blackwell PRO W |

| Percentile | 97 | 95 |

Architecture Differences

The A10G uses the GA102 chip built on Samsung’s 8 nm process, packing 28,300 million transistors into a 628 mm² die. That results in a transistor density of 45.1 million per mm². The RTX 4000 Ada’s AD104 chip, fabricated on TSMC’s 5 nm node, crams 35,800 million transistors into just 294 mm² — a density of 121.8 million per mm². This 2.7x density advantage is the defining architectural difference. The A10G compensates with a physically larger chip and more execution units, but the RTX 4000 Ada achieves better power efficiency per transistor.

Both cards support DirectX 12 Ultimate (12_2), OpenGL 4.6, and Vulkan 1.4, so API-level features are identical. The RT core and tensor core counts differ (72 and 288 on the A10G versus 48 and 192 on the RTX 4000 Ada), but both architectures support ray tracing and AI acceleration. The A10G’s higher core counts suggest better raw performance in ray-traced and tensor-heavy workloads, though the RTX 4000 Ada’s newer architecture may offer improved per-core efficiency — the data doesn’t capture that directly.

The memory subsystems differ fundamentally. The A10G uses a 384-bit bus with 24 GB of GDDR6 at 12.5 Gbps effective, yielding 600.2 GB/s. The RTX 4000 Ada uses a 160-bit bus with 20 GB at 18 Gbps effective, yielding 360.0 GB/s. The A10G’s wider bus is a legacy design choice favoring bandwidth, while the RTX 4000 Ada’s faster clock speed per pin reflects newer memory technology. The A10G also has a higher pixel rate (164.2 GPixel/s) and texture rate (492.5 GTexel/s) versus 139.2 GPixel/s and 417.6 GTexel/s on the RTX 4000 Ada.

The Verdict

The benchmark data clearly favors the NVIDIA A10G for raw performance. It wins both head-to-head tests, leads in every compute metric (FP32, FP16, pixel rate, texture rate), and offers 4 GB more memory with 66.7% more bandwidth. Its 97th percentile ranking versus 95th for the RTX 4000 Ada confirms this — the A10G sits in a higher performance tier. Buyers who need maximum compute throughput for AI training, scientific computing, or high-resolution rendering should choose the A10G based on this data.

The RTX 4000 Ada is the better choice for efficiency and integration. Its 130 W TDP and 300 W suggested PSU make it easier to deploy in power-constrained environments. The 4x DisplayPort 1.4a outputs are essential for workstation use — the A10G has no display outputs at all. The newer 5 nm process and 35,800 million transistors in a smaller die suggest architectural refinement, even if that doesn’t translate to benchmark wins. Its active production status (versus end-of-life for the A10G) also matters for long-term availability.

In short: the A10G is the performance king, the RTX 4000 Ada is the efficiency and practicality pick. The 17.8% Vulkan gap and 7.8% OpenCL gap are significant enough that any workload heavily dependent on those APIs should lean A10G. But for mixed-use workstations where power, cooling, and display outputs matter, the RTX 4000 Ada’s lower power draw and connectivity make it the more versatile card. The data doesn’t support a single winner — it supports two different jobs.