AMD Radeon R9 M470 vs NVIDIA Tesla M2070-Q
Comparative analysis of AMD Radeon R9 M470 and NVIDIA Tesla M2070-Q videocards for all known characteristics in the following categories: Essentials, Technical info, Video outputs and ports, Compatibility, dimensions and requirements, API support, Memory, Technologies. Benchmark videocards performance analysis: PassMark - G3D Mark, PassMark - G2D Mark, Geekbench - OpenCL, GFXBench 4.0 - Manhattan (Frames), GFXBench 4.0 - Manhattan (Fps), GFXBench 4.0 - T-Rex (Frames), GFXBench 4.0 - T-Rex (Fps), CompuBench 1.5 Desktop - Face Detection (mPixels/s), CompuBench 1.5 Desktop - Ocean Surface Simulation (Frames/s), CompuBench 1.5 Desktop - T-Rex (Frames/s), CompuBench 1.5 Desktop - Video Composition (Frames/s), CompuBench 1.5 Desktop - Bitcoin Mining (mHash/s).
Differences
Reasons to consider the AMD Radeon R9 M470
- Videocard is newer: launch date 4 year(s) 9 month(s) later
- Around 57% higher core clock speed: 900 MHz vs 574 MHz
- Around 50% higher texture fill rate: 48 GTexel / s vs 32.1 GTexel / s
- Around 71% higher pipelines: 768 vs 448
- Around 49% better floating-point performance: 1,536 gflops vs 1,027.7 gflops
- A newer manufacturing process allows for a more powerful, yet cooler running videocard: 28 nm vs 40 nm
- Around 92% higher memory clock speed: 6000 MHz vs 3132 MHz
- Around 78% better performance in PassMark - G3D Mark: 2331 vs 1306
Specifications (specs) | |
Launch date | 15 May 2016 vs 25 July 2011 |
Core clock speed | 900 MHz vs 574 MHz |
Texture fill rate | 48 GTexel / s vs 32.1 GTexel / s |
Pipelines | 768 vs 448 |
Floating-point performance | 1,536 gflops vs 1,027.7 gflops |
Manufacturing process technology | 28 nm vs 40 nm |
Memory clock speed | 6000 MHz vs 3132 MHz |
Benchmarks | |
PassMark - G3D Mark | 2331 vs 1306 |
Reasons to consider the NVIDIA Tesla M2070-Q
- Around 50% higher maximum memory size: 6 GB vs 4 GB
- Around 45% better performance in PassMark - G2D Mark: 444 vs 307
Specifications (specs) | |
Maximum memory size | 6 GB vs 4 GB |
Benchmarks | |
PassMark - G2D Mark | 444 vs 307 |
Compare benchmarks
GPU 1: AMD Radeon R9 M470
GPU 2: NVIDIA Tesla M2070-Q
PassMark - G3D Mark |
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PassMark - G2D Mark |
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Name | AMD Radeon R9 M470 | NVIDIA Tesla M2070-Q |
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PassMark - G3D Mark | 2331 | 1306 |
PassMark - G2D Mark | 307 | 444 |
Geekbench - OpenCL | 47924 | |
GFXBench 4.0 - Manhattan (Frames) | 1804 | |
GFXBench 4.0 - Manhattan (Fps) | 1804 | |
GFXBench 4.0 - T-Rex (Frames) | 1674 | |
GFXBench 4.0 - T-Rex (Fps) | 1674 | |
CompuBench 1.5 Desktop - Face Detection (mPixels/s) | 24.784 | |
CompuBench 1.5 Desktop - Ocean Surface Simulation (Frames/s) | 917.512 | |
CompuBench 1.5 Desktop - T-Rex (Frames/s) | 3.125 | |
CompuBench 1.5 Desktop - Video Composition (Frames/s) | 44.227 | |
CompuBench 1.5 Desktop - Bitcoin Mining (mHash/s) | 93.533 |
Compare specifications (specs)
AMD Radeon R9 M470 | NVIDIA Tesla M2070-Q | |
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Essentials |
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Architecture | GCN 2.0 | Fermi |
Code name | Strato | GF100 |
Launch date | 15 May 2016 | 25 July 2011 |
Place in performance rating | 768 | 770 |
Type | Laptop | Workstation |
Launch price (MSRP) | $5,489 | |
Technical info |
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Boost clock speed | 1000 MHz | |
Core clock speed | 900 MHz | 574 MHz |
Floating-point performance | 1,536 gflops | 1,027.7 gflops |
Manufacturing process technology | 28 nm | 40 nm |
Pipelines | 768 | 448 |
Texture fill rate | 48 GTexel / s | 32.1 GTexel / s |
Transistor count | 2,080 million | 3,100 million |
Thermal Design Power (TDP) | 225 Watt | |
Video outputs and ports |
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Display Connectors | No outputs | No outputs |
Compatibility, dimensions and requirements |
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Interface | PCIe 3.0 x16 | PCIe 2.0 x16 |
Laptop size | large | |
Length | 248 mm | |
Supplementary power connectors | 1x 6-pin + 1x 8-pin | |
API support |
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DirectX | 12.0 (12_0) | 12.0 (11_0) |
OpenGL | 4.5 | 4.6 |
Memory |
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Maximum RAM amount | 4 GB | 6 GB |
Memory bandwidth | 96 GB / s | 150.3 GB / s |
Memory bus width | 128 Bit | 384 Bit |
Memory clock speed | 6000 MHz | 3132 MHz |
Memory type | GDDR5 | GDDR5 |
Shared memory | 0 | |
Technologies |
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DirectCompute 5.0 |