Graphics Cards for AI/GPU Services

Comparison of GPUs Used in Our Services

Graphics Card	Performance (FP32 TFLOPS)	Memory (GB)	Core Count	Tensor Cores	Architecture	Bus Width (bits)	Additional Specifications
NVIDIA RTX 4090	82.58	24	16,384 CUDA	512	Ada Lovelace	384	PCIe 4.0, 4K and 8K gaming
AMD RX 7900 XTX	61.44	24	6,144 RDNA 3	N/A	RDNA 3	384	AV1 Encode/Decode, Ray Tracing
NVIDIA RTX 5000 Ada	~64	32	9,728 CUDA	304	Ada Lovelace	256	Professional graphics, PCIe 4.0
AMD W7900 PRO	61	48	14,080 RDNA 3	N/A	RDNA 3	384	AV1 Encode/Decode, Ray Tracing
NVIDIA RTX 6000 Ada	91.06	48	18,176 CUDA	568	Ada Lovelace	384	48 GB memory, PCIe 4.0
NVIDIA H100	1450 (TF32 Matrix Ops)	80	114,000 CUDA	640	Hopper	512	AI/ML accelerator, NVLink
NVIDIA H200	~1600 (TF32 Matrix Ops)	96	Unspecified	Unspecified	Hopper	512	AI/ML, advanced NVLink

Performance (TFLOPS): Values are for single-precision floating-point calculations (FP32), which are relevant for both gaming and scientific computations. For H100 and H200, TF32 performance for AI computations accelerated by tensor cores is included.
Memory (GB): The amount of memory available on the graphics card. More memory is advantageous for working with large datasets (AI, rendering).
Core Count: The number of compute cores on the card (CUDA for NVIDIA, RDNA for AMD). The higher the number of cores, the greater the potential parallel performance.
Tensor Cores: Specialized cores designed to accelerate computations in machine learning (AI), particularly for training and inference of neural networks. Available only in NVIDIA graphics cards.
Architecture: The underlying technological platform on which the card is built. Newer architectures (Ada Lovelace, Hopper) bring improvements in performance and efficiency.
Bus Width (bits): The width of the memory interface, which influences the speed of data access in memory.

Our AI/GPU Services Offering