RTX 4070 TFLOPS Specs | Generated by AI
Question: RTX 4070 TFLOPS?
Answer:
Base RTX 4070 (5888 CUDA cores, AD104): FP32: 29.15 TFLOPS, FP16: 29.15 TFLOPS, BF16: 29.15 TFLOPS. Tensor core throughput is much higher: 184 fourth-generation tensor cores offering 466 tensor-TFLOPS for AI processing (that’s the INT8 dense figure — FP16 tensor is ~58 TFLOPS dense / 117 with sparsity per WareDB).
Related SKUs for context:
- RTX 4070 Ti: 7,680 CUDA cores, providing 40.09 TFLOPS of FP32 performance
- RTX 4070 SUPER: FP32: 35.48 TFLOPS, FP16: 35.48 TFLOPS, BF16: 35.48 TFLOPS
For your GPT-2 760M training context: 29 FP32 TFLOPS on a single 4070 is toy-scale — you’d want BF16 tensor throughput (~58 TFLOPS dense) and note the 12GB VRAM cap will bottleneck batch size long before compute does. MI300X you’re already using has ~163 TFLOPS FP32 / much higher matmul throughput, so 4070 is really a dev/inference box, not a training box for anything beyond small-scale experiments.
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