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Executive summary: For 70B-class model inference, the main infrastructure decision is not simply which GPU is fastest. It is whether the system can hold the model weights, sustain the required context length, serve concurrent users without memory fragmentation, and do all of that at a cost that matches the workload. In practice, the best choice […]
For most 70B-class dense LLMs, the practical GPU choice is determined less by raw compute than by memory headroom for weights, KV cache, and concurrency. A single 80GB GPU can serve a heavily quantized deployment, but BF16 or FP16 inference usually needs multi-GPU tensor parallelism or a larger-memory accelerator. The correct answer depends on quantization, […]
