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Choosing infrastructure for 70B-class language model inference is no longer a simple question of raw GPU speed. For most enterprise teams, the real decision is about memory headroom, context length, batching efficiency, software compatibility, rack power, and the cost of delivering stable tokens per second under production load. This report compares the most relevant accelerators […]
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, […]
