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Research questionHow can 70B language models fit on one GPU while preserving long-context speed and accuracy?A 70B model must fit its weights and growing KV cache within one GPU’s limited memory. Long prompts make compression choices affect both decoding speed and model accuracy.
AI
AI Memory
Inference Optimization
Technology
Latest papersRecent research connected to this question, newest first.Budget-Aware Compression Pipeline for Single-GPU LLM Inference: Methods, Trade-offs, and Coupling EffectsThe evidence concerns 70B-parameter language-model inference on a single NVIDIA A40, using pruning, weight and KV-cache quantization, and KV-cache sparsification. The reported pipeline uses about 33 GB, reaches about 57 tokens per second on 10k-token prompts, and keeps absolute accuracy within 5% on common and reasoning benchmarks; broader hardware and workload behavior is not established.research paper · Sep 3, 2026
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