GPU Acceleration and Optimization

NCP-GENL · 28 questions

• City DGX fine-tuning of a large ordinance model no longer fits DDP replicas per GPU. What multi-GPU choice fits?
• A county continued-pretrain of a 70B civic model needs layers split across GPUs. Which parallelism applies?
• Housing trainers stage a deep encoder across four GPUs while microbatches keep flowing. What technique is that?
• A 311 intent model already fits on one GPU. How should the city speed fine-tuning with spare accelerators?
• Long council transcripts exceed one GPU’s activation memory for context. Which approach helps?
• A city MoE router model places different experts on different GPUs. What parallelism is that?
• Public-health bilingual clinical-note adaptation runs on an 8-GPU node. Which hybrid strategy fits when neither pure DP nor pure TP alone is ideal?
• Even a single layer of an assessor multimodal stack exceeds one GPU. What is required?
• Transit fine-tuning on A100s needs higher tokens/sec. Which optimization should be enabled?
• Clerk GPU jobs raise microbatch until memory spikes. What tuning restores steadier throughput?
• A long housing SFT run shows loss spikes under FP16. Which mixed-precision choice is more appropriate?
• Emergency model serving sees alert spikes and underfilled GPU batches. How should ops keep Tensor Cores busy?
• Water-utility overnight jobs need larger effective batches than activations allow. What technique helps?
• School-district trainers see GPUs idle between steps waiting on data. What host-side fix helps?
• Parks captioning enables AMP and must prove Tensor Core gains to budget owners. What should they measure?
• Profiling a city LLM shows Softmax+MatMul dominating step time. What attention-path action fits?
• Housing LoRA has only two GPUs and needs a larger effective batch. Which technique mimics that batch?
• A long-context ordinance model must spread QKV GEMMs across GPUs. What distribution approach fits?
• Public-health wants reproducible clinic-model runs with gradient accumulation. What configuration rule matters?
• Courts see memory-bound attention GEMM timelines on DGX. Which kernel choice is appropriate?
• County assessor parcel-description fine-tuning OOMs mid-epoch on fixed GPUs. Which change keeps the effective batch workable without buying hardware?
• Transit’s long-context schedule LLM uses sequence-parallel attention, but wall time barely improves. What should ops prioritize?
• Nsight Systems shows the city’s 311 trainer idle on GPU while CPU tokenization saturates. What fix matches the profile?
• Emergency-management fine-tunes fragment GPU memory mid-run despite average free VRAM looking fine. What should ops do?
• Kernel timelines for clerk SFT show unfused LayerNorm dominating step time. Which change is most appropriate?
• Housing-authority inference latency spikes line up with heavy host↔device copies in the profiler. What is the right response?
• Water-lab LLM OOMs only on rare very long reports; memory profiles show activation peaks. What policy fits?
• School-district IT’s custom CUDA preprocess kernel is slow. Nsight Compute shows low SM occupancy. What next?