Fine-tuning
9 articlesContinual Pre-Training: When Fine-Tuning Is Not Enough
Fine-tuning teaches behaviour; continual pre-training teaches a language or a domain's underlying distribution. It costs orders of magnitude more and risks catastrophic forgetting. This sets out when it is genuinely…
Indian-Language Adaptation: Tokenizers, Data and GPU Planning
Sovereign Indian models from Sarvam, BharatGen and Gnani now cover 22 languages, and IndiaAI has commissioned over 36,000 GPUs heading toward 100,000. This covers tokenizer efficiency, data strategy and how…
Sizing the RL Fine-Tuning Loop: Rollouts, Verifiers and GPU Split
Reinforcement learning with verifiable rewards is now standard post-training, and it inverts the usual sizing assumption: most of the GPU budget goes to generating rollouts, not to gradient steps. That…
Fine-Tuning Mixture-of-Experts Models: Memory and Routing Realities
MoE models activate few parameters but must hold all experts in memory. A widely cited example needs roughly 94 GB at FP16 for inference alone, and fine-tuning adds optimiser state…
Beyond SFT: Sizing DPO and RLVR Post-Training Infrastructure
The 2026 post-training recipe is SFT, then DPO, then RL with verifiable rewards. DPO doubles resident model copies; GRPO halved RL memory by dropping the critic, putting 7-32B reasoning training…
GPU Cluster Networking for Training and Fine-Tuning
GPU cluster networking is the critical path for distributed training and fine-tuning: the interconnect fabric between GPUs determines whether your cluster scales linearly or stalls on collective communication. For workloads…
Fine-Tuning GPU Server Sizing for Enterprise LLMs
Fine-tuning infrastructure should be sized around dataset shape, experiment cadence, checkpoint strategy, and governance controls before GPU count. RDP GPU Mart can help Indian teams compare DRACO GPU server options…
Best On-Prem Setup for a Startup Training Small LLMs (<13B)
A startup working with sub-13-billion-parameter models needs far less hardware than the headlines suggest: a single GPU workstation with 48–141 GB of VRAM handles fine-tuning (QLoRA/LoRA) and inference for models…
How Many GPUs Do You Need to Fine-Tune a 70B LLM On-Prem?
Fine-tuning a 70-billion-parameter LLM on-premises needs roughly one GPU for QLoRA, two GPUs for standard LoRA, and eight or more for full FP16 fine-tuning. The deciding variable is the tuning…