--- title: "Fine-tuning by data + compute scale: four named playbooks" description: "From under 1k examples to over 1M. Single A10G to 128 B200. Indicative cost, recommended method, hardware tier per scale. The architecture of the fine-tune changes with the data, not the other way around." source: "https://www.kensink.com/llm/fine-tuning/by-scale/" canonical: "https://www.kensink.com/llm/fine-tuning/by-scale/" --- ★ By scale Direct LLM · benchmark-first Production grade FINE-TUNING · SCALE # Four named playbooks. By data, by compute, by cost. The right method changes with the data volume and the compute budget. We name four tiers, the method for each, the hardware that fits, and an honest cost range. H100 H200 B200 A10G FSDP DeepSpeed [Start a conversation →](https://www.kensink.com/contact) [Fine-tuning hub →](https://www.kensink.com/llm/fine-tuning) Tiers 4 (Tiny · Small · Mid · Large) Range <1k → 1M+ labelled examples Default tiny Few-shot + DSPy, no fine-tune Default mid LoRA r=16 with DoRA, single H100 \[FOUR TIERS\] ## The architecture changes with the scale. Each tier card carries a different brand gradient so the eye can scan across at a glance. The method, hardware, and indicative cost are the durable parts. Pricing moves quarterly; we re-validate every engagement. Tier Tiny Under 1,000 labelled examples Proof of concept, narrow specialist, before-data shipping. The cheapest path is usually not fine-tuning. Method Few-shot + DSPy / GEPA prompt optimization Hardware Inference only Indicative cost $0 to $100 in API spend Tier Small 1,000 to 50,000 examples Most enterprise fine-tunes land here: support tuning, style alignment, structured extraction, per-customer adapters. LoRA's sweet spot. Method LoRA r=16 with DoRA, all-linear targeting Hardware 1 GPU (A10G, 4090, A100, or 1x H100) Indicative cost $10 to $500 per run Tier Mid 50,000 to 1,000,000 examples Multi-tenant SaaS with diverse customer data, deep domain adaptation, models that need cross-task generalization. Method DoRA + DPO, or QLoRA on a 70B base Hardware 1 to 8 H100s, FSDP Indicative cost $500 to $5,000 per run Tier Large 1M+ examples or CPT + SFT pipelines Continued pretraining for foreign vocabulary, full SFT on hard reasoning, GRPO/RFT runs that need thousands of rollouts, custom model builds. Method Full SFT or CPT+SFT+DPO+GRPO pipeline Hardware 8 to 128 H100/H200/B200, FSDP or Megatron Indicative cost $5,000 to $200,000+ per run \[HARDWARE IN 2026\] ## GPUs we deploy on, by tier. The 2025-2026 supply has shifted. H100 stays the workhorse, H200 broadly available, B200 shipping with ~2.5x H100 training performance. 24 GB A10G Single-GPU LoRA on 7B base, QLoRA on 13B. Cheap iteration. ~$1.10/hr cloud 80 GB H100 Workhorse. FP16 LoRA on 7B-13B, QLoRA on 70B. 8x H100 for FSDP SFT to 70B. $2-4/GPU-hr cloud 141 GB H200 New default for 70B+ FP16 fine-tunes on a single card. Broadly available across 24+ providers. $2.10-$10.60/GPU-hr 192 GB B200 (Blackwell) ~2.5x H100 training perf. CPT, full SFT at 100B+, frontier RFT runs. $2.99-$6/GPU-hr \[ WHAT YOU GET \] ## What's documented at handoff. 1 tier Scale tier picked with rationale 1 method Method chosen and justified 1 budget Cost range agreed before any run 1 plan Iteration loop, not a moonshot \[COMMON QUESTIONS\] ## What buyers ask before they sign. How do we estimate cost for our fine-tune? Rough order of magnitude for SFT on 1B training tokens (1 epoch, 8B base model): ~6-10 H100-hours, so $25-60 cloud. For 70B SFT: ~150-250 H100-hours, $700-$1,500. Multiply by epochs. LoRA at the same scale is roughly 30-50% cheaper. QLoRA on a 70B fits one 48GB GPU so the cost collapses again. Does QLoRA always fit if FP16 LoRA does not? Almost always. NF4 + double-quantization shrinks the base to ~25% of FP16. A 70B model in QLoRA needs about 46GB at training; a single A100 80GB or H100 80GB handles it comfortably. The accuracy gap to FP16 LoRA is 0.5 to 1.5 points on typical benchmarks. When do we need a multi-node B200 cluster? Full SFT on 70B+ at scale, continued pretraining at 10B+ tokens, GRPO/RFT runs that need thousands of rollouts on a frontier-grade base. Below that, single-node FSDP on 8 H100s is enough. Above that, Lambda 1-Click Clusters or CoreWeave are the right answer. What's the iteration-cost trade? Single-GPU LoRA is the fastest iteration loop (minutes to hours per run). Multi-node full SFT is the slowest (hours to days). Build the first three to five iterations on LoRA, validate the data and the approach, then escalate to full SFT only if benchmarks force it. \[RELATED FINE-TUNING TOPICS\] ## Worth a look next. [ 01 · FINE-TUNING ### Methods SFT, LoRA, QLoRA, DoRA, DPO, SimPO, ORPO, KTO, GRPO/RFT, distillation, model merging. Every named technique with when it earns the build. Read more](https://www.kensink.com/llm/fine-tuning/methods/)[ 02 · FINE-TUNING ### Data pipeline Sourcing, PII redaction (Presidio), synthetic data (Distilabel, Nemotron), DEITA quality scoring, MinHash + SemDedup, labeling vendors, feedback loops. Read more](https://www.kensink.com/llm/fine-tuning/data-pipeline/)[ 03 · FINE-TUNING ### Platforms OpenAI RFT, Anthropic on Bedrock, Vertex, Azure Foundry, Databricks Mosaic, Together, Predibase, NeMo Customizer, Modal, Lambda. Side-by-side with our take. Read more](https://www.kensink.com/llm/fine-tuning/platforms/)[ 05 · FINE-TUNING ### Custom model build Continued pretraining, SFT, preference optimization (DPO, SimPO, ORPO), reasoning distillation (R1 lineage), model merging (TIES, DARE). The full build pipeline. Read more](https://www.kensink.com/llm/fine-tuning/custom-models/)[ 06 · FINE-TUNING ### Compliance EU AI Act (Article 25 substantial-modification trap), GDPR, HIPAA, FedRAMP, Colorado AI Act, India DPDP, China GenAI Measures. Region-by-region for tuned LLMs. Read more](https://www.kensink.com/llm/fine-tuning/compliance/) FINE-TUNING · SCALE · KENSINK LABS ## Size the work to the data. Not the other way around. We start small, benchmark fast, escalate only when the numbers force it. The first iteration is cheap by design. [Start a conversation →](https://www.kensink.com/contact) [All fine-tuning topics](https://www.kensink.com/llm/fine-tuning)