Kalkulator Biaya Training AI 2026: Pre-training dan Fine-tuning

Biaya training AI 2026 abarca 7 orde magnitudo — dari $1 fine-tuning adapter ke $200M+ pre-training frontier. Untuk pricing real-time, GPU Pricing Calculator. Untuk fine-tuning, LLM Fine-tuning Cost Calculator.

Aturan paling penting: hampir tidak ada yang harus pre-train from scratch 2026. Model open-weight cukup baik sebagai starting points.

Berapa biaya training AI 2026?

Tipe training	Range biaya	Waktu	Use case
LoRA fine-tune 7B	$1-50	15 menit - 2 jam	Mayoritas production
LoRA fine-tune 70B	$50-500	1-8 jam	Adapter 70B domain
Full fine-tune 7B	$100-1,000	2-12 jam	Tokenizer/vocab baru
Full fine-tune 70B	$5,000-50,000	24-96 jam	Capability major
Pre-train 7B from scratch	$100k-500k	1-4 minggu	Research / niche
Pre-train 70B from scratch	$5M-8M	4-12 minggu	Foundation research
Pre-train 405B	$40M-80M	3-6 bulan	Frontier research
Pre-train 1T+	$100M-500M+	6-18 bulan	Hanya frontier labs

Untuk 99% tim, range realistic $1-1,000 untuk LoRA.

Formula untuk biaya training

training_cost = total_gpu_hours × $/gpu_hour

Versi praktis LoRA:

training_cost = corpus_tokens × epochs × per-million_training_rate

Contoh Llama 4 8B di Together:

Corpus: 5M tokens, Epochs: 3
Together LoRA rate: $1.00 per 1M
Cost: 5 × 3 × $1.00 = $15, Waktu: ~30 menit

Full fine-tune 70B di RunPod:

50M tokens, 3 epochs, H100 SXM5 ×8
Throughput: ~30M tokens/jam
Waktu: 5 jam
Cost GPU: $2.99/jam × 8 × 5 = $120

Pre-train Llama 4 8B from scratch:

15T training tokens
129,000 GPU-jam (256 H100s × 3 minggu)
Cost: 129,000 × $2.99 = $385,000

Kenapa fine-tuning kalahkan training from scratch?

1. Kualitas comparable. Fine-tune 8B match kualitas 8B training from scratch di 10,000× lebih murah.
2. Kecepatan dramatically lebih cepat. 30 menit vs 3 minggu.
3. Open-weights tutup gap. Llama 4, DeepSeek V3, Qwen 2.5, Mistral Large 2.

Exception: bahasa niche, domain khusus, research frontier.

Hardware GPU terbaik untuk training 2026

• LoRA fine-tune ≤8B — Single H100 PCIe atau A100 80GB
• LoRA fine-tune 70B — H100 SXM ×2 atau ×4 dengan NVLink
• Full fine-tune ≤8B — H100 SXM ×4
• Full fine-tune 70B — H100 SXM ×8 cluster minimum
• Pre-train 7B — Minimum 64 H100s
• Pre-train 70B — Minimum 256 H100s
• Pre-train 405B+ — 2,000-10,000 H100s
• B200 cluster training >70B — 1.5-2× lebih cepat di 1.6× cost = 25% lebih murah per run

Path termurah untuk fine-tune model custom 2026

Tier 1 (di bawah $50)

• LoRA Llama 4 8B Fireworks: 5M × 3 × $0.50/M = $7.50
• LoRA Llama 4 8B Together: $15
• OpenAI GPT-4o mini fine-tune: $45

Tier 2 (di bawah $500)

• LoRA Llama 4 70B Together: $90
• LoRA Llama 4 70B Fireworks: $45 (termurah 70B)
• OpenAI GPT-4o fine-tune: $375
• Self-host LoRA H100 SXM ×4: $96

Tier 3 (di bawah $5,000)

• Full fine-tune Llama 4 70B RunPod: $1,148
• Full fine-tune AWS p5 spot: $2,458
• Mistral Small 3 managed: $450

Biaya termasuk selain GPU jam

1. Persiapan data. $2,000-$10,000 engineer time per project.
2. Hyperparameter sweeps. 30-100% compute tambahan.
3. Storage dan checkpointing. Model 70B checkpoint = 140GB.
4. Validation dan evaluation. 50-100 GPU-jam.
5. Run gagal dan OOMs. 20-30% run gagal.

Sewa atau miliki GPU untuk training?

GPU-jam tahunan	Sewa menang	Miliki menang
<500	✅
500-4,000	✅ tipis	colo + own ≈
4,000-15,000		✅ colo + own
>15,000		✅ own

Di bawah 4,000 GPU-jam/tahun, sewa menang. H100 capex: ~$30,000, amortize 3 tahun = $830/bulan vs $2,153/bulan RunPod.

Cara cerdas train AI 2026

1. Jangan pre-train from scratch kecuali lab research.
2. LoRA fine-tune dulu, full fine-tune hanya jika LoRA gagal.
3. Pakai managed fine-tuning hingga $1,000/bulan spend.
4. Test dengan data kecil dulu. 1M token × 1 epoch.
5. Eval rigorously.

Untuk pricing dan projeksi tahun-1, LLM Fine-tuning Cost Calculator. Untuk infra modeling, Agent Dev Cost Calculator.

Kesalahan 2026 terbesar: over-training. Mayoritas tim plateau setelah 1-2 epochs. Train kurang, eval lebih.