GPU Cost Calculator: What AI Inference and Training Really Costs on Every Major Platform

• GPU compute costs vary 10–30× depending on provider, GPU model, and reservation type — an A100 on AWS on-demand is $3.00–$3.50/hour; the same GPU reserved for 1 year drops to $1.60–$1.90/hour; spot/preemptible instances go as low as $0.80–$1.20/hour.
• For AI inference (running a model to generate outputs), GPU cost is rarely the right metric — token cost from managed APIs (OpenAI, Anthropic, Google) is almost always cheaper than self-hosting unless you're at very high volumes.
• The break-even volume where self-hosted GPU beats managed API typically requires 50–200 million tokens per day — equivalent to a large-scale production application, not a startup or side project.
• Indian cloud providers (Jio Cloud, BSNL, Sify) offer GPU instances at ₹400–₹1,200/hour — but GPU availability, reliability, and support lag significantly behind AWS, Azure, and GCP.

Use our free GPU Cost Calculator to compute hourly, daily, and monthly GPU rental cost across providers — and find whether self-hosting or managed API is cheaper for your volume.

GPU Pricing by Provider and Model (2025)

NVIDIA H100 (Flagship — Best for LLM Training and Inference)

NVIDIA A100 80GB (Most Common for LLM Work)

NVIDIA A10G / A10 (Mid-Range — Good for Inference)

NVIDIA T4 (Entry-Level — Small Models, Dev Work)

GPU Cost in INR (For Indian Developers)

At USD/INR of ₹83.50 (verify current rate):

GPU	Provider	$/hr	₹/hr	₹/day	₹/month
H100	Lambda Labs	$3.29	₹274	₹6,580	₹1,97,400
A100 80GB	RunPod	$1.64	₹137	₹3,282	₹98,460
A100 80GB	Vast.ai	$1.20	₹100	₹2,400	₹72,000
A10G	AWS	$1.006	₹84	₹2,009	₹60,270
T4	RunPod	$0.20	₹17	₹400	₹12,000
T4	Google Cloud	$0.35	₹29	₹700	₹21,000

Indian cloud alternatives:

• Jio Cloud GPU: ₹300–₹800/hr (limited availability, V100 class)
• E2E Networks GPU: ₹180–₹600/hr (A100 available, good uptime)
• Yotta Infrastructure: ₹400–₹1,000/hr (enterprise-focused)

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GPU Cost Calculator: What Are You Computing?

For AI Training

Training Cost = GPU Hours Required × Cost per GPU Hour

Estimating GPU hours for training:

Key insight: Fine-tuning (adapting an existing model) costs 1,000–10,000× less than training from scratch. For 99% of use cases, fine-tuning an open-source model is the right approach, not training from scratch.

For AI Inference (Serving)

Monthly Inference Cost = (Requests/day × Tokens/request × Cost per token) OR (GPU hours × GPU cost)

Self-hosted inference — what you can serve per GPU:

Self-Hosted GPU vs. Managed API: Break-Even Analysis

Scenario: Running Llama-3 70B equivalent quality

Managed API option:

• GPT-4o: $5/1M input tokens + $15/1M output tokens → avg ~$10/1M tokens
• Claude Sonnet: $3/1M input + $15/1M output → avg ~$9/1M tokens

Self-hosted option (A100, RunPod $1.64/hr):

• Throughput: ~80 tokens/sec
• 1M tokens takes: 1,000,000 ÷ 80 = 12,500 seconds = 3.47 hours
• Cost: 3.47 × $1.64 = $5.69 per 1M tokens

Break-even: At 1M+ tokens/day, self-hosting A100 is 40–75% cheaper than GPT-4o.

But add:

• Engineering time to set up and maintain inference server: $2,000–$10,000/month
• Downtime and reliability costs: significant for production apps
• Model updates and fine-tuning: ongoing cost

Realistic break-even for self-hosting: 30–100M tokens/day in production volume, with a dedicated ML engineer. Below this — use managed APIs.

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GPU Selection Guide by Use Case

Use Case	Recommended GPU	Why
Development / prototyping	T4 or A10G	Cheap, sufficient for testing
Fine-tuning 7B model	A10G or A100 40GB	Fits full model, LoRA efficient
Fine-tuning 70B model	2–4× A100 80GB	Needs multi-GPU for VRAM
Production inference (7–13B)	A10G or L4	Best price/performance for inference
Production inference (70B)	A100 80GB or H100	VRAM requirement
LLM training from scratch	H100 cluster	Maximum throughput
Image generation (SD/FLUX)	A10G or 3090	Good VRAM/price balance
Video generation	H100 or multi-A100	Extremely compute-intensive

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Spot/Preemptible Instances: 60–70% Discount With Trade-offs

Spot instances can be interrupted with 2-minute warning (AWS) or 30-second warning (GCP). This makes them:

Suitable for:

• Batch training jobs (with checkpointing every 10–15 minutes)
• Offline inference batch processing
• Data preprocessing and experimentation

Not suitable for:

• Real-time inference APIs
• Interactive applications
• Long training runs without robust checkpointing

Best practice: Use spot for training (checkpoint frequently), use on-demand or reserved for inference serving.

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FAQ

What GPU do I need to run Llama 3 70B?

Llama 3 70B requires approximately 140GB of VRAM in full precision (BF16). Options: 2× A100 80GB (160GB total), or use quantisation (Q4/Q8) to run on a single A100 80GB (with Q4 reducing VRAM to ~35–40GB). For inference, a single A100 80GB with 8-bit quantisation delivers ~80–120 tokens/second throughput.

What is the cheapest way to get GPU compute in India?

RunPod and Vast.ai offer the lowest rates globally — accessible from India with USD payment. E2E Networks is the best Indian provider for regulatory or latency reasons, offering A100 instances at competitive INR rates. For short experimental runs, Google Colab Pro ($10/month) provides T4/A100 access that beats hourly rates at low volumes.

How do I estimate GPU hours needed to fine-tune a model?

Rule of thumb for LoRA fine-tuning on a 7B model: 1–4 hours on a single A10G for a dataset of 10,000–100,000 examples. Full fine-tuning: 8–24 hours on 2–4× A100 for the same dataset. Scale linearly with dataset size and roughly 10× for each 10× model size increase.

Is it worth buying a GPU vs. renting cloud compute?

For consistent workloads above 4–6 hours/day: purchasing may be cost-effective over 3 years. An RTX 4090 (24GB VRAM, ~$1,500–$2,000 USD / ₹1.25–₹1.65 lakh) amortised over 3 years = ~₹3,700–₹4,600/month. Equivalent cloud A10G: ₹50,000–₹60,000/month. For Indian developers doing serious AI work: owning a 4090 pays back in 3–4 months vs. cloud.