Best GPUs for Large Language Model Training in 2025

"Should I use H100s or will A100s work?" If you've asked this question while staring at wildly different price tags, you're not alone. Choosing the right GPU for LLM training feels like threading a needle—go too cheap and your model won't fit in memory, go too expensive and your CFO starts asking uncomfortable questions about ROI.

The truth is, there's no one-size-fits-all answer. The "best" GPU depends on your model size, whether you're doing full training or fine-tuning, your budget constraints, and how much you value your time. This guide cuts through the marketing hype to give you practical recommendations based on what actually matters.

Understanding LLM Training Requirements

Memory Requirements by Model Size and Training Method

The VRAM you need depends significantly on whether you're doing inference, fine-tuning with parameter-efficient methods, or full training:

Note: Memory requirements are estimates based on FP16/BF16 precision and can vary by framework, model architecture, and optimization techniques. Always add 20-30% buffer for safety. Pricing data is current as of December 2024.

7B Parameter Models:

• Inference only: 10-16GB
• LoRA fine-tuning (FP16): ~15GB
• QLoRA fine-tuning (8-bit): ~9GB
• Full fine-tuning (FP16): ~67GB

13B Parameter Models:

• Inference only: 20-24GB
• LoRA fine-tuning (FP16): ~28GB
• QLoRA fine-tuning (8-bit): ~17GB
• Full fine-tuning (FP16): ~125GB

30-70B Parameter Models:

• Inference only: 40-80GB
• LoRA fine-tuning (FP16): 60-146GB
• QLoRA fine-tuning (8-bit): 35-88GB
• Full fine-tuning (FP16): 180-672GB (multi-GPU required)

175B+ Parameter Models:

• Requires large-scale multi-GPU setups regardless of method
• Full fine-tuning: 1TB+ (16x-64x GPUs minimum)

Key GPU Characteristics for LLMs

1. VRAM Capacity: Primary constraint for model size
2. Memory Bandwidth: Affects training speed
3. Tensor Core Performance: Accelerates matrix operations
4. Multi-GPU Interconnect: NVLink for efficient scaling

Top GPU Choices by Model Scale

Let's get specific. Here's what you should actually rent based on your model size:

For 7-13B Parameter Models

Best Choice: RTX 4090

• VRAM: 24GB (sufficient for 13B with optimization)
• Pricing: $0.25-0.80/hr
• Pros: Best cost-performance, widely available
• Cons: Consumer GPU, no NVLink

Alternative: A100 40GB

• VRAM: 40GB (comfortable headroom)
• Pricing: $1.19-2/hr
• Pros: Data center reliability, NVLink for multi-GPU
• Cons: 2-3x more expensive than RTX 4090

Recommendation: Use RTX 4090 for fine-tuning and experimentation. A100 for production training pipelines.

For 30-70B Parameter Models

Best Choice: A100 80GB

• VRAM: 80GB per GPU
• Pricing: $0.50-4.22/hr
• Configuration: 4x-8x GPUs typical
• Pros: Proven architecture, good availability

Alternative: H100 80GB

• VRAM: 80GB per GPU
• Pricing: $1.87-7/hr
• Configuration: 4x-8x GPUs
• Pros: 2-3x faster training vs A100 (workload dependent)

For teams considering H200 as well, see our detailed H100 vs H200 comparison to understand when the premium makes sense.

Cost Analysis (illustrative example):

• Training 65B model on 8x A100: ~$20/hr, 100 hours = $2,000
• Training 65B model on 8x H100: ~$40/hr, 35 hours = $1,400

Note: Actual training times vary significantly based on dataset size, optimization techniques, and hardware configuration. Always benchmark your specific workload.

Recommendation: H100 saves money for large projects despite higher per-hour cost. A100 better for intermittent training.

For 175B+ Parameter Models

Only Choice: H100 or H200

• Configuration: 16x-64x GPUs minimum
• H100: $1.87-7/hr per GPU
• H200: $2-8/hr per GPU (141GB VRAM)
• Pros: Only viable option for this scale

Architecture Considerations:

• Requires distributed training (DeepSpeed, Megatron)
• NVLink essential for efficiency
• Infiniband networking recommended

Recommendation: H200 if you're consistently hitting 80GB limit. Otherwise H100 for better availability and cost.

Special Considerations

Fine-Tuning vs Full Training

Fine-Tuning (LoRA, QLoRA):

• Requires 50-70% less VRAM
• Can use cheaper GPUs
• RTX 4090 handles up to 30B with QLoRA
• A100 40GB comfortable for most fine-tuning

Full Pre-Training:

• Requires larger VRAM budgets
• Benefits from premium GPUs
• Multi-GPU almost always necessary

Parameter-Efficient Methods

Modern techniques dramatically reduce VRAM requirements, making larger models accessible on affordable hardware:

• QLoRA (8-bit quantization): Reduces memory by ~50% compared to LoRA

  • Example: Fine-tune 70B model in just 88GB (vs 672GB for full training)
  • Train 13B models on a single RTX 4090 (24GB)

• LoRA (Low-Rank Adaptation): ~77% VRAM reduction vs full fine-tuning

  • Example: Fine-tune 7B in 15GB (vs 67GB full training)
  • Maintains nearly identical performance to full fine-tuning

• FSDP (Fully Sharded Data Parallel): Efficient multi-GPU training

  • Shards model, gradients, and optimizer states across GPUs
  • Enables training models that exceed single-GPU capacity

These advances mean you can fine-tune a 13B model on consumer hardware or a 70B model on a single A100 80GB—tasks that would have required expensive multi-GPU setups just a year ago.

Multi-GPU Considerations

NVLink vs PCIe

• NVLink: 600GB/s (A100) or 900GB/s (H100), essential for 30B+ models
• PCIe: 64GB/s, acceptable for smaller models

Scaling Efficiency

Cloud Provider Recommendations

For Startups and Scale-Ups

• Spheron: No enterprise tax, marketplace pricing gives startups access to enterprise-grade GPUs at 50-70% lower costs
• RunPod: Good balance of cost and reliability with simple pricing
• Lambda Labs: Straightforward pricing without hidden fees

Why These Work for Startups: Traditional cloud providers charge enterprise premiums for features startups don't need. These platforms offer the same hardware without the markup.

For Enterprise Teams

• Spheron: Enterprise tier with dedicated support, maintains cost advantages
• Lambda Labs: Clean, predictable pricing for finance team approval
• CoreWeave: Large-scale multi-GPU configurations for established teams
• AWS/GCP: When compliance requirements mandate specific certifications

Cost Optimization Tips

For comprehensive cost reduction strategies, see our guide to reducing AI compute costs by 80%.

1. Use Spot Instances: 50-70% savings for interruptible training
2. Checkpoint Frequently: Enables spot usage without progress loss
3. Right-Size Model: Don't train larger than necessary
4. Consider Fine-Tuning: Often matches full training at 10% the cost
5. Batch Jobs: Rent GPUs only when actively training

Decision Framework

Step 1: Determine model size

• What parameter count do you need?
• Full training or fine-tuning?

Step 2: Calculate VRAM requirements

• Model size × 4 bytes (FP16) + optimizer states + gradients
• Add 20-30% buffer

Step 3: Choose GPU tier

• < 40GB: RTX 4090 or A100 40GB
• 40-80GB: A100 80GB

• 80GB: Multi-GPU H100/H200

Step 4: Evaluate costs across providers

• Compare real-time pricing across multiple providers
• Factor in total training time, not just per-hour rates
• Calculate total cost per experiment including all fees

Frequently Asked Questions

What GPU do I need to fine-tune a 7B model? For 7B model fine-tuning, an RTX 4090 (24GB) works perfectly with LoRA or QLoRA methods, costing just $0.25-0.80/hr. You'll need about 15GB VRAM for LoRA fine-tuning or 9GB with QLoRA. For full fine-tuning without parameter-efficient methods, you'd need an A100 40GB with its 67GB requirement.

Can I train a 70B model on a single GPU? Yes, with parameter-efficient methods. QLoRA can fine-tune a 70B model in 88GB, which fits on a single H100 or A100 80GB GPU. For full fine-tuning, you'll need multi-GPU setups (typically 4-8x H100 or A100 80GB) as it requires 280-400GB total VRAM.

Is H100 worth it over A100 for LLM training? For large projects, yes. H100 is 3x faster than A100. While H100 costs $1.87-7/hr vs A100's $0.50-4.22/hr, the training time reduction often results in lower total cost. For example, training a 65B model on 8x H100 takes ~35 hours ($1,400) vs 100 hours on 8x A100 ($2,000).

Conclusion

For most teams, the right GPU choice breaks down like this:

• 7-13B models: RTX 4090 provides unbeatable value
• 30-70B models: A100 80GB offers the best balance of reliability and cost
• 70B+ models: H100 recommended despite higher per-hour cost
• 175B+ models: H100/H200 mandatory, requires substantial budgets

Start with smaller, cheaper GPUs for experimentation. You'll learn what actually works for your use case without burning through your budget. Once your requirements crystallize—once you know exactly what model size, context length, and batch sizes you need—then scale to premium hardware. Compare pricing across multiple providers; the landscape is competitive and rates vary significantly.