NVIDIA GPU Comparison for AI/ML

Choose A30 for cost-effective inference of small-to-medium models, or A100 for high-throughput inference of large models. Here's how they compare for inference:

A30 (24GB HBM2) - Inference Optimized:

• Memory: 24GB HBM2 - sufficient for models up to ~30B parameters with quantization
• Power: 165W TDP - lowest power consumption for efficient inference
• Cost: €0.85/hour - 2.5x cheaper than A100
• INT8 performance: Excellent for quantized inference workloads
• Availability: 1x, 2x, 4x, 8x configurations for scaling

A100 (80GB HBM2e) - High-Performance Inference:

• Memory: 80GB HBM2e - supports models up to ~175B parameters with quantization
• Power: 300W TDP - higher throughput per GPU
• Cost: €2.15/hour - premium performance
• FP16/BF16 performance: Faster for half-precision inference
• Availability: 1x, 2x, 4x, 8x configurations for large-scale serving

Performance Comparison for Common Models:

Small models (7B parameters, e.g., Llama 2 7B, Mistral 7B):

• A30: 40-60 tokens/second with INT8 quantization - excellent cost efficiency
• A100: 80-120 tokens/second with FP16 - faster but overkill for this size
• Recommendation: A30 (2.5x lower cost with sufficient performance)

Medium models (13-30B parameters, e.g., Llama 2 13B):

• A30: 20-35 tokens/second with INT8/INT4 quantization
• A100: 50-80 tokens/second with FP16 or INT8
• Recommendation: A30 for cost-sensitive deployments, A100 for low-latency requirements

Large models (70B parameters, e.g., Llama 2 70B):

• A30: Requires 4x GPUs with aggressive quantization (INT4) - challenging
• A100: 1-2 GPUs with INT8 quantization - practical
• Recommendation: A100 (simpler deployment, better performance)

Very large models (175B+ parameters, e.g., GPT-3 scale):

• A30: Not recommended - insufficient memory per GPU
• A100: 2-4 GPUs with INT8 quantization
• Recommendation: A100 only option

When to choose A30:

1. Cost optimization: Inference-only workloads with budget constraints
2. Small-to-medium models: 7B-30B parameter models with quantization
3. Batch inference: High-throughput, lower-latency-tolerance workloads (e.g., content generation, summarization)
4. Production inference: Deploy multiple A30 GPUs for horizontal scaling (€0.85/hour each)
5. Energy efficiency: 165W TDP minimizes power costs for sustained inference

When to choose A100:

1. Large models: 70B+ parameter models requiring high memory capacity
2. Low-latency inference: Real-time chatbots, code assistants requiring sub-second response
3. Mixed workloads: Fine-tuning + inference on the same infrastructure
4. Future-proofing: Support larger models as your product scales
5. Premium services: High-performance inference for paying customers

Cost-performance analysis (Llama 2 13B inference):

• A30: ~30 tokens/second @ €0.85/hour = 35 tokens/second per €1/hour
• A100: ~70 tokens/second @ €2.15/hour = 33 tokens/second per €1/hour
• Result: Similar cost efficiency, but A100 provides lower latency per request

Scaling strategy:

• Horizontal scaling with A30: Deploy 4x A30 (€3.40/hour total) for distributed inference across multiple models or users
• Vertical scaling with A100: Deploy 1x A100 (€2.15/hour) for single large model with high throughput

For most inference workloads serving models under 30B parameters, A30 provides the best cost efficiency. Choose A100 when you need to serve large models (70B+) or require maximum throughput per GPU.

Choose H100 for cutting-edge performance and large-scale training, or A100 for proven reliability and cost-effective training/inference. Here's how they compare:

Performance Comparison:

H100 (80GB HBM3):

• FP8 Tensor Cores: 4x faster AI training than A100 (with Transformer Engine)
• Memory bandwidth: 3.35 TB/s (vs 2 TB/s A100) - 67% faster data throughput
• Architecture: Hopper (2022) with 4th-gen Tensor Cores
• Power: 700W TDP - highest performance per watt for FP8 workloads
• Multi-GPU: NVLink 4.0 with 900 GB/s inter-GPU bandwidth

A100 (80GB HBM2e):

• FP16/BF16 Tensor Cores: 3rd-gen proven for production training
• Memory bandwidth: 2 TB/s - excellent for most AI workloads
• Architecture: Ampere (2020) - battle-tested in production
• Power: 300W TDP - better power efficiency for sustained workloads
• Multi-GPU: NVLink 3.0 with 600 GB/s inter-GPU bandwidth
• Availability: Available in 1x, 2x, 4x, 8x configurations

When to choose H100:

1. Large language model training: >70B parameter models benefit from FP8 Tensor Cores
2. Cutting-edge research: Experiments requiring absolute maximum performance
3. Time-sensitive training: Reduce training time by 2-4x compared to A100
4. Large batch inference: High-throughput inference with FP8 optimization
5. Single GPU deployment: 1x H100 configuration only (Leafcloud)

When to choose A100:

1. Multi-GPU training: Scale from 1x to 8x GPUs for flexible configurations
2. Cost optimization: Starting at €2.15/hour vs €3.45/hour for H100 (39% lower cost)
3. Proven workloads: Production training with FP16/BF16 precision
4. Power constraints: 300W TDP vs 700W TDP - better for sustained workloads
5. Inference workloads: A100 provides excellent inference performance at lower cost

Real-world scenarios:

Training a 70B LLM:

• H100: ~12 days with FP8 mixed precision (Transformer Engine)
• A100 8x: ~25 days with BF16 mixed precision
• Cost: H100 faster but single GPU limits scalability; A100 8x more flexible for large training runs

Inference (GPT-3.5 scale):

• H100: ~60 tokens/second with FP8 TensorRT-LLM optimization
• A100: ~35 tokens/second with FP16 TensorRT-LLM optimization
• Cost: A100 may be more cost-effective for lower-latency requirements

Fine-tuning (LoRA on 13B model):

• Both GPUs handle this easily - A100 provides better cost efficiency

Leafcloud pricing (on-demand):

• H100 (1x): €3.45/hour - maximum performance
• A100 (1x): €2.15/hour - proven reliability and flexibility
• A100 (8x): Available for multi-GPU training at scale

For most AI workloads, A100 provides the best balance of performance, cost, and flexibility. Choose H100 when you need absolute maximum performance and FP8 optimization for specific workloads.

The NVIDIA RTX 6000 Blackwell has 96GB of GDDR7 ECC memory per GPU with 1,800 GB/s memory bandwidth.

Why 96GB matters for AI workloads:

Large Language Models (LLMs): Run large models in a single GPU with high memory capacity:

• Large parameter models with quantization: 70B models with 4-bit quantization fit comfortably
• Medium models in full precision (FP16/BF16): 30-40B parameter models run smoothly
• Multi-GPU scaling: 2 GPUs = 192GB, 4 GPUs = 384GB total VRAM for even larger models
• Multimodal models: Large vision-language models requiring significant context windows

Comparison to other GPUs:

• H100 (80GB): 20% more memory per RTX 6000 GPU, plus newer Blackwell architecture
• A100 (80GB): Similar capacity, but RTX 6000 has newer architecture with GDDR7
• A30 (24GB): 4x less memory - limited to smaller models or aggressive quantization

Memory bandwidth (1,800 GB/s): Critical for inference throughput. Higher bandwidth means faster token generation for LLMs and better performance for batch inference.

ECC (Error-Correcting Code): Enterprise-grade reliability - detects and corrects memory errors during long-running training or inference jobs.

Practical implications: With 96GB GDDR7 memory per GPU and Blackwell architecture, the RTX 6000 offers excellent value for production inference workloads, balancing capacity, performance, and cost efficiency. Scale from 1 to 4 GPUs based on model size requirements.

Choose RTX 6000 Blackwell for cost-effective inference with newer architecture and more memory, or H100 for maximum training throughput and FP8 optimization. Here's how they compare:

RTX 6000 Blackwell (96GB GDDR7) - Inference Focused:

• Architecture: Blackwell (2024) - newest generation with 5th-gen Tensor Cores
• Memory: 96GB GDDR7 per GPU (20% more than H100)
• Memory bandwidth: 1,800 GB/s per GPU
• Power: ~300W TDP (estimated) - more efficient than H100 for inference
• Cost: €2.76/hour on-demand (€2.35/hour with commitment) - 20% cheaper than H100
• Availability: 1x, 2x, 4x configurations (available now)
• Best for: Inference, fine-tuning, multimodal AI, production deployments

H100 (80GB HBM3) - Training and Inference:

• Architecture: Hopper (2022) - 4th-gen Tensor Cores
• Memory: 80GB HBM3 per GPU
• Memory bandwidth: 3.35 TB/s per GPU (1.86x faster than RTX 6000)
• Power: 700W TDP - highest performance density for training
• Cost: €3.45/hour on-demand - premium performance
• Availability: 1x configuration only (Leafcloud)
• Best for: Large-scale training, FP8 optimization, cutting-edge research

Key Differences:

Memory Capacity:

• RTX 6000 Blackwell: 96GB per GPU = supports larger models per GPU
  • Example: Run Llama 3 70B with less aggressive quantization
  • Multi-GPU: 2x = 192GB, 4x = 384GB total VRAM
• H100: 80GB per GPU = industry-proven capacity
  • Example: Run Llama 2 70B with INT8 quantization

Memory Bandwidth:

• H100: 3.35 TB/s = faster data throughput for training
• RTX 6000 Blackwell: 1,800 GB/s = sufficient for inference, slower for training

Architecture Generation:

• RTX 6000 Blackwell: Newer 5th-gen Tensor Cores (2024)
• H100: 4th-gen Tensor Cores (2022)

When to choose RTX 6000 Blackwell:

1. Inference workloads: Serving large language models (70B-405B parameters) with vLLM or TensorRT-LLM
2. Cost optimization: 20% cheaper than H100 (€2.35/hour committed vs €3.45/hour H100)
3. Memory-intensive models: Larger batch sizes or longer context windows (96GB vs 80GB)
4. Multi-GPU inference: Scale to 4x GPUs (384GB total) for very large models
5. Fine-tuning: LoRA/QLoRA fine-tuning of 70B+ models
6. Production deployments: Power-efficient inference for sustained workloads

When to choose H100:

1. Large-scale training: Training models from scratch (not just fine-tuning)
2. FP8 optimization: Workloads leveraging Transformer Engine for FP8 training
3. Maximum bandwidth: Memory-bandwidth-bound workloads requiring 3.35 TB/s
4. Proven at scale: Battle-tested in production for 2+ years

Real-world comparison (Llama 3 70B inference):

• RTX 6000 Blackwell: ~50-70 tokens/second @ €2.35/hour (committed)
• H100: ~60-80 tokens/second @ €3.45/hour
• Cost efficiency: RTX 6000 Blackwell provides ~95% of H100 performance at 32% lower cost

Real-world comparison (Fine-tuning 70B model with LoRA):

• RTX 6000 Blackwell: Supports full fine-tuning with 96GB memory, sufficient bandwidth
• H100: Faster fine-tuning due to higher memory bandwidth (3.35 TB/s)
• Cost: RTX 6000 Blackwell 32% cheaper for overnight fine-tuning runs (€2.35/hour committed vs €3.45/hour H100)

Multi-GPU scenarios:

• RTX 6000 Blackwell Quad Pro (4x GPUs): 384GB total VRAM @ €11.04/hour on-demand
  • Deploy 405B parameter models with quantization
• H100 (1x GPU only): 80GB @ €3.45/hour
  • Single GPU limits scalability for very large models

Recommendation:

• For inference and fine-tuning: RTX 6000 Blackwell offers better value with newer architecture, more memory, and lower cost
• For large-scale training: H100 provides faster training throughput with higher memory bandwidth
• For production deployment: RTX 6000 Blackwell is the new default for inference workloads, VM included

Leafcloud offers RTX 6000 Blackwell now in Amsterdam with configurations from 1x to 4x GPUs, providing cost-effective inference infrastructure with EU sovereignty.

The NVIDIA RTX 6000 Blackwell is NVIDIA's 5th-generation professional GPU for AI and HPC workloads, launched in 2024-2025 as part of the Blackwell architecture family.

Key specifications:

• 96GB GDDR7 ECC memory per GPU: High-capacity VRAM for large models and batch sizes
• 1,800 GB/s memory bandwidth: High data throughput for inference-heavy workloads
• 5th-generation Tensor Cores: Optimized for FP8, FP16, and INT8 inference with 2x throughput over Hopper architecture
• PCIe Gen5 interface: High-speed connectivity for data center deployment

Comparison to H100:

• Memory: 96GB vs 80GB (20% more capacity per GPU)
• Newer architecture: Blackwell (2024) vs Hopper (2022)
• Better FP8 support: Native FP8 Tensor Cores for efficient inference
• Lower power per TFLOP: More efficient for sustained workloads

Enterprise features:

• ECC memory (error-correcting code) for data integrity
• Multi-GPU configurations: Scale from 1 to 4 GPUs (96GB to 384GB total VRAM)
• Professional driver support and long-term availability
• Validated for AI frameworks (PyTorch, TensorFlow, JAX, vLLM, TensorRT-LLM)

Ideal workloads: LLM inference (large parameter models), model fine-tuning, multimodal AI, video processing at scale, HPC simulations, scientific computing requiring high memory capacity.

Leafcloud configurations:

Three configurations available starting from €2.35/hour with commitment (€2.76/hour on-demand):

• Blackwell Pro (1 GPU): 32 vCPU, 256GB RAM, 2TB NVMe - €2.76/hour on-demand (€2.35/hour with commitment)
• Blackwell Duo Pro (2 GPUs): 64 vCPU, 512GB RAM, 4TB NVMe - €5.52/hour on-demand
• Blackwell Quad Pro (4 GPUs): 128 vCPU, 1TB RAM, 8TB NVMe - €11.04/hour on-demand

Available now on Leafcloud infrastructure in Amsterdam, Netherlands. Commitment discounts available for 6, 12, and 36-month terms.

The RTX 6000 Blackwell is optimized for workloads requiring high memory capacity (96GB per GPU) and efficient inference with Blackwell architecture. Scale from 1 to 4 GPUs based on your needs. Ideal use cases:

AI Inference (Production):

• LLM serving: Deploy large language models (70B+ parameters) with vLLM or TensorRT-LLM for chatbots, content generation, code assistants
• Multimodal AI: Vision-language models (CLIP, Flamingo), text-to-image (Stable Diffusion XL), image understanding
• Real-time inference: Low-latency applications requiring consistent sub-second response times
• Batch inference: High-throughput workloads processing thousands of requests per hour
• Multi-GPU scaling: Deploy 405B+ parameter models with Blackwell Duo Pro (2 GPUs) or Quad Pro (4 GPUs)

Model Fine-tuning & Training:

• Fine-tune large models (70B+) on domain-specific data with LoRA/QLoRA
• Train mid-to-large models (7B-70B) from scratch
• Experiment with model architectures in single or multi-GPU setups

Video & Media Processing:

• Real-time video encoding/transcoding with GPU-accelerated FFmpeg
• AI video upscaling and enhancement (4K/8K workflows)
• Live streaming pipelines with Apache Kafka + GPU processing
• Broadcast-quality media production

Computer Vision:

• Object detection and tracking at scale (surveillance, autonomous systems)
• Image processing pipelines (medical imaging, satellite imagery)
• Real-time visual AI (manufacturing quality control, retail analytics)

Scientific Computing & HPC:

• Climate modeling and weather forecasting
• Molecular dynamics simulations (drug discovery, materials science)
• Financial modeling (risk analysis, options pricing)
• Genomics and bioinformatics (sequence alignment, protein folding)

When to choose RTX 6000 Blackwell over H100: The RTX 6000 Blackwell offers newer Blackwell architecture with 96GB GDDR7 memory per GPU (20% more than H100), making it ideal for inference workloads requiring high memory capacity and bandwidth. For pure training throughput, H100 remains strong, but RTX 6000 Blackwell excels for inference, fine-tuning, and cost-efficient deployment at €2.35/hour with commitment (€2.76/hour on-demand), VM included.

Leafcloud offers NVIDIA H100, A100, A30, and RTX 6000 Blackwell GPUs in Amsterdam.

NVIDIA H100 (80GB HBM3): Flagship datacenter GPU with 700W TDP. Perfect for large language model training, multi-modal AI, and high-performance inference. Available in 1x GPU configuration. Starting at €3.45/hour.

NVIDIA A100 (80GB HBM2e): Proven workhorse for ML training and HPC workloads. 300W TDP with exceptional performance-per-watt. Available in 1x, 2x, 4x, and 8x GPU configurations for scaling. Starting at €2.15/hour.

NVIDIA A30 (24GB HBM2): Cost-effective inference GPU. 165W TDP makes it ideal for production inference workloads and smaller models. Available in 1x, 2x, 4x, and 8x GPU configurations. Starting at €0.85/hour.

RTX 6000 Blackwell (96GB GDDR7): Next-generation Blackwell architecture with 96GB GDDR7 memory per GPU. Available now. Built for running large language models efficiently with exceptional memory bandwidth. Available in 1x, 2x, and 4x GPU configurations (Pro, Duo Pro, Quad Pro). Ideal for inference workloads and model deployment. Starting from €2.35/hour with commitment (€2.76/hour on-demand), VM included.

All GPUs support Kubernetes orchestration via Gardener, OpenStack provisioning, and Terraform deployment. Flexible pricing with hourly on-demand rates and commitment discounts available for 6, 12, and 36-month terms.