📚 GPU & AI Model Selection Guide
How to Choose the Right GPU and Model
H100 vs A100 or L40S? GPT-4o vs Claude 3.5 — Which is Better? This guide helps you choose the best config for your needs.
GPU Comparison
Choose the right GPU for your training or inference needs
$2.49/hr
312 TFLOPS FP16 · 40GB HBM2
Best For
- ResNet/VGG and other classic CV model training
- BERT / small models (<1B) fine-tuning
- Single-GPU inference testing & experiments
- Stable Diffusion inference (40GB available)
- Teaching / Academic research
- Cost-sensitive small projects
Not Suitable For
- 7B+ Large model full-parameter training
- GPT-4 class model inference
- Tasks requiring multi-GPU NVLink
$6.50/hr
312 TFLOPS FP16 · 80GB HBM2e
Best For
- 7B-13B model fine-tuning (Qwen / LLaMA)
- ResNet/DenseNet and other CV model training
- Stable Diffusion XL training
- Medium-scale inference service deployment
- Whisper batch transcription
- Most research & commercial applications
Not Suitable For
- 70B+ model full-parameter training
- Tasks requiring NVLink multi-GPU acceleration
$12.50/hr
3,979 TFLOPS FP16 · 80GB HBM3 · NVLink
Best For
- 70B+ model full-parameter training (Qwen / DeepSeek)
- GPT-3.5 / GPT-4 class model inference
- 8-GPU NVLink cluster, near-linear scaling
- LLaMA 3 / Mistral large model fine-tuning
- Autonomous driving, AlphaFold, complex tasks
- All scenarios requiring high VRAM & bandwidth
Not Suitable For
- Budget-sensitive small tasks
- Old model training on single GPU only
$2.20/hr
362 TFLOPS FP8 · 48GB GDDR6
Best For
- Stable Diffusion image generation (high throughput)
- video transcoding / frame interpolation
- Small/medium model inference deployment
- Multi-GPU needed but budget limited
- Image classification / object detection inference
Not Suitable For
- Large model training requiring HBM bandwidth
- Tasks requiring >48GB VRAM
| GPU | Architecture | VRAM | Bandwidth | FP16 Performance | NVLink | Hourly Rate | Suitable Scale |
|---|
| A100 40GB PCIe | Ampere | 40GB HBM2 | 64 GB/s | 312 TFLOPS | - | $2.49 | Small (Experiment/Test) |
| A100 80GB SXM | Ampere | 80GB HBM2e | 2 TB/s | 312 TFLOPS | 600 GB/s | $6.50 | Medium (Commercial) |
| H100 SXM 80GB | Hopper | 80GB HBM3 | 3.35 TB/s | 3,979 TFLOPS | 900 GB/s | $12.50 | Large (100B+ Params) |
| H200 SXM 80GB | Hopper | 80GB HBM3e | 4.8 TB/s | 3,979 TFLOPS | 900 GB/s | $18.00 | Extra Large (Trillion Params) |
| L40S 48GB | Ada | 48GB GDDR6 | 864 GB/s | 362 TFLOPS | - | $2.20 | Medium (Inference-focused) |
| H100 8x NVLink | Hopper x8 | 640GB HBM3 | 7.2 TB/s | 31,832 TFLOPS | NVLink 4.0 | $89.00 | Ultra-scale Cluster |
AI Model Comparison
Choose the best model for your task and budget
Input $5/M · Output $15/M · Context 128K
- Multimodal (text/image/audio)
- Strongest complex reasoning, math, and code capabilities
- Best for general use cases, ready out of the box
- 128K context, ideal for long document analysis
- Excellent multilingual support (including Chinese)
Best for: Complex code/Architecture design/Advanced reasoning/Multimodal tasks
Input $3/M · Output $15/M · Context 200K
- 200K long context, ideal for large documents
- Excellent creative writing and long-text analysis
- Code capability close to GPT-4o, slightly cheaper
- Best safety, built-in helpfulness and harmlessness principles
- Haiku as fast, low-cost alternative ($0.25/$1.25)
Best for: Long-text analysis/Content creation/Code review/Academic research
Input $0.075/M · Output $0.30/M · Context 1M
- Extremely low price, best cost-performance
- 1M context, million-token level
- Video understanding support (Gemini Pro)
- Native multimodal support
- Significant cost advantage in high-concurrency scenarios
Best for: Batch data processing/Long context/Cost-sensitive applications
Input $0.27/M · Output $1.10/M · Context 64K
- Extremely strong code and math reasoning
- Leading performance among open-source models
- Highly optimized for Chinese language
- Price 20-30x lower than GPT-4o
- Supports Function Calling / Tool Use
Best for: Code generation/Math/Chinese optimization/Cost-sensitive applications
Selection Decision Tree
Answer a few questions to find your optimal config
🎮 What type of task are you working on?
LLM Training / Fine-tuning
Inference Deployment
Image / Video Generation
CV / Audio / Other
Recommended by Task Type
Click an option above to see recommendations
📈 What is your model size?
< 1B parameters
1B - 13B
13B - 70B
70B+ parameters
Recommended by Model Size
Click an option above to see recommendations
💰 What is your budget?
$2-3/hour (Budget)
$4-7/hour (Medium)
$10-15/hour (Comfortable)
$50+/hour (Large Scale)
Recommended by Budget
Click an option above to see recommendations
Configuration Checklist
Recommended hardware and software config by scenario
💻
Large Model Training (7B-70B)
- H100 SXM 80GB x 1-8
- NVLink multi-GPU cluster (8 GPUs optimal)
- PyTorch 2.x + DeepSpeed
- CUDA 12.x + cuDNN 8.x
- Distributed Training (ZeRO-2/3)
- High-speed Shared Storage (optional)
💬
LLM Inference Deployment
- H100 / A100 80GB (by concurrency)
- vLLM / TensorRT-LLM
- FP16 / INT8 Quantization
- Continuous Batching
- Reverse Proxy (Nginx / Caddy)
- Monitoring (Grafana + Prometheus)
🎨
Image / Video Generation
- A100 80GB / L40S (for SDXL)
- CUDA 12 + cuDNN
- Diffusers Library (SDXL / FLUX)
- 16GB+ VRAM (SDXL recommended)
- NVMe Local Storage (model cache)
- Batch inference: A100 more cost-effective
📊
CV Model Training
- A100 40GB/80GB (sufficient for CV)
- PyTorch / TensorFlow
- CUDA 12.x
- Data Augmentation (Albumentations)
- Distributed DataLoader
- wandb / TensorBoard monitoring
🎤
Speech / Audio Processing
- A100 40GB (sufficient for Whisper)
- Whisper Large V3
- FFmpeg Audio Preprocessing
- Batch transcription: per-minute billing is cheaper
- Subtitle generation (Whisper + timed-text)
- Triton Inference Server
🚀
Quick Experiment / POC
- A100 40GB PCIe (lowest cost)
- JupyterHub / VS Code Server
- Pre-installed PyTorch / TensorFlow
- Data upload (Object Storage)
- Pay by the hour — no charge when not in use
- Validate before scaling to production training
Frequently Asked Questions
How much VRAM for training?▼
Rule of thumb: Inference VRAM ≈ params × 2 bytes (FP16). 7B needs ~14GB, 13B needs ~26GB, 70B needs ~140GB. Training needs 2-4× more (activation memory). DeepSpeed ZeRO-3 shards across GPUs.
A100 vs H100 or L40S?▼
Check two things: VRAM and bandwidth. H100 has 80GB HBM3 (vs A100 HBM2e), 3.35TB/s bandwidth (vs 2TB/s), and 6× the compute. For 13B+ training, H100 bandwidth makes multi-GPU scaling near-linear. For ≤7B models, A100 80GB offers better value.
When is NVLink cluster worth it?▼
NVLink 8-GPU cluster ($89/hr H100) is for 70B+ full-parameter training. For 7B-13B models, 8-GPU PCIe (cheaper) gets near-NVLink efficiency. NVLink key advantage: GPU-to-GPU bandwidth (900 GB/s vs PCIe 64 GB/s), huge for large model multi-GPU training.
Hourly vs Monthly: Which is better?▼
Monthly = buying 730 hours upfront, ~35% discount. If you use >50 hours/month, monthly saves money. Enterprise/continuous use: monthly. Personal/irregular use: hourly — pay only for what you use, billed by the minute.
Model API vs Compute?▼
Use our API for: quick prototyping, no infrastructure management, predictable costs, low usage (<$1000/month). Rent compute yourself for: high usage (>$1000/month), full model control (fine-tuning/deploying your own), high data privacy needs, avoiding third-party dependency.
Not sure which one to pick?
Our technical consultant helps evaluate your needs for free — plan in 30 minutes