# Which compute instance for AI models training and inference? Choosing the right GPU instance is crucial to the success of your AI projects. An optimal configuration not only improves performance, but also keeps costs under control. This guide will help you navigate through the various options available to find the ideal solution for your needs. ## Why is the compute instance choice so important? Your choice of GPU infrastructure has a direct impact on..: * **Performance**: model training speed and inference latency * **Cost**: optimizing your budget by avoiding over-sizing * **Scalability**: ability to scale according to your needs * **Reliability**: stability of your workloads in production ## Which compute instance to choose for model training? ### 1. Large Language Models (LLMs) models training Fine-tuning Large Language Models represents one of the most resource-intensive tasks in modern AI development. The hardware requirements vary significantly based on model size, from smaller 7B parameter models to massive 70B+ architectures. This section will help you select the optimal configuration for your fine-tuning project, ensuring efficient resource utilization while maintaining performance.

Model Size	Server type	VRAM	Recommended Offers
Small (less than 7B parameters)	VM	From 22 GB (for 1B parameters models) to 140 GB (to fine-tune models such as DeepSeek-R1 7B)	1 to 3B models: 👉 The most cost-effective: 1xA100 80G 👉 The most efficient: 1xH100 80G 7B models: 👉 1xH200 141 GB
Medium (12B-32B)	VM or Bare-Metal	From 200 to 500 GB (to fine-tune models such as DeepSeek-R1 32B)	12 to 14B models: 👉 2xH200 141 GB 27 to 32B models: 👉 8xA100 80G Bare Metal or 4xH200 141 GB
Large (70B and more)	Bare-Metal	More than 1000GB	👉 8xH200 141 GB Bare-Metal 👉 8xB200 192 GB Bare-Metal

Model Size

Server type

VRAM

Recommended Offers

Small (less than 7B parameters)

From 22 GB (for 1B parameters models) to 140 GB (to fine-tune models such as DeepSeek-R1 7B)

1 to 3B models:
👉 The most cost-effective: 1xA100 80G

👉 The most efficient:

1xH100 80G
7B models:
👉 1xH200 141 GB

Medium (12B-32B)

VM or Bare-Metal

From 200 to 500 GB (to fine-tune models such as DeepSeek-R1 32B)

12 to 14B models:
👉 2xH200 141 GB
27 to 32B models:

👉 8xA100 80G Bare Metal
or 4xH200 141 GB

Large (70B and more)

Bare-Metal

More than 1000GB

👉 8xH200 141 GB Bare-Metal
👉 8xB200 192 GB Bare-Metal

### 2. Computer Vision models training Whether you're developing object detection systems, processing medical imagery, or creating next-generation AI art, selecting the right GPU infrastructure is crucial for your success. The computational requirements for vision tasks vary significantly based on complexity and scale. Classification tasks might require modest GPU power, while advanced generative models demand substantial computational resources. This section outlines three primary categories of computer vision workloads - classification, segmentation, and generation - each with its unique hardware requirements and optimal configurations.

Use Case	Models example	Resource intensity	Batch
Classification (images, videos): object detection, face recognition	ResNet, YOLO	⭐️⭐️⭐️	32-64
Segmentation (pixel-level): medical imaging, satellite analysis	U-Net, DeepLab	⭐️⭐️⭐️⭐️	16-32
Generative models (stable diffusion): image generation, AI art	Stable Diffusion	⭐️⭐️⭐️⭐️⭐️	8-16

#### Classification Models

Data Volume	Training Time	Recommended Instance	Type	Comment
<100GB	<24h	1xL40S 48 GB	VM	Perfect for development and testing
100-500GB	1-3 days	1xH100 80GB	VM	Parallel processing beneficial
500GB-1TB	3-7 days	2xL40S 48 GB	VM/BM	Higher throughput needed
>1TB	>1 week	8xH100 80 GB	BM	Bare Metal for optimal performance

#### Segmentation Models

Data Volume	Training Time	Recommended Instance	Type	Comment
< 200GB	< 48h	2x H200 141 GB	VM	High-res image processing
200GB-1TB	3-5 days	4x A100 80GB	VM	Multiple batch processing
1TB-5TB	1-2 weeks	8x H100 80 GB	BM	Heavy data augmentation
> 5TB	> 2 weeks	8x H200 80 GB	BM	Maximum processing power

#### Generative Models

Data Volume	Training Time	Recommended Instance	Type	Notes
< 500GB	< 3 days	4x H100 80 GB	VM	Model fine-tuning
500GB-2TB	3-7 days	8x H100 80 GB	BM	Full model training
2TB-10TB	1-3 weeks	8x H200 141 GB	BM	Large scale training
> 10TB	> 3 weeks	16x H100 80GB	BM	Distributed training

### 3. Audio/Speech models training Whether you're developing voice recognition systems, building text-to-speech applications, or exploring the cutting edge of AI music generation, selecting the right GPU infrastructure is crucial for successful model training. This section outlines three primary categories of audio ML workloads - speech recognition, text-to-speech synthesis, and audio generation - each requiring specific hardware configurations to achieve optimal performance. | Use Case | Input Data Type | Dataset Size | Model Examples | Resource Intensity | | ------------------ | --------------------------------------------- | ------------ | ------------------------------- | ------------------ | | Speech Recognition | Audio files (.wav, .mp3), Labeled transcripts | 100GB-1TB | Whisper, DeepSpeech, Wav2Vec | ⭐⭐⭐ | | Text-to-Speech | Text corpus, Audio pairs | 50-500GB | Tacotron, FastSpeech, VALL-E | ⭐⭐⭐⭐ | | Audio Generation | Audio samples, MIDI files | 1-2TB | MusicLM, AudioLDM, Stable Audio | ⭐⭐⭐⭐⭐ | #### Speech Recognition Models

Model Size (param)	VRAM/RAM Needed	Recommended Instance	Type	Comment
Small (< 100M params)	24GB/64GB	1x A6000 48GB	VM	Development/testing
Medium (100M-500M)	48GB/128GB	2x L40S 48GB 1x H100 80GB	VM	Production training
Large (500M-1B)	96GB/256GB	4x L40S 48GB	VM/BM	Large scale training
Very Large (>1B)	160GB/384GB	8xH100 80GB	BM	Enterprise scale

#### Text-to-Speech Models

Model Size	VRAM/RAM Needed	Recommended Instance	Type	Notes
Small (< 200M params)	80GB/192GB	2x H100 80GB	VM	Basic TTS
Medium (200M-500M)	160GB/384GB	4xH100 80GB	VM	Multi-speaker
Large (500M-1B)	320GB/768GB	8x H100 80GB	BM	High-quality TTS
Very Large (>1B)	640GB/1TB	8x H200 141GB	BM	Enterprise TTS

## Which compute instance to choose for model inference? ### 1. Large Language Models (LLMs) inference Whether you're serving chatbots, content generation, or text analysis applications, choosing the right infrastructure is crucial for balancing performance, cost, and user experience. The requirements for LLM inference vary significantly based on several key factors: model size (from 7B to 70B+ parameters), user load (from individual testing to thousands of concurrent users), and latency requirements (from real-time chat applications to batch processing). Each of these factors directly impacts your choice of infrastructure, from single GPU instances to distributed multi-GPU deployments. #### LLM Inference Sizing - Small Scale (1-50 concurrent users)

Model Size	Concurrent Users	VRAM/RAM needed	Latency Target	Recommended Instance	Type	Estimated RPS*	Comment
7B	1-10	16GB/32GB	< 100ms	1x RTX4090	VM	15-20	Development/testing
7B	11-25	24GB/64GB	< 100ms	1x L40S	VM	30-40	Small production
7B	26-50	48GB/128GB	< 100ms	1x H100	VM	60-80	Medium production
13B	1-10	24GB/64GB	< 150ms	1x RTX4090	VM	10-15	Development/testing
13B	11-25	48GB/128GB	< 150ms	1x H100	VM	25-35	Small production
13B	26-50	80GB/192GB	< 150ms	2x H100	VM/BM	50-70	Medium production
70B	1-10	80GB/192GB	< 200ms	2x H100	VM/BM	5-8	Small production
70B	11-25	160GB/384GB	< 200ms	4x H100	BM	15-20	Medium production
70B	26-50	320GB/768GB	< 200ms	8x H100	BM	35-45	Large production

#### LLM inference Sizing - Medium Scale (51-200 concurrent users)

Model Size	Concurrent Users	VRAM/RAM	Latency Target	Recommended Instance	Type	Estimated RPS*	Notes
7B	51-100	48GB/128GB	< 100ms	2x L40S	VM	100-120	Production
7B	101-150	80GB/192GB	< 100ms	2x H100	VM/BM	150-180	High-performance
7B	151-200	160GB/384GB	< 100ms	4x H100	BM	200-240	Enterprise scale
13B	51-100	160GB/384GB	< 150ms	4x H100	BM	80-100	Production
13B	101-150	240GB/512GB	< 150ms	4x H200	BM	120-150	High-performance
13B	151-200	320GB/768GB	< 150ms	8x H100	BM	160-200	Enterprise scale
70B	51-100	480GB/1TB	< 200ms	8x H200	BM	60-80	Production
70B	101-150	640GB/1.5TB	< 200ms	16x H100	BM	90-120	High-performance
70B	151-200	800GB/2TB	< 200ms	16x H200	BM	140-180	Enterprise scale

#### LLM Inference Sizing - Large Scale (201-1000+ concurrent users)

Model Size	Concurrent Users	VRAM/RAM	Latency Target	Recommended Instance	Type	Estimated RPS*	Notes
7B	201-500	320GB/768GB	< 100ms	8x H100	BM	300-400	Enterprise scale
7B	501-1000	640GB/1.5TB	< 100ms	8x H200	BM	600-800	High-scale production
7B	1000+	1.2TB/2.5TB	< 100ms	16xH200	BM	1000+	Distributed clusters
13B	201-500	480GB/1TB	< 150ms	8x H200	BM	250-350	Enterprise scale
13B	501-1000	800GB/2TB	< 150ms	8x H200	BM	500-700	High-scale production
13B	1000+	1.6TB/3TB	< 150ms	16x H200	BM	800+	Distributed clusters
70B	201-500	1.2TB/2.5TB	< 200ms	16x H100	BM	200-300	Enterprise scale
70B	501-1000	2TB/4TB	< 200ms	24x H200	BM	400-600	High-scale production
70B	1000+	3TB/6TB	< 200ms	32x H200	BM	700+	Distributed clusters

### 2. Image Generation Inference Sizing Deploying image generation models like Stable Diffusion for production introduces unique infrastructure challenges compared to traditional ML workload. The hardware requirements vary significantly based on three key factors: model complexity (from base models to SDXL with refiners), concurrent user load (affecting batch processing and queue management), and image generation parameters (resolution, steps, and additional features like ControlNet or inpainting). Each of these factors directly impacts your choice of infrastructure and can significantly affect both performance and operational costs. #### Small scale (1-50 concurrent users)

Model Type	Concurrent Users	VRAM/RAM	Latency Target*	Recommended Instance	Type	Images/Minute**	Notes
SD XL Base	1-10	16GB/32GB	< 3s	1x RTX4090	VM	15-20	Development/testing
SD XL Base	11-25	24GB/64GB	< 3s	1x L40S	VM	30-40	Small production
SD XL Base	26-50	48GB/128GB	< 3s	1x H100	VM	60-80	Medium production
SD XL + Refiner	1-10	24GB/64GB	< 5s	1x L40S	VM	10-15	Development/testing
SD XL + Refiner	11-25	48GB/128GB	< 5s	1x H100	VM	25-35	Small production
SD XL + Refiner	26-50	80GB/192GB	< 5s	2x H100	VM or BM	50-70	Medium production

#### Medium scale (51-200 concurrent users)

Model Type	Concurrent Users	VRAM/RAM	Latency Target*	Recommended Instance	Type	Images/Minute**	Notes
SD XL Base	51-100	160GB/384GB	< 3s	4x H100	VM or BM	120-150	Production
SD XL Base	101-150	320GB/768GB	< 3s	8x H100	BM	200-250	High-performance
SD XL Base	151-200	480GB/1TB	< 3s	8x H200	BM	300-350	Enterprise scale
SD XL + Refiner	51-100	320GB/768GB	< 5s	8x H100	BM	100-130	Production
SD XL + Refiner	101-150	480GB/1TB	< 5s	8x H200	BM	180-220	High-performance
SD XL + Refiner	151-200	640GB/1.5TB	< 5s	8x H200	BM	250-300	Enterprise scale

#### Large scale (201-1000+ concurrent users) | Model Type | Concurrent Users | VRAM/RAM | Latency Target\* | Recommended Instance | Type | Images/Minute\*\* | Notes | | --------------- | ---------------- | --------- | ---------------- | ------------------------------------------------ | ---- | ----------------- | ------------------- | | SD XL Base | 201-500 | 800GB/2TB | < 3s | [**16x H100**](https://www.sesterce.com/booking) | BM | 400-500 | Multi-cluster | | SD XL Base | 501-1000 | 1.6TB/4TB | < 3s | [**16x H200**](https://www.sesterce.com/booking) | BM | 800-1000 | Distributed system | | SD XL Base | 1000+ | 2.4TB/6TB | < 3s | [**24x H200**](https://www.sesterce.com/booking) | BM | 1500+ | Global distribution | | SD XL + Refiner | 201-500 | 1.2TB/3TB | < 5s | [**24x H100**](https://www.sesterce.com/booking) | BM | 350-450 | Multi-cluster | | SD XL + Refiner | 501-1000 | 2TB/5TB | < 5s | [**32x H200**](https://www.sesterce.com/booking) | BM | 700-900 | Distributed system | | SD XL + Refiner | 1000+ | 3TB/8TB | < 5s | [**40x H200**](https://www.sesterce.com/booking) | BM | 1200+ | Global distribution |