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Bare Metal Servers for AI and Machine Learning Workloads

AI inference and machine learning training workloads have specific infrastructure requirements: high core counts for parallel processing, large memory for model loading, and fast storage for dataset access. Bare metal eliminates the overhead of virtualisation — your ML workload runs on dedicated silicon without sharing resources with other tenants.

India's AI and data science sector is growing rapidly. Bagful's bare metal configurations serve Indian AI companies, ML research teams, and enterprises running LLM inference in production.

20+ years experience
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Infrastructure requirements for AI workloads

Model inference at scale requires consistent compute. A cloud instance that's "mostly" dedicated may occasionally throttle under noisy neighbour conditions — unacceptable for production inference APIs. Bare metal gives guaranteed CPU access. AMD EPYC's large L3 cache is particularly suited to LLM inference where cache-resident model weights reduce memory bandwidth pressure.

  • AMD EPYC 9354 (32 cores, 128MB L3 cache) — optimal for LLM inference
  • High memory configurations — large models require large RAM
  • NVMe storage — fast dataset loading for training pipelines
  • No virtualisation overhead — dedicated silicon execution
  • GPU configurations available for accelerated inference

Use cases Bagful serves

LLM inference API hosting (running your own Llama, Mistral, or fine-tuned model in production), ML training pipelines for medium-scale datasets, computer vision inference servers, NLP processing backends, vector database infrastructure (Weaviate, Qdrant, Milvus), and data science batch processing.

Location considerations for AI workloads

India-region for Indian AI companies with Indian data compliance requirements. Singapore for APAC AI services. US locations for models serving North American API consumers. Your advisor can recommend optimal placement based on where your inference requests originate.

Global infrastructure. India-based support.

Bagful operates bare metal infrastructure across 25 locations in the US, Europe, and Asia-Pacific. Your server runs wherever your users or compliance requirements demand. What does not change, regardless of which datacenter your hardware sits in, is who you are dealing with: a team based in India, available in IST, billing in rupees, with a phone number and WhatsApp you can actually reach.

This is the combination international providers cannot offer — global reach without the global support queue — and that pure Indian hosting companies cannot match. Bagful has operated from India for over 20 years. Our infrastructure advisors are in Mohali and Mumbai, not Manila or Dublin.

North India
Mohali, Punjab
Engineering and operations team. Dedicated server provisioning, technical support, and managed services. Primary hub for clients across Punjab, Haryana, Delhi NCR, Chandigarh, and northern India.
West India
Mumbai, Maharashtra
Sales and enterprise accounts. Serving clients across Maharashtra, Gujarat, and western India. Point of contact for infrastructure deployments, managed cloud, and enterprise contracts.

Support runs 10AM–7PM IST, Monday to Saturday. WhatsApp is available for critical infrastructure issues outside those hours. Every invoice is issued in INR with GST from Bagful International LLP — an Indian entity, not an overseas billing address.

Frequently Asked Questions

AMD EPYC 9354 (32 cores, 256GB RAM configurations) is optimal for LLM inference — large L3 cache reduces memory bandwidth pressure on large model weights. For smaller models (7B parameters and below), Intel Xeon Gold configurations are well-suited.
Yes. GPU configurations are available — ask your advisor for current GPU hardware availability and pricing. GPU stock for bare metal is limited; waitlist registration is available.
Yes. Weaviate, Qdrant, Milvus, Chroma, and other vector databases run on standard Linux. AMD EPYC's large core count and memory bandwidth suit vector similarity search workloads.
For consistent production inference, bare metal outperforms equivalent cloud instances in throughput consistency. Cloud has advantages in elasticity — scale up for training, scale down after. For steady-state production inference with predictable load, bare metal typically gives better cost-per-inference.

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