AI Workspace
May 20, 2026
AI projects often raise a similar question: how much GPU do you really need at the current stage? Shakti AI Workspace VM gives you two powerful alternatives for answering that question – run your AI workloads on NVIDIA H100 or NVIDIA L40S. Each is built to excel at a different stage of the AI lifecycle, so the real opportunity is matching the right GPU to your workload and getting the best performance for every rupee you spend. Picking the wrong one either burns budget on power you don’t need or leaves you compute-starved for the model you’re trying to build. Here’s how to tell which one fits your workload.
The two GPUs at a glance:
| Feature | H100 SXM GPU | L40S GPU |
|---|---|---|
| Available configurations | 1x, 2x, 4x H100 | 1x, 2x L40S |
| GPU memory | 80 GB HBM3 per GPU | 48 GB GDDR6 per GPU |
| Best for | SLM training, fine-tuning, HPC | LLM inference, RAG, computer vision, Digital Twin |
| Large language models | Ideal for 30B–70B+ models, distributed workloads | Ideal for 7B–32B inference and fine-tuning |
| Training performance | Very high | Moderate |
When is L40S the right call?
NVIDIA L40S VMs are the workhorse for the vast majority of day-to-day enterprise AI work. If you’re running proof of concepts, building RAG applications, doing computer vision or image generation, serving production AI APIs, or fine-tuning models in the 7B–13B parameters range, L40S delivers excellent performance.
It is also the better starting point if you’re not yet sure how much compute your workload will need long-term. With Shakti Workspace VM, a single L40S virtual machine enable teams to prototype, validate, and iterate at a fraction of the cost before committing to heavier infrastructure. For multi-user AI applications, video analytics, and medium-scale inference where 48 GB of GDDR6 comfortably fits the model and batch size, L40S is not a compromise, it’s the optimised choice.
When is H100 the better choice?
NVIDIA H100 comes into its own when you’re running workloads that demand serious compute and memory capacity : training models from scratch, distributed fine-tuning across multiple GPUs, advanced NLP and multimodal AI, or any Small language model in the 30B–70B+ parameter range. The 80 GB of HBM3 memory per GPU means larger models and batch sizes fit without the memory juggling that smaller GPUs force you into, and NVLink interconnect on the 2x and 4x configurations gives you genuinely fast GPU-to-GPU communication upto 900 Gb/sec, not just more GPUs bolted together.
This matters most for distributed training and fine-tuning models, where inter-GPU bandwidth directly determines how efficiently your workload scales. If you’re doing AI research, or small to medium model training where every hour of GPU time compounds, NVIDIA H100’s very high training performance can help you complete these workloads faster and more efficiently. It’s also simply the only option once your model size exceeds what a 48 GB card can hold.
A practical way to decide
Rather than asking ‘which GPU is more powerful?’, ask yourself three questions:
- Are you training or inferencing? Training and distributed fine-tuning favour NVIDIA H100. Inference, RAG, and most production APIs run efficiently on L40S.
- How big is your model? Under 32B parameters, L40S handles it well. Above 30B, especially 70B+, you’re in H100 territory, and likely need the 2x or 4x NVLink configurations.
- What stage are you at? Early experimentation, POCs, and small-to-medium fine-tuning jobs are better suited to L40S economics. Production training pipelines and research at scale justify H100’s premium.
Many teams don’t pick one and stop there. A common pattern on Shakti Cloud is prototyping and fine-tuning smaller models on L40S, then scaling up to H100 for full training runs or when model size outgrows what L40S memory can support, without switching platforms or re-architecting the workflow.
Shakti AI Workspace VM: Flexible GPU Compute for Every Stage of AI
Whichever GPU your workload points to, Shakti AI Workspace VM is built so the decision doesn’t lock you in. Both H100 and L40S are available as dedicated monthly plans , so you can match the commitment to how certain you are about your workload.
Configurations scale in place: 1x, 2x, or 4x H100 with NVLink, or 1x or 2x L40S, so growing from a single-GPU fine-tuning job to a distributed training run doesn’t mean re-architecting your setup, just resizing it. Every VM ships with unlimited free ingress and egress, GUI-based self-service provisioning, and enterprise-grade RBAC, multi-tenancy, and data residency compliance, regardless of which GPU you pick.
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