Best GPU for LLM Inference in India
Find the best GPU for serving large language models in India. VRAM requirements, throughput optimization, and cost-per-token analysis for LLM inference deployments.
VRAM Requirements for Large Language Model (LLM) Inference / Serving
Minimum VRAM
24 GB
Recommended VRAM
48 GB
Recommended GPUs
Key Considerations
- VRAM determines the largest model you can serve. A 7B model in FP16 needs approximately 14 GB; quantised to INT4, it fits in 4 GB. The L40S (48 GB) handles most models up to 30B parameters.
- Memory bandwidth drives token generation speed. For auto-regressive LLM inference, tokens per second scales linearly with memory bandwidth. The H200 (4.8 TB/s) generates tokens significantly faster than the L40S (864 GB/s).
- Use quantisation (INT8, INT4, GPTQ, AWQ) to fit larger models on smaller GPUs and improve throughput. Modern inference engines like vLLM and TensorRT-LLM support efficient quantised inference.
- For cost-optimised inference at scale, consider the NVIDIA L40S. It offers excellent FP8/INT8 throughput at a fraction of the H100's price and fits in standard PCIe servers.
- Batch size matters for throughput. GPUs with more VRAM can batch more concurrent requests, improving cost-per-token. Calculate your expected concurrent users to determine VRAM needs.
What NOT to buy
Do not over-buy training-grade GPUs (H100 SXM with NVLink) if you only need inference. The NVLink premium is wasted on inference workloads where each GPU typically serves requests independently. Also avoid HBM-based GPUs for small models that fit in GDDR6 memory.
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