Selecting the right GPU for LLM is very important for fine-tuning or inference, performance and efficiency. Here we explore some crucial factors like model size, precision levels, techniques, and batching to optimise GPU utilisation. In this blog we compare GPUs for LLMs with NVIDIA T4,L40s, RTX A600 or H600 models.
Comparison Table of different GPUs for LLM
Here is a side by side comparison for different GPUs for LLM.
| Feature | NVIDIA T4 | NVIDIA L40S | RTX A6000 | NVIDIA H200 |
| Architecture | Turing | Ada Lovelace | Ampere | Hopper |
| Relative Cost | Low | Medium–High | Medium | Very High |
| GPU Category | Entry-level data center | Inference-optimized data center | Workstation / server | Enterprise data center |
| VRAM Capacity | 16 GB GDDR6 | 48 GB GDDR6 ECC | 48 GB GDDR6 ECC | 80–141 GB HBM3e |
| Memory Bandwidth | Low | High | High | Extremely high |
| Tensor Core Support | FP16, INT8 | FP16, FP8, INT8 | FP16, TF32 | FP16, FP8, TF32 |
| Power Consumption (TDP) | ~70 W | ~350 W | ~300 W | 700 W+ |
| Deployment Type | Cost-efficient inference | Production inference | Development & research | Enterprise / hyperscale |
| Recommended Model Size | ≤ 7B (quantized) | 13B–70B | 7B–30B | 70B+ |
| Primary Use Case | Budget LLM serving | High-throughput LLM inference | LLM development & testing | Training & hyperscale inference |
| LLM Training | Not suitable | Limited | Moderate | Ideal |
| LLM Inference | Small models | Medium–large models | Medium models | Large-scale |
| Fine-Tuning | – | Limited | Good | Excellent |
| Multi-GPU Scaling | – | Limited | NVLink | NVSwitch / HGX |
When to Choose Each NVIDIA GPU
The models below have demonstrated great capabilities in natural language processing tasks, ranging from text generation and translation to sentiment analysis and question answering. But, the impressive performance of LLMs comes at a cost, they demand major computational resources, especially during the inference phase.
NVIDIA T4 – Best Budget Inference Option
NVIDIA T4 is an affordable inference at scale, small models and mixed precision production serving. Comes with low power and 16 GB memory.
Pros
- Power efficient and cost effective to operate.
- Ideal for high throughput INT8/FP16 microservices, inference and edge servers.
Cons
- Has only 16GB VRAM which may limit maximum context length and model size.
- Has older architecture, lower raw tensor throughput than the new hopper based GPUs.
Ideal for production inference from small to medium LLMs, low power deployments or massively parallel low latency endpoints.
NVIDIA L40S – Ideal for Medium or Large Inference
NVIDIA L40S is a best all rounder for large model inference and interactive generative tasks that are tuned transformer throughput with 48 GB ECC and higher tensor performance.
Pros
- Comes with 48GB ECC GDDR6 and very high tensor throughput tuned for transformer workloads (Ada Lovelace based, improved tensor cores).
- Specially optimized for generative AI inference and dense LLM serving, offers great FP8/INT8 performance.
Cons
- High power draws are more expensive than T4, still not as dominant for multi-GPU training as Hopper class servers.
- It is not a built in training GPU like H200 for large scale distributed training.
Perfect for single GPU or small cluster inference for large LLMs, GPU hosts that offer strong throughput for real-time generation.
NVIDIA RTX A600
NVIDIA RTX A600 is a workstation grade GPU that comes with 48GB and NVLink, great for researcher/dev workflows, model development and medium scale fine tuning.
Pros
- NVIDIA RTX A600 comes with 48GB ECC with NVLink where multiple GPUs can be pooled – Great for debugging, dev, medium fine tuning.
- High FP32 performance and broad software or driver support for compute and visualization.
Cons
- Workstation thermal/power profile comes with 300w which is designed for servers and desks, not hyperscale training racks.
- For large production clusters, data center GPUs with transformer engine tuning with L40s and H200 often outperform.
Ideal for model development, research and fine tuning prototypes, experiments that need a single large framebuffer and workstation tools.
NVIDIA H200
NVIDIA H200 is heavy duty data center training and large scale inference that comes with massive memory, FP8, SXM/PCIe options. Choose for serious cluster training and throughput.
Pros
- Comes with hopper class design, massive memory, tensor FLOPS, FP8 / Transformer engine, high bandwidth meant for training large transformers models and dense inference.
- It is available in HGX/HGX like systems for multi-GPU scale.
Cons
- Expensive and requires specialised servers (SXM or Specific PCIe platforms and power cooling.
- Not suitable for small teams or low scale production interference
Ideal for distributed training of large models, production inference at hyperscale where latency throughput balances needs for superior tensor performance.
How to Choose The Ideal GPU for LLM
- If you have budget constraints, then T4 is a great inference for quantized and small to medium LLMs.
- If you are looking for the best single GPU inference value for latest LLMs. L40s 48GB with Ada-tensor tuning makes it an ideal choice for production generative workloads.
- If you are looking for workflows/fine tuning on a workstation: RTX A6000 48, NVLink balances memory and dev tooling.
- For hyperscale inference and heavy training, H200/Hopper class systems are great if you can invest in top of the line cluster performance.
Conclusion
The choice of NVIDIA GPU for your LLM inference project is a strategic decision that directly impacts your AI’s performance and efficiency. While the above GPUs offer excellent value for various workloads. By carefully considering your specific needs, utilizing and complementing them with the right software tools, and optimizing your deployment, you can build a powerful and cost-effective AI infrastructure. Also, staying up-to-date with the latest GPU advancements is necessary to ensure your language AI remains competitive in the long run.
FAQ’s
What GPU is recommended for AI?
| Workload Type | Suitable GPUs |
| Training small models (7B parameters) | L4, A100, RTX 4070 |
| High-throughput inference serving | H200, B200, H100 |
| Development and experimentation | V100, A100, RTX 4090 |
| Computer vision and image processing | L40S, H200, RTX 5090 |
Can I use LLM without any GPU?
Yes you can run them locally with only RAM and CPU, you may require GGUF model files , you can use raw Llama. cpp or KoboldCpp, the latter is recommended. However, it is way too slower than a GPU in most cases.
Is the LLM better on RAM or GPU?
If you are looking for maximum performance and scalability, GPUs are the clear choice for running LLMs. Running LLMs in RAM on CPUs is more accessible, especially for smaller-scale projects and hobbyists. But for experimentation or cost-conscious apps, CPU plus RAM setups is still recommended. However, there are big trade-offs in speed.
What are the advantages of NVIDIA GPUs?
NVIDIA GPUs have the ability to perform huge amounts of calculations simultaneously, which makes them perfect for the computationally demanding tasks in AI. GPUs significantly improve the training of AI models, which enables researchers and developers to iterate on models faster and unlock breakthroughs in AI capabilities.
