Memory fit
Choose enough VRAM for model weights, runtime state and workload overhead.
GPU cloud for
LLM fine-tuning.
Fine-tuning requirements vary dramatically between full-parameter training and parameter-efficient methods. Size the GPU configuration around the actual training recipe.
Measured speed
Evaluate latency and throughput under conditions that resemble production.
Total workload
Consider CPU, system memory, storage and GPU count alongside the accelerator.
01 / SIZINGThere is no universal best GPU.
Size for what
you actually run.
The useful question is not simply which GPU is fastest. It is which configuration satisfies your workload at an acceptable cost.
- Identify full fine-tuning versus parameter-efficient training
- Account for parameters, gradients and optimizer state
- Choose batch size and sequence length deliberately
- Measure step time, memory use and total training cost
02 / GPU OPTIONSUseful starting points, not blanket recommendations.
NVIDIA A100
Established training accelerator with 40–80 GB memory classes.
NVIDIA H100
80 GB Hopper-class compute for demanding training.
NVIDIA H200
141 GB memory class for larger training workloads.
NVIDIA B200
Blackwell-class compute for high-end AI training.
BURSTDOCK BENCHMARKS
Validate with measurements.
GPU selection should ultimately be validated against your model, framework and traffic pattern. BurstDock publishes benchmark results only when they have actually been measured under documented conditions.
Benchmark methodology