Memory fit
Choose enough VRAM for model weights, runtime state and workload overhead.
GPU for
AI inference.
Choose inference compute by the performance your application needs, not a generic GPU ranking. Model size, precision, context and batching can materially change the result.
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.
- Fit model weights and runtime state in memory
- Set latency targets before optimizing throughput
- Test realistic prompt and output lengths
- Measure at the concurrency your application expects
02 / GPU OPTIONSUseful starting points, not blanket recommendations.
NVIDIA L40S
48 GB option for generative AI and mixed accelerated workloads.
NVIDIA H100
80 GB for demanding model-serving workloads.
NVIDIA H200
141 GB for memory-intensive inference.
NVIDIA B200
180 GB Blackwell-class accelerator.
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