Memory fit
Choose enough VRAM for model weights, runtime state and workload overhead.
GPU cloud for
Llama models.
Run Llama-family workloads on GPU compute sized for the actual model, numerical precision, context length and traffic pattern.
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.
- Account for model weights and numerical precision
- Leave headroom for KV cache and runtime overhead
- Test context length and concurrent requests
- Measure tokens per second and cost for your serving stack
02 / GPU OPTIONSUseful starting points, not blanket recommendations.
NVIDIA H100
80 GB Hopper-class compute for demanding inference and training.
NVIDIA H200
141 GB memory class for larger or memory-heavy workloads.
NVIDIA B200
Blackwell-class compute for high-end AI workloads.
NVIDIA A100
Established Ampere-class option for compatible AI stacks.
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