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Benchmarks vs vLLM

We benchmark LLMBoost against vLLM — the popular open-source baseline — on identical hardware, models, and workloads. Higher is better; LLMBoost wins across the board.

Real workloads, MLPerf methodology

Numbers below use the OpenOrca dataset (the same data used in MLPerf Inference) on AMD Instinct™ GPUs. The tables are live — pulled from our benchmark sheet and updated regularly.

Headline results​

MetricvLLM (baseline)LLMBoost
Generation throughput1×up to

Throughput speedup​

Generation throughput speedup (LLMBoost tok/s ÷ vLLM tok/s) per model and GPU, highest first. Filter by GPU; hover a badge for the underlying tok/s.

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LLMBoost serves up to more tokens/second than vLLM for the same model on the same GPU — auto-tuned parallelism, batching, and KV-cache management turn into direct cost-per-token savings.

Where the speed comes from​

  1. Intelligent auto-tuning — parallelism + memory tuned to your exact GPU.
  2. Memory optimization — efficient KV-cache management and layout.
  3. Hardware-specific kernels — deep AMD ROCm integration.

Proof points​

  • Record MLPerf Inference v5.0, v5.1, and v6.0 results on AMD Instinct™ GPUs.

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