vLLM + NVIDIA
by vLLM Project · open-source · Last verified 2026-03-17
vLLM's NVIDIA backend leverages CUDA kernels, FlashAttention-2, and PagedAttention to deliver state-of-the-art throughput for LLM inference on NVIDIA A100, H100, and H200 GPUs. The integration supports tensor and pipeline parallelism across multiple GPUs, FP8/FP16/BF16 quantization, and CUDA graph capture for minimal per-token latency.
https://vllm.ai ↗B+
B+—Good
Adoption: AQuality: A+Freshness: A+Citations: B+Engagement: F
Specifications
- License
- Apache-2.0
- Pricing
- open-source
- Capabilities
- paged-attention, continuous-batching, tensor-parallelism, fp8-quantization, openai-compatible-api
- Integrations
- nvidia-a100, nvidia-h100, huggingface-hub, ray
- Use Cases
- high-throughput-serving, multi-gpu-inference, production-llm-api, batch-inference
- API Available
- Yes
- Tags
- inference, nvidia, gpu, tensor-parallelism, high-throughput
- Added
- 2026-03-17
- Completeness
- 100%
Index Score
72.1Adoption
85
Quality
93
Freshness
92
Citations
78
Engagement
0
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