Scaling Open Source LLM Serving with vLLM, SGLang, and FlashAttention-3

Serving large language models at enterprise scale requires optimizing both throughput (tokens per second per GPU) and latency (time to first token). Traditional PyTorch inference implementations suffer from severe memory fragmentation due to key-value (KV) cache allocation. Frameworks like vLLM and SGLang leverage virtual memory paging and FlashAttention-3 kernels to maximize GPU compute efficiency.

1. The Memory Bottleneck: KV-Cache Fragmentation

During auto-regressive generation, the attention mechanism stores key and value tensors for all previous tokens in memory. In standard implementations, contiguous memory pre-allocation causes up to 60–80% of GPU VRAM to lie idle due to internal and external memory fragmentation.

2. PagedAttention & Continuous Batching

vLLM solves this by introducing PagedAttention, an algorithm inspired by virtual memory paging in operating systems. KV-cache tensors are stored in non-contiguous physical memory blocks, enabling dynamic block allocation and memory sharing across parallel sampling requests.

Production vLLM Server Launch Harness:

from vllm import LLM, SamplingParams

# Configure model with Tensor Parallelism across 4 GPUs
llm = LLM(
    model="deepseek-ai/DeepSeek-R1-Distill-Qwen-32B",
    tensor_parallel_size=4,
    gpu_memory_utilization=0.92,
    max_num_seqs=256,
    trust_remote_code=True
)

sampling_params = SamplingParams(
    temperature=0.6,
    top_p=0.95,
    max_tokens=1024
)

prompts = ["Explain PagedAttention virtual memory allocation in multi-GPU LLM inference."]
outputs = llm.generate(prompts, sampling_params)

for output in outputs:
    print(f"Generated text: {output.outputs[0].text}")

3. Performance Benchmarks

Deploying models with vLLM PagedAttention achieves significant throughput gains compared to naive baseline servers:

4. Operational Recommendations

• FP8 KV-Cache Quantization: Enable FP8 KV-cache quantization to double total concurrent request capacity per GPU node.
• Tensor Parallel Alignment: Match tensor parallel size directly to GPU board topology (e.g., 4 or 8 NVLink GPUs per node).