At 03:14 UTC during a routine load test of a 70-billion-parameter reasoning model across four NVIDIA L40S GPUs (48 GB GDDR6 each, 192 GB aggregate VRAM), our inference worker crashed mid-prefill with torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 1.25 GiB (GPU 2; 47.54 GiB total capacity; 35.12 GiB already allocated). Despite having over 11.8 GiB of unallocated physical memory per card according to nvidia-smi, the serving engine stalled whenever concurrent 16k-token prompts crossed 18 active sequences, while Time-To-First-Token (TTFT) spiked from 410 ms to 3,890 ms.

Two distinct hardware and runtime failure modes caused this collapse: non-contiguous CUDA virtual address reservation during unchunked prefill spikes, and PCIe Access Control Services (ACS) forcing every Tensor Parallel (tp_size=4) all-reduce ring synchronization through the dual-socket AMD EPYC host root complex instead of direct PCIe switch Peer-to-Peer (P2P) DMA. Below is the exact diagnostic workflow, kernel tuning script, and vLLM production configuration that restored P2P DMA and increased sustained token throughput by 2.4x.

1. Anatomy of the 74% VRAM OOM: Activation Spikes vs. PagedAttention Blocks

Standard transformer inference engines pre-allocate a static Key-Value (KV) cache buffer sized to max_model_len * max_num_seqs. vLLM replaces contiguous reservation with PagedAttention, partitioning remaining GPU memory into fixed-size logical KV blocks (defaulting to 16 tokens per block) mapped via a block table analogous to OS virtual memory pages. During startup, vLLM executes a dummy forward pass to profile peak activation memory and claims --gpu-memory-utilization (default 0.90) of total VRAM for the KV block pool.

Why does a worker crash with torch.OutOfMemoryError when nvidia-smi reports 26% free memory? Because PyTorch's caching allocator (cudaMallocAsync / native block allocator) separates reserved memory pools from active tensors. When three 24,000-token RAG prompts hit the scheduler in the same iteration without chunked prefill enabled, the intermediate FP16 MLP activation tensors (specifically the SwiGLU gate and up-projection buffers) require a contiguous multi-gigabyte allocation outside the pre-allocated KV cache pool.

Architecture Diagram: PCIe ACS Bottleneck vs. Direct Switch P2P + Chunked Prefill
BROKEN: ACS Enabled (CPU Root Bounce) Host CPU Root / IOMMU GPU 0 (L40S) SYS Hop: 11.4 GB/s GPU 1 (L40S) All-Reduce: 3.8ms OPTIMIZED: ACS Disabled + FP8 KV Cache PCIe Gen4 Switch (PHB/PIX) GPU 0 (L40S) P2P DMA: 28.1 GB/s GPU 1 (L40S) All-Reduce: 0.9ms

To eliminate fragmented PyTorch segments and cap transient prefill activation buffers, you must export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True prior to spawning worker processes and enforce --enable-chunked-prefill with a bounded --max-num-batched-tokens ceiling.

2. Auditing PCIe Topology and Disabling ACS P2P Redirection

Unlike NVIDIA H100 SXM5 nodes interconnected via 900 GB/s NVLink switches, PCIe-based accelerator servers (L40S, RTX 6000 Ada, A100 PCIe) rely on PCIe Gen4/Gen5 x16 lanes (31.5 GB/s unidirectional bandwidth per Gen4 x16 link) for Tensor Parallel synchronization. Every transformer layer requires two all-reduce collective operations—meaning a 80-layer model executes 160 all-reduce barriers per generated token.

Run nvidia-smi topo -m and nvidia-smi topo -p2p r to verify whether your GPUs communicate directly across the PCIe switch (PIX or PXB) or traverse the host CPU interconnect (SYS): 

# 1. Inspect physical topology matrix and P2P read status
nvidia-smi topo -m
nvidia-smi topo -p2p r

# Example degraded output when PCIe ACS is enabled in BIOS/OS:
#   GPU0  GPU1  GPU2  GPU3
# GPU0   X    SYS   SYS   SYS
# Status: CNS (Chipset Not Supported / ACS Redirect Active)

When CNS or SYS appears between cards sharing the same physical PCIe switch, Linux IOMMU and PCIe Access Control Services (ACS) are intercepting peer-to-peer transactions and routing them up to the CPU root port for address translation. On bare-metal Ubuntu 22.04/24.04 nodes, disable ACS redirection across all PCIe bridge devices with the following script before starting the CUDA container: 

#!/usr/bin/env bash
# disable_pcie_acs.sh — Run as root on host before starting vLLM containers
set -euo pipefail

for BDF in $(lspci -d "*:*:*" | awk '{print $1}'); do
  if setpci -v -s "${BDF}" ECAP_ACS+0x6.w > /dev/null 2>&1; then
    echo "[pci-tuning] Disabling ACS on PCIe bridge ${BDF}"
    setpci -v -s "${BDF}" ECAP_ACS+0x6.w=0000
  fi
done

# Verify NCCL P2P direct access
export NCCL_P2P_LEVEL=PXB
export NCCL_IB_DISABLE=1

3. Production vLLM Engine Configuration (FP8 KV Cache & Chunked Prefill)

On Ada Lovelace (L40S) and Hopper (H100) architectures, converting the KV cache from 16-bit (`bfloat16`) to 8-bit floating point (`fp8_e4m3`) cuts per-token KV memory footprint in half with less than 0.18% perplexity drift on long-context retrieval benchmarks. For Llama-3.3-70B using Grouped-Query Attention (GQA with 8 KV heads, 128 head dimension, 80 layers), a single token in `bfloat16` consumes:

2 (K+V) × 80 layers × 8 KV heads × 128 dim × 2 bytes = 327,680 bytes (320 KB/token). Across a 32,768-token context window, one session occupies 10.48 GB of KV cache in FP16, but only 5.24 GB when quantized to fp8_e4m3.

Below is the battle-tested production configuration manifest (vllm-production-config.json) and systemd/Docker launch command for a 4x L40S node: 

{
  "model": "meta-llama/Llama-3.3-70B-Instruct-FP8-Dynamic",
  "tensor_parallel_size": 4,
  "dtype": "bfloat16",
  "kv_cache_dtype": "fp8_e4m3",
  "calculate_kv_scales": true,
  "gpu_memory_utilization": 0.92,
  "max_model_len": 32768,
  "block_size": 32,
  "enable_chunked_prefill": true,
  "max_num_batched_tokens": 4096,
  "max_num_seqs": 64,
  "enable_prefix_caching": true,
  "disable_custom_all_reduce": false,
  "enforce_eager": false
}

Pair this configuration with the following container runtime environment variables to lock in expandable CUDA segments and prevent NCCL shared-memory exhaustion inside Docker: 

docker run -d --name vllm-llama70b-prod \
  --gpus all \
  --shm-size=32g \
  --ipc=host \
  --ulimit memlock=-1 \
  -p 8000:8000 \
  -e PYTORCH_CUDA_ALLOC_CONF="expandable_segments:True,max_split_size_mb:512" \
  -e NCCL_P2P_DISABLE=0 \
  -e NCCL_SHM_DISABLE=0 \
  -v /mnt/nvme0/hf_cache:/root/.cache/huggingface \
  vllm/vllm-openai:v0.7.2 \
  --model meta-llama/Llama-3.3-70B-Instruct-FP8-Dynamic \
  --tensor-parallel-size 4 \
  --kv-cache-dtype fp8_e4m3 \
  --calculate-kv-scales \
  --gpu-memory-utilization 0.92 \
  --max-model-len 32768 \
  --block-size 32 \
  --enable-chunked-prefill \
  --max-num-batched-tokens 4096 \
  --enable-prefix-caching

4. Empirical Benchmark: Baseline vs. Tuned 4x L40S Cluster

We benchmarked both configurations using vllm bench serve with a synthetic ShareGPT + long-context RAG workload (mean input length: 6,400 tokens; mean output length: 512 tokens; 32 concurrent clients over 300 seconds):

Metric (4x NVIDIA L40S 192GB) Baseline (BF16 KV, ACS On, No Chunking) Tuned (FP8 KV, ACS Off, Chunked 4096) Delta
Max Concurrent 16k Sequences 14 sessions (OOM at 18) 46 sessions (Zero OOMs) +228% capacity
Median TTFT (p50) 1,420 ms 548 ms -61.4% latency
Tail TTFT (p99) 4,890 ms 1,180 ms -75.8% latency
Inter-Token Latency (ITL p50) 42.6 ms/tok (23.4 tok/s) 18.9 ms/tok (52.9 tok/s) +126% speed
Total Output Throughput 418 tokens/sec 1,012 tokens/sec 2.42x throughput
Effective Cost per 1M Output Tokens $1.48 / 1M tokens $0.61 / 1M tokens -58.8% unit cost

5. Operational Verification Checklist

Before promoting any multi-GPU vLLM deployment to production traffic, verify three telemetry counters on the Prometheus /metrics endpoint (port 8000):

  • vllm:gpu_cache_usage_perc: Should remain below 0.88 under peak p95 load. If it pins at 0.99, preemption and recomputation events (vllm:num_preemptions_total) will stall active streams.
  • vllm:num_requests_waiting: A sustained queue above 8 indicates prefill starvation. Lower --max-num-batched-tokens from 4096 to 2048 to interleave decode steps more aggressively.
  • NCCL Ring Bandwidth: Run nccl-tests/build/all_reduce_perf -b 8M -e 512M -f 2 -g 4 on node boot and assert bus bandwidth exceeds 22.0 GB/s on PCIe Gen4 switches before registering the node in your load balancer.