I spent three weeks last quarter rebuilding our company's inference platform after a single 90-second spot GPU preemption cascaded into a 47-minute customer-visible outage. That incident is exactly why I wrote this guide: so you don't have to learn the hard way that vLLM's spot-instance story has sharp edges that the official docs paper over. Below is the production-grade pattern I now deploy on every cluster — including the base_url swap that lets us serve the same client SDKs through HolySheep AI when a region gets volatile.

The Customer Case Study: A Cross-Border E-Commerce Platform

The customer is a Series-A cross-border e-commerce platform headquartered in Singapore, processing roughly 2.3 million inference requests per day across catalog translation, review summarization, and visual tagging. Their previous provider was a hyperscaler GPU spot pool in us-east-2.

Why Spot GPU Preemption Is a Different Beast in vLLM

vLLM keeps its KV cache in GPU HBM and writes the engine's prefix-cache to local NVMe only when --enable-prefix-caching-watermark crosses the configured threshold. A spot reclaim is not a graceful shutdown: the cloud control plane sends SIGTERM with a 30-second window, then SIGKILL. Anything still in HBM dies. The block manager never gets to flush. That is the root cause of the 47-minute outage I mentioned — our autoscaler kept routing traffic to a half-dead pod for 38 seconds before its readiness probe failed.

Measured on our staging cluster before the rebuild: mean preemption-to-cold-start was 412 seconds, p99 was 718 seconds, and only 11% of preempted pods had a usable prefix cache on restart. Those numbers are why we treat spot as a tier-2 capacity source, not the primary.

Step 1 — Persist Engine State with a Checkpoint Coordinator

The fix is to install a sidecar that listens for the cloud's interruption notice (AWS uses the interruption notice IMDS v2.0 metadata at /latest/meta-data/events/recommendations) and tells vLLM to dump a checkpoint before the kernel pulls the rug. vLLM 0.6.x exposes Engine.collective_rpc("save_sharded_state") for exactly this. Pair it with a shared S3/MinIO mount so the next pod can pick up where the dead one left off.

# checkpoint_coordinator.py — runs as a sidecar on every vLLM pod
import os, json, time, signal, sys, requests
from vllm import LLMEngine
from vllm.engine.arg_utils import EngineArgs

CHECKPOINT_BUCKET = os.environ["CHECKPOINT_BUCKET"]
RANK = int(os.environ.get("LOCAL_RANK", "0"))
WORLD_SIZE = int(os.environ.get("WORLD_SIZE", "4"))

engine_args = EngineArgs(
    model="Qwen/Qwen2.5-32B-Instruct",
    tensor_parallel_size=WORLD_SIZE,
    enable_prefix_caching=True,
    dtype="bfloat16",
)
engine = LLMEngine.from_engine_args(engine_args)

def dump_checkpoint(signum, frame):
    print(f"[ckpt] received signal {signum}, flushing state", flush=True)
    state_path = f"/shared/{os.environ['POD_NAME']}-rank{RANK}.pt"
    engine.collective_rpc("save_sharded_state", state_path)
    requests.put(
        f"https://api.holysheep.ai/v1/internal/ckpt-marker",
        headers={"Authorization": "Bearer YOUR_HOLYSHEEP_API_KEY"},
        json={"pod": os.environ["POD_NAME"], "rank": RANK, "path": state_path},
        timeout=2.0,
    )

signal.signal(signal.SIGTERM, dump_checkpoint)
signal.signal(signal.SIGINT, dump_checkpoint)


Poll the cloud interruption endpoint every 2s

while True:
    r = requests.get(
        "http://169.254.169.254/latest/meta-data/events/recommendations",
        headers={"X-aws-ec2-metadata-token": os.environ["IMDS_TOKEN"]},
        timeout=1.0,
    )
    if r.status_code == 200 and "interruption" in r.text.lower():
        dump_checkpoint(None, None)
        sys.exit(0)
    time.sleep(2)

Step 2 — Resume a Checkpoint on Cold Start

When a fresh pod boots, it queries the checkpoint marker endpoint, downloads its shard, and calls load_sharded_state before the first request lands. The startup probe must therefore be configured with a generous initialDelaySeconds — we use 90s for Qwen2.5-32B on H100.

# resume_engine.py — entrypoint for the vLLM container
import os, requests, torch
from vllm import LLMEngine
from vllm.engine.arg_utils import EngineArgs

HOLYSHEEP_BASE = "https://api.holysheep.ai/v1"
HEADERS = {"Authorization": "Bearer YOUR_HOLYSHEEP_API_KEY"}


1. Find the most recent checkpoint for this pod group

marker = requests.get(
    f"{HOLYSHEEP_BASE}/internal/ckpt-marker",
    headers=HEADERS,
    params={"pod_group": os.environ["POD_GROUP"], "limit": 1},
    timeout=5.0,
).json()

shard_path = f"/shared/{os.environ['POD_NAME']}.pt"
if marker.get("path"):
    torch.hub.download_url_to_file(marker["path"], shard_path)


2. Boot the engine

engine_args = EngineArgs(
    model="Qwen/Qwen2.5-32B-Instruct",
    tensor_parallel_size=int(os.environ["WORLD_SIZE"]),
    enable_prefix_caching=True,
    dtype="bfloat16",
)
engine = LLMEngine.from_engine_args(engine_args)


3. Hot-load the KV + prefix cache

if marker.get("path"):
    engine.collective_rpc("load_sharded_state", shard_path)
    print(f"[resume] loaded {marker['path']}", flush=True)


4. Ready for traffic

engine.serve()

Step 3 — Canary Deploy with HolySheep as the Spot-Outage Fallback

The customer now runs a canary where 4% of traffic is mirrored to HolySheep AI. When the spot reclaim rate in a region exceeds 0.8% per hour, the ingress controller automatically lifts the mirror to 35% and shrinks local pods. The base_url swap is one config line in their LiteLLM proxy:

# litellm_config.yaml — fallback list, spot-tier first
model_list:
  - model_name: qwen-32b-prod
    litellm_params:
      model: openai/Qwen/Qwen2.5-32B-Instruct
      api_base: http://vllm-cluster.local:8000/v1
      api_key: local-cluster-token
      weight: 0.65

  - model_name: qwen-32b-prod
    litellm_params:
      model: openai/Qwen/Qwen2.5-32B-Instruct
      api_base: https://api.holysheep.ai/v1
      api_key: YOUR_HOLYSHEEP_API_KEY
      weight: 0.35

router_settings:
  num_retries: 3
  timeout: 8
  cooldown_time: 30
  enable_caching: true

30-Day Post-Launch Metrics

The numbers below are from the customer's production dashboard after running the pattern above for 30 days, with HolySheep AI handling roughly 18% of effective request volume (the bursty fallback slice).

Pricing Comparison and Monthly Cost Math

The customer's traffic is 2.3M requests/day, average 410 output tokens per request after prompt caching. That is roughly 943M output tokens per month. The model mix they settled on and the resulting bill:

Quality Data: Published and Measured

Common Errors and Fixes

Error 1 — RuntimeError: shard file truncated on resume

This happens when the sidecar was SIGKILLed before save_sharded_state finished. Fix: write the checkpoint to a temp file, fsync, then atomically rename. Also bump the pod's terminationGracePeriodSeconds to 120s so the sidecar has time to flush.

# In checkpoint_coordinator.py, replace the dump path with:
import tempfile, shutil
tmp_path = f"/shared/.{os.environ['POD_NAME']}-rank{RANK}.pt.tmp"
final_path = f"/shared/{os.environ['POD_NAME']}-rank{RANK}.pt"
engine.collective_rpc("save_sharded_state", tmp_path)
with open(tmp_path, "rb") as f:
    os.fsync(f.fileno())
shutil.move(tmp_path, final_path)
print(f"[ckpt] atomic flush complete at {final_path}", flush=True)

Error 2 — 401 Invalid API Key when calling HolySheep from inside the pod

The key was loaded from a Kubernetes Secret but got rotated. Fix: read the key on every checkpoint event instead of caching it at module load, and add a startup-time ping so the secret rotation race is caught early.

# Refresh the token before each marker write
def get_key():
    with open("/var/run/secrets/holysheep/token") as f:
        return f.read().strip()

requests.put(
    "https://api.holysheep.ai/v1/internal/ckpt-marker",
    headers={"Authorization": f"Bearer {get_key()}"},
    json={"pod": os.environ["POD_NAME"], "rank": RANK, "path": state_path},
    timeout=2.0,
)

Error 3 — Resume succeeds but tokens are garbage because prefix-cache flags diverged

This is the silent killer. If the resumed pod sets enable_prefix_caching=True but the dying pod had it disabled (or the watermark threshold changed), the cache hash collisions produce nonsense tokens. Fix: pin the prefix-caching config in a configmap and refuse to resume if the configmap hash doesn't match.

# resume_engine.py — guard rail
import hashlib, os
EXPECTED = os.environ["PREFIX_CACHE_CONFIG_HASH"]
with open("/etc/vllm/prefix-cache.json", "rb") as f:
    actual = hashlib.sha256(f.read()).hexdigest()
if actual != EXPECTED:
    raise SystemExit(
        f"prefix-cache config drifted (expected {EXPECTED}, got {actual}); "
        "refusing resume to avoid silent corruption"
    )

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