Measured LLM API uptime: 99.3%  ·  5+ hrs downtime per month

How much is your team
wasting on OOM crashes?

Every No available memory for cache blocks and CUDA out of memory is GPU time you paid for and got nothing back.

All numbers sourced. Methodology at bottom.

Set $0 to see GPU idle cost only · typical B2B: $0.01–$0.05

$

RunPod community cloud pricing, April 2026 · runpod.io/gpu-pricing

vLLM / SGLang / TGI instances active simultaneously

150

Avg throughput when endpoint is healthy

1010,000

Measured LLM API uptime: 99.3% = 5.1 hrs downtime/month · universal.cloud

Time from crash to endpoint back online (restart + root-cause)

5 min6 hrs

Monthly cost of OOM downtime

$0
■ GPU idle: — ■ Revenue lost: —

—

—

Monthly breakdown

OOM incidents —
Total downtime —
Revenue lost (requests dropped × rev/req) —
Requests dropped —
GPU idle cost (you pay even when down) —

Annualised, if nothing changes

$0

What changes with ml-memguard

Without

—

—

With ml-memguard

—

2 incidents · 2 min MTTR

Monthly savings —
Annualised savings —
How does ml-memguard get here? ▾
→ Fewer incidents (your value → 2/month) — preflight checks estimate memory before launch and reject configs that would OOM. Most crashes never start.
→ Shorter MTTR (your value → 2 min) — VLLMWatchdog detects the OOM exit, selects a safe config from its bandit policy, and relaunches automatically. No pager wake-up.
→ KV cache monitor — polls KV cache usage every 5 s. When usage exceeds 95% for 3 consecutive ticks, triggers a planned graceful restart before a crash — further shrinking downtime.

Know your safe max_num_seqs before you launch.

ml-memguard calculates the largest batch that fits your GPU budget and monitors KV cache pressure at runtime — before it takes your endpoint down.

$ pip install ml-memguard[vllm]
# One line replaces all the trial-and-error:
safe = guard_vllm(llm)
# safe.max_num_seqs → guaranteed to fit
View on GitHub →

Open source · works with vLLM, SGLang, and any OpenAI-compatible server.

RunPod community cloud pricing, April 2026 · runpod.io/gpu-pricing

Memory estimators are wrong 42.6% of the time on avg · LLMem, IJCAI 2024

GPU time billed before the OOM kills the job — serious fine-tuning runs for hours

5 min12 hrs

All GPUs billed during the failed run — 7B–70B LoRA typically needs 4–8

164

GPU spend wasted per month

$0

paid for compute that returned zero results

Monthly breakdown

OOM crashes —
GPU-hours burned (all GPUs) —
Restart & reprovision overhead —
Engineer time lost —

Annualised, if nothing changes

$0

Stop guessing batch size. Know before you launch.

ml-memguard estimates memory before your job starts and monitors pressure at runtime — for Unsloth, HuggingFace Trainer, and mlx_lm.

$ pip install ml-memguard
# Works with Unsloth, HuggingFace, mlx_lm
View on GitHub →

Open source · Apache 2.0 license.

Documented OOM pain — real sources

$12,000

burned in a single month — 3–4 OOM crashes per experiment before a working config was found

— Alloc Labs post-mortem

42.6%

average error rate across existing GPU memory estimation tools — the best available solutions are wrong nearly half the time

— LLMem, IJCAI 2024

99.3%

measured LLM API uptime in production = 5.1 hours of unplanned downtime per month per endpoint

— universal.cloud, 2025

How we calculate this

Inference serving — monthly cost

gpu_idle_cost = incidents × (downtime_min / 60) × gpu_rate × servers
requests_dropped = incidents × (downtime_min / 60) × req_per_hr
revenue_lost = requests_dropped × revenue_per_request
total = gpu_idle_cost + revenue_lost

GPU prices: RunPod community cloud (April 2026). Downtime baseline: 99.3% measured LLM API uptime = 5.1 hrs/month, split into ~4 incidents of ~75 min each.

Training — monthly GPU waste

crashes_per_month = oom_freq_per_week × 4.33
gpu_hrs_wasted = crashes × (crash_min / 60) × num_gpus
gpu_cost = gpu_hrs_wasted × gpu_rate
reprovision_overhead: 18 min per crash (measured avg)
engineer_time: 35 min per crash (investigation + relaunch)

Memory estimator error rate: LLMem (IJCAI 2024, arxiv.org/abs/2404.10933). Reprovision and engineer time from practitioner reports.

GPU pricing sources

  • RunPod community cloud, April 2026: runpod.io/gpu-pricing
  • IntuitionLabs H100 comparison, 2026: $1.49–$6.98/hr across 15+ providers
  • Lambda Labs: $2.99/hr (H100 SXM)
  • CoreWeave: $6.16/hr (H100 SXM, bundled node pricing)

This calculator uses RunPod community cloud as the floor. Enterprise providers (AWS p4d, GCP A100, Azure NDv4) run 2–5× higher.