KV Cache Memory Growth
KV cache memory grows with sequence length in transformer inference and some training setups, causing OOM at long contexts.
KV cache memory grows with sequence length in transformer inference and some training setups, causing OOM at long contexts.
What this failure is
KV Cache Memory Growth is a Memory failure seen during ML training runs. KV cache memory grows with sequence length in transformer inference and some training setups, causing OOM at long contexts. Common tags: Kv Cache, Inference, Long Context, Transformer.
Is this what broke your run? Paste your log.
You're reading about KV Cache Memory Growth. Paste your own crash log or traceback below and get the real root cause for YOUR run, not this generic entry. No account, no card. Logs are masked at ingress and never saved to account history.
Want 14 days on the Scale plan?
Request an evaluation code. A verified workplace organization activates up to 50 diagnoses a day, alerts, history, and follow-up questions. No credit card or automatic subscription.
Why it happens (the mechanism)
KV cache stores K and V for all layers and all positions. Standard MHA uses 2 * num_layers * hidden_size. GQA reduces cache size. Sliding window attention limits cache growth. Taken together, these mechanisms explain why the failure is reproducible, why it tends to surface on specific workloads or scales, and why generic mitigation attempts often fall short without addressing the underlying cause.
What you'll observe
- Memory grows with sequence length
- OOM at long context lengths
- KV cache uses more memory than model weights
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| Memory is O(L * H * D) per token for KV cache | KV cache stores K and V for all layers and all positions |
| OOM with long context | Standard MHA uses 2 * num_layers * hidden_size |
| Memory grows linearly with batch * sequence length | GQA reduces cache size |
Which systems are affected
- LLM inference with long context
- Training with very long sequences
- Multi-query or grouped-query attention
How to confirm this is the problem
Use this checklist to test the hypothesis against a small reproduction. No single line proves the root cause, so preserve the preceding events and compare one variable at a time.
- ✓Reproduce the failure from a clean checkpoint/seed: the symptom must appear without warm-up state from a previous run.
- ✓Verified signal present: Memory is O(L * H * D) per token for KV cache
- ✓Verified signal present: OOM with long context
- ✓Verified signal present: Memory grows linearly with batch * sequence length
- ✓A targeted fix from the "How to fix it" section eliminates or substantially reduces the symptom within one validation pass.
The fix and the prevention pattern
The root cause is on this page and stays free. A free account adds the exact remediation steps, saved history, and the fix on every entry in the encyclopedia.
Sign up free. Unlock the full analysisNo credit card. Daily allowance follows verified trust tier. Instant access.
Diagnose this failure in VS Code
Select the traceback or open the failed terminal, then run Denpex locally to see the initiating rank, collateral failures, exact fix, and verification command without uploading the log.
Install the free VS Code extensionRelated failures to investigate next
Root cause
- KV cache stores K and V for all layers and all positions
- Standard MHA uses 2 * num_layers * hidden_size
- GQA reduces cache size
- Sliding window attention limits cache growth
The fix and how to prevent it
Evaluate Denpex on your own logs
Request a Scale evaluation code. A verified workplace organization activates 14 days with up to 50 diagnoses a day. Every account keeps its current diagnosis allowance and gets a verification path. No card or automatic subscription.
Don't just read the fix, diagnose your run
The encyclopedia tells you what went wrong. Denpex tells you what went wrong in YOUR training run. With your logs, your config, and your stack.