If vLLM stalls, retries forever or crashes with an assertion once KV-cache offloading is on, first work out which of three reported failure patterns you have. They are a scheduler deadlock under cache pressure, a failed read from a secondary tier, and an allocation assertion on hybrid-cache models. Each has a different trigger and needs different evidence. This guide is a synthesis of vLLM’s official documentation and three public issue reports. It is not a personal incident write-up, and every claim about a bug is tied to the version its reporter named.
How KV offloading is configured today
vLLM’s cache configuration reference defines kv_offloading_size as the offloading buffer size in GiB. Its default is None, which means KV offloading is off. When you set a size, vLLM enables CPU offloading through kv_offloading_backend. The documented backends are native and lmcache. Check the flags your installed release actually accepts before you change a production configuration, because this area changes quickly.
The KV Offloading Usage Guide (page footer dated August 9, 2026) covers tiered setups. It also documents a per-request max_offload_tokens option that caps how much of a prefix is eligible for offload, and zero disables offload for that request. The guide labels this option experimental, so treat it as version-sensitive.
Step 1: Pin the runtime
Reports from different releases are not interchangeable, so record these before you touch any settings:
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- The exact vLLM release or commit, and the Python version
- The model identifier and its architecture (full attention, or hybrid with Mamba layers)
- Hardware and runtime, and the parallelism settings
- The offloading backend,
kv_offloading_size, and any tier settings - Prefix-caching settings, the KV connector role, and speculative decoding such as MTP
- Relevant environment variables
Step 2: Classify the symptom
| Pattern | Reported symptom | Version in report | Issue |
|---|---|---|---|
| Scheduler deadlock | Engine shows Running: 0 reqs, Waiting: N reqs, zero GPU-cache usage and zero throughput |
v0.22.0 | #45388 (opened June 12, 2026) |
| Tier read-failure livelock | A request keeps retrying a promotion until aborted | Not stated here; see the report | #49176 (opened July 20, 2026) |
| Hybrid-cache assertion | EngineCore crashes on an assertion | v0.25.1 | #50454 (opened July 30, 2026) |
Scheduler makes no progress under load
Issue #45388 describes prefix caching with kv_role=kv_both and a working set larger than the GPU KV cache. The report’s setup used a 32,768-token GPU KV cache. Concurrent requests reuse prefixes that have been offloaded. The engine then reportedly stops scheduling anything. The authors reproduced it on v0.22.0 with a low-level request harness and say a precise request sequence is needed. A generic server smoke test may therefore pass on a system that still has this bug. This is one reported case, not a universal diagnosis for every stall.
A request retries a tier promotion forever
Issue #49176 describes a different mechanism. When loading from a secondary tier fails, the file is deleted. An asynchronous lookup still reports the block as present, so the same promotion is attempted again and fails again. Look at tier I/O errors, missing or truncated data, and whether the lookup state is invalidated after a failure. Capacity pressure may have nothing to do with it.
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EngineCore asserts and crashes
Issue #50454 reports an assertion on v0.25.1 with a Mamba-hybrid model, native KV offloading, prefix caching and MTP all enabled. The reporter says an earlier two-phase allocation fix was already present, yet this case still reproduced. Capture the full assertion and stack trace. Also record the cache-group layout and the speculative-decoding configuration, since hybrid groups are part of the trigger.
Step 3: Build a minimal reproduction
- Keep the trigger: same model architecture and cache groups, a fixed and small cache budget, the same backend and tier, and the same prefix-cache setting.
- Replace live traffic with a short deterministic sequence of prompt lengths and concurrent requests, so a failure can be replayed.
- Change one variable at a time: offloading off, prefix caching off, lower concurrency, MTP off. Only record a result you actually observed. Each run tells you which layer the bug lives in.
- Stop at the smallest case that still fails and save the exact commands.
Step 4: Capture the right signals
Log scheduler state, running and waiting request counts, GPU cache usage, throughput, exceptions and tier I/O messages. In a stall like the one in #45388, the key evidence is that waiting requests are non-zero while running requests and cache usage are zero. vLLM’s metrics design page lists request and GPU-cache gauges. It also notes that some CPU-swapping metrics describe legacy v0 behavior, so don’t assume an older metric measures the current v1 offloading path.
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Step 5: Search, then report
vLLM’s troubleshooting guide recommends searching existing issues before filing. It asks you to include complete environment and configuration details with a small reproduction. It also says to turn off any debugging environment variables once you have finished diagnosing, because leaving them on can slow the system.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choosing an investigation path
When the symptoms don’t clearly match one report, compare your case on these axes:
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- Failure layer: scheduler progress, tier read and lookup consistency, or allocation assertion.
- Cache topology: a single full-attention group, or hybrid and multiple groups.
- Workload trigger: concurrent pressure, a missing or corrupt offloaded block, or prefix-cache hits combined with MTP.
- Version and fix status: check whether the issue is still open for your release.
- Observability: stalled scheduler metrics, tier load errors, or an EngineCore stack trace.
No frequency or performance-impact figures for these bugs were found in the sources reviewed, so none are given here. The issue numbers and versions above describe individual reports only.
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