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Most LLM-Scaler-vLLM PV 1.5 failures become straightforward once you identify which memory pool is exhausted. A model that cannot allocate its weights needs lower precision or more parallelism; a context or batching failure needs less KV-cache demand; runtime preemption needs lower concurrency or more cache capacity; slow startup may be a storage or host-memory problem rather than a GPU problem.
Use the sequence below: classify the failure, change one control, rerun a fixed workload, and watch prefill, decode, queueing, cache utilization, and scheduler state separately. Do not raise gpu-memory-utilization blindly: it enlarges the cache reservation and can turn a marginal startup into an immediate out-of-memory error.
Start by identifying the failure class
Collect the error message, the point at which it occurs, and the runtime measurements before changing flags. LLM-Scaler-vLLM can fail while allocating model weights, while reserving KV cache, while admitting a batch, or during normal scheduling. These cases have different remedies.
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|---|---|---|
| Engine exits while loading weights or initializing CUDA | Model-weight allocation, sometimes combined with runtime buffers | Lower-precision or quantized model, higher tensor parallelism, or pipeline parallelism |
| Startup says the requested sequence length needs more KV cache than is available | Context-length reservation | Lower --max-model-len; only raise gpu-memory-utilization when VRAM is genuinely free |
| Requests are admitted but are repeatedly preempted or moved to swap | Concurrent KV-cache demand and scheduler pressure | Lower --max-num-seqs and --max-num-batched-tokens, or add safe memory/parallelism |
| Startup takes a long time before inference begins | Network or shared-file storage, model loading, or host-memory swapping | Use local model storage, check CPU memory, and test with --load-format dummy |
| Time to first token is high for long prompts | Prefill work | Reduce prompt length, improve prompt batching, or use hardware with more prefill capacity |
| Generated tokens arrive slowly after the first token | Decode bandwidth, cache pressure, or contention | Lower concurrency if the GPU is thrashing; inspect inter-token latency and cache use |
Record prompt length, output length, concurrency, model precision, tensor and pipeline parallelism, max_model_len, max_num_batched_tokens, and max_num_seqs for every run. Without that record, an apparent improvement can simply be a different workload.
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Fix model-weight allocation OOM at startup
If the model itself cannot fit, changing batching flags will not solve the initial allocation. vLLM describes the basic condition plainly: “If the model is too large to fit in a single GPU, you will get an out-of-memory (OOM) error.” Weight memory is only part of the budget; runtime buffers and KV cache also need space.
Estimate the weight footprint
NVIDIA’s sizing heuristic is:
weight_memory_per_gpu = total_parameters × bytes_per_parameter ÷ TP
TP is the tensor-parallel degree. The bytes-per-parameter assumptions are:
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|---|---|---|
| BF16 or FP16 | 2 bytes per parameter | Approximate weight storage before runtime overhead |
| FP8 | 1 byte per parameter | Approximate weight storage before runtime overhead |
| INT4 or NVFP4 | 0.5 bytes per parameter | Approximate weight storage before runtime overhead |
For example, a 70-billion-parameter BF16 model has a rough weight figure of 140 GB before buffers and KV cache. That is a planning estimate, not a guarantee of fit. Sharding across four GPUs reduces the per-GPU weight share in the formula, but each GPU still needs communication buffers, kernels, and cache space.
Choose a remedy in the right order
- Use a supported lower-precision or quantized checkpoint. Moving from BF16/FP16 to FP8 or a supported INT4/NVFP4 format reduces the weight term. Confirm that the model, GPU architecture, and LLM-Scaler-vLLM build support the chosen format; precision changes can affect quality and kernel compatibility.
- Increase tensor parallelism. Raising
TPspreads weights across more GPUs. The trade-off is synchronization overhead, which can reduce throughput or increase latency if communication becomes the bottleneck. - Use pipeline parallelism when appropriate. Pipeline parallelism can make a model fit when tensor sharding alone is unsuitable, but it adds pipeline stages and can add latency. Validate with the same prompt and concurrency mix you will serve.
- Reserve room for non-weight memory. Do not size the weights to consume every reported megabyte. KV cache, CUDA allocations, and runtime buffers must coexist with the model.
If the process fails before a server accepts requests, start with this weight analysis. Reducing --max-model-len cannot make an oversized weight allocation fit when the failure occurs before KV-cache initialization.
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Fix KV-cache and context-length OOM
After weights fit, vLLM reserves KV cache for active sequences. A long maximum context or a large batch can request more cache than remains available. Intel’s startup guidance for this condition is to increase gpu_memory_utilization or decrease max_model_len; in practice, decrease the context limit first when your application does not require the full window.
Lower the context reservation
--max-model-len is a capacity ceiling, not a promise that every request will use that many tokens. Set it to the largest prompt-plus-output context your product actually needs, with a deliberate margin for special tokens and templates. A lower ceiling reduces the amount of KV cache the engine must plan for and often lets the server start.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsDo not lower the value below real requests without an application decision: requests that exceed the configured limit will be rejected or truncated according to the serving layer’s behavior. Test the longest supported prompt, not only a short smoke test.
Control concurrent sequence demand
For concurrent traffic, also reduce:
--max-num-seqs: the maximum number of sequences scheduled together. Fewer simultaneous sequences reduce concurrent KV-cache blocks.--max-num-batched-tokens: the token budget admitted to a batch. A lower budget limits the amount of prompt and decode work competing for cache and compute in one scheduling step.
These controls address different dimensions. A short request count can still be expensive when each request has a very long context; a long queue of short requests can still pressure the scheduler when the sequence limit is high. Tune both against representative traffic.
Use gpu-memory-utilization as a capacity control
gpu-memory-utilization determines how much GPU memory the engine is willing to use for its allocations, including KV-cache capacity. Raising it can help only when other processes are not consuming VRAM and the remaining headroom is safe. If another workload already occupies memory, a higher value can cause startup OOM by enlarging the preallocation target.
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Lowering the value creates a safety margin for co-located processes, but it leaves less room for KV cache and can increase preemption. Intel examples commonly use values around 0.8–0.9; those are examples, not universal defaults. Measure free VRAM on the exact GPU and deployment, then change the value in a small step while checking whether the engine starts and whether cache pressure improves.
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Startup success does not prove that the serving configuration has enough cache for production concurrency. vLLM reports preemption when available KV-cache space is insufficient. The documented responses are to increase safe memory utilization or tensor parallelism, or to decrease max_num_seqs and max_num_batched_tokens.
Read the scheduler states
Use the monitor output to distinguish normal queueing from memory pressure:
- RUNNING means sequences are actively scheduled.
- WAITING means requests are queued for service.
- SWAPPED indicates that sequences have been moved out of active GPU execution because of memory-management pressure.
Frequent SWAPPED states point to VRAM exhaustion or an overly aggressive admission policy. Cache utilization above 90% is identified in Intel’s monitor documentation as a condition that may trigger eviction and increase latency. Treat sustained values near that level as a capacity warning rather than a target.
Pick the least costly intervention
| Intervention | VRAM effect | Throughput and latency trade-off | Compatibility and operations |
|---|---|---|---|
Lower max_num_seqs |
Reduces concurrent KV demand | May lower peak throughput but can remove thrashing and stabilize inter-token latency | No extra hardware; may increase queue time |
Lower max_num_batched_tokens |
Limits per-step token admission | Can reduce batch efficiency while preventing oversized scheduling steps | Simple flag change; benchmark prompt-heavy and decode-heavy traffic |
Lower max_model_len |
Reduces planned context capacity | Requests needing longer contexts no longer fit; shorter requests gain headroom | Requires an application-level context decision |
| Increase tensor parallelism | Spreads weights and can provide more aggregate memory | Synchronization overhead may add latency | Requires additional compatible GPUs and communication setup |
Raise gpu-memory-utilization |
Increases possible cache reservation | May improve admission until another allocation collides; can worsen startup OOM | Safe only when the GPU has verified free headroom |
When preemption disappears but queue time becomes excessive, the bottleneck has moved from GPU cache capacity to serving capacity. Add capacity or adjust admission rather than continuing to raise memory targets.
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Separate slow loading from slow engine initialization
A server that appears hung may be waiting on model files, not building the inference engine. Shared or network filesystems can make model loading slow, and host RAM pressure can force the operating system to swap.
Use local model storage
Place the model on local storage when shared or network access is the suspected bottleneck. Compare startup with the same image, model, and flags; changing storage and compute at the same time makes the result difficult to interpret.
Test without loading weights
Run the engine with --load-format dummy to test initialization without loading model weights. If dummy initialization is fast but normal startup is slow, investigate download, filesystem throughput, model-file access, or host-memory pressure. If both are slow, inspect engine configuration, GPU initialization, and container startup instead.
Check CPU memory and swap
GPU memory is not the only startup resource. Watch host RAM and swap activity while the model loads. Operating-system swapping can make file access and process initialization appear stalled even when GPU utilization is low.
Use a measurement loop that distinguishes prefill from decode
Benchmark one controlled change at a time. Keep the model revision, precision, GPU set, prompt dataset, output limit, and request concurrency fixed while testing a single flag.
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Record the workload
- Prompt and output token lengths, including their distribution rather than only averages.
- Concurrency and arrival pattern.
- Model precision and quantization format.
- Tensor-parallel and pipeline-parallel degrees.
max_model_len,max_num_batched_tokens, andmax_num_seqs.- GPU memory utilization setting and observed cache utilization.
Watch the right metrics
- Time to first token and prefill time: expose prompt-processing cost.
- Inter-token latency and decode time: expose token-generation speed after the first token.
- Token throughput: show aggregate work completed, but interpret it alongside latency.
- Queue time: reveal admission or capacity limits even when GPU utilization looks high.
- Cache utilization: identify approaching eviction and preemption.
- Scheduler state: distinguish RUNNING, WAITING, and SWAPPED behavior.
Map symptoms to actions
- Long prefill or high time to first token with long prompts: reduce prompt or context length, improve prompt batching, or move to hardware with more prefill capacity.
- Long decode or high inter-token latency: investigate memory bandwidth, cache pressure, and contention; lower concurrency if the GPU is thrashing.
- High queue time or many WAITING requests: adjust batching and concurrency or add serving capacity.
- Frequent preemption or SWAPPED states: reduce KV demand or add safe memory and parallelism.
Use vllm bench serve for repeatable serving tests. Compare p50 and tail behavior, not only a single throughput number; a configuration that reports higher aggregate tokens while producing severe inter-token stalls may be worse for interactive users.
Apply tuning changes in a safe sequence
- Confirm the failure phase. Capture the startup log or runtime scheduler evidence before editing flags.
- Fit the weights. Choose precision, quantization, tensor parallelism, or pipeline parallelism that leaves room for runtime allocations.
- Set a realistic context ceiling. Lower
--max-model-lenif the application does not need the advertised maximum. - Limit admission pressure. Reduce
--max-num-seqsand then--max-num-batched-tokenswhen concurrent KV demand causes preemption. - Adjust memory utilization only with evidence. Raise
gpu-memory-utilizationin small steps only after verifying that no other process is consuming the apparent headroom. - Re-run the fixed benchmark. Compare prefill, decode, queueing, cache use, and scheduler states.
- Check tail latency and failure rate. Keep a change only if it meets the service objective without introducing startup OOM or unacceptable queueing.
Changing several flags together hides causality. For example, a simultaneous reduction in context length and concurrency may remove preemption, but you will not know which limit was necessary or whether one can later be relaxed.
Enable diagnostic logging for hangs and crashes
Increase observability only while diagnosing a reproducible problem:
VLLM_LOGGING_LEVEL=DEBUGincreases vLLM log detail.VLLM_LOG_STATS_INTERVAL=1emits statistics at one-second intervals.CUDA_LAUNCH_BLOCKING=1makes CUDA launches synchronous, which can make the failing operation easier to locate but can slow execution.NCCL_DEBUG=TRACEexposes detailed collective-communication logs for parallel deployments.VLLM_TRACE_FUNCTION=1provides function tracing but is extremely slow; reserve it for difficult cases and disable it afterward.
Capture the logs together with GPU memory, scheduler states, and the exact command line. Remove debug settings from normal production runs unless their overhead is acceptable.
Check generation defaults after an image or model update
A change in output style or sampling behavior is not automatically a performance regression. vLLM notes that v0.8.0 and later use the model creator’s generation_config.json defaults. If behavior changed after an image or model update, compare the run with --generation-config vllm before attributing the difference to batching, memory, or parallelism.
Keep generation settings constant during performance comparisons. Otherwise, a different default temperature, stop rule, or sampling limit can change output length and make throughput or latency comparisons invalid.
Quick Recap
Production checklist for the PV 1.5 image
- Verify that every flag you plan to use is supported by the exact
intel/llm-scaler-vllm:1.5image and its bundled vLLM version; the cited guidance does not provide a PV 1.5-specific change log. - Calculate a weight-memory estimate using the model parameter count, precision, and tensor-parallel degree.
- Leave explicit headroom for KV cache, runtime buffers, and other GPU processes.
- Set
max_model_lento a tested application requirement rather than a theoretical maximum. - Load-test with realistic prompt and output lengths at the intended concurrency.
- Watch cache utilization and scheduler states; treat sustained cache use above 90% or frequent SWAPPED states as pressure signals.
- Keep model files local when shared storage causes slow startup, and use
--load-format dummyto isolate loading time. - Store the command line, environment variables, model revision, and benchmark results for each change.
- Use one-variable experiments and retain the lowest-complexity configuration that meets latency, throughput, and reliability targets.
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