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Most LLM-Scaler-vLLM PV 1.5 failures have one of four causes: model weights do not fit, the engine reserves too much KV cache for the configured context and batch, runtime scheduling exhausts that cache, or model loading and host-memory pressure make startup appear hung. Identify the class first, then change one control at a time. Lower --max-model-len, --max-num-seqs, and --max-num-batched-tokens when concurrent KV-cache demand is the problem; use a supported quantization or more parallelism when the weights themselves are too large. Raise gpu-memory-utilization only when the GPU has verified free VRAM, because the setting enlarges the preallocated cache and can create a startup OOM on an already occupied card.
Start by identifying which memory problem you have
Do not tune every flag after seeing “out of memory.” The same message can be produced while loading weights, allocating the KV cache, or serving requests. The remedy that fixes one class can make another worse.
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Weight-allocation OOM during engine startup
This occurs before the server can accept requests, usually while model parameters are being placed on GPUs. A useful first estimate from NVIDIA is:
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Use two bytes per parameter for BF16 or FP16, one byte for FP8, and 0.5 bytes for INT4 or NVFP4. This is only a sizing estimate: runtime buffers, CUDA allocations, framework overhead, and KV cache also need VRAM. If the estimate already exceeds the memory available on each GPU, changing batch settings will not make the weights fit.
| Weight format | Planning value | What it changes |
|---|---|---|
| BF16 or FP16 | 2 bytes per parameter | Highest weight footprint among the listed formats; generally preserves the model’s full-precision behavior. |
| FP8 | 1 byte per parameter | Approximately halves the parameter-storage estimate, subject to model and kernel support. |
| INT4 or NVFP4 | 0.5 bytes per parameter | Smallest estimate in this guide; verify quality, hardware, and loader compatibility for the particular checkpoint. |
If weights do not fit, select a supported lower-precision checkpoint, increase tensor parallelism (TP), or use pipeline parallelism (PP). TP divides the model across GPUs but adds synchronization; PP can add pipeline latency. Neither option creates free memory without a performance or operational trade-off.
KV-cache or context-length OOM at initialization
If the weights load but engine initialization says the requested sequence length needs more KV cache than is available, the issue is capacity reserved for attention history. The usual first fix is to lower --max-model-len to the longest context your application actually needs. A maximum context of 32,768 tokens reserves capacity that a workload capped at 8,192 tokens can never use productively.
Concurrent requests multiply the requirement. Reduce --max-num-seqs to limit simultaneous sequences and reduce --max-num-batched-tokens to cap tokens processed in one scheduler iteration. These controls directly reduce the cache pressure created by a large batch or many long prompts.
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Runtime preemption, swapping, and latency spikes
A server can start successfully and still run out of usable KV-cache space under load. vLLM reports preemption when it cannot keep all active sequences resident. The scheduler may move sequences to a swapped state or recompute work later, producing bursts of high latency and lower throughput. Frequent preemption calls for lower --max-num-seqs or --max-num-batched-tokens, more cache capacity, or more parallelism—not merely a larger request timeout.
Slow startup caused by loading or the host
A long pause before the engine starts is not always a GPU problem. Shared or network filesystems can make checkpoint reads slow, and host RAM pressure can cause operating-system swapping. A useful isolation test is --load-format dummy: it exercises engine initialization without loading model weights. If dummy loading is fast but normal loading is slow, investigate storage bandwidth, filesystem latency, download or extraction work, and CPU memory.
Control KV-cache demand before changing hardware
Set a realistic maximum context
Measure real prompt and output lengths, including system prompts and tool traces, then set --max-model-len above the observed requirement with a deliberate margin. Lowering this value reduces the worst-case KV reservation and often resolves initialization OOM immediately. It also rejects or truncates requests that exceed the new limit, so make the application’s context policy match the server setting.
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--max-num-seqs controls how many sequences can be active together. Lower it when many simultaneous requests fill the cache even though individual requests are acceptable. --max-num-batched-tokens limits the total tokens scheduled in one iteration; reducing it can protect VRAM and smooth latency, at the cost of less work per iteration.
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These flags solve different dimensions of pressure. A service with short prompts but too many users is primarily a sequence-concurrency problem. A service with fewer users sending very long prompts is primarily a token and context problem. Change the control that matches the observed workload instead of lowering every limit arbitrarily.
Use GPU memory utilization as a measured capacity control
The gpu-memory-utilization setting determines how much of a GPU’s memory the engine may use for its allocations, including cache capacity. Increasing it can help when another process is not consuming VRAM and the card has safe, verified headroom. It cannot reclaim memory held by another process. On an occupied GPU, increasing the value can enlarge the preallocation and turn a marginal startup into an OOM.
Lowering the value creates a safety margin for other processes and driver allocations but leaves less room for KV cache. Intel’s examples commonly use values around 0.8–0.9; treat those as starting points, not guarantees. Check actual free VRAM on the exact GPU, model, container, and driver combination before selecting a value.
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When the weights do not fit: precision and parallelism choices
Quantization
Move to a supported FP8, INT4, or NVFP4 model when the weight estimate exceeds per-GPU capacity. Confirm that the checkpoint’s quantization format is supported by the PV 1.5 image and your GPU architecture. Lower precision reduces weight memory, but it can change output quality, require model-specific kernels, or fail during loading if the runtime lacks support. Benchmark representative prompts rather than assuming every quantized checkpoint behaves identically.
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Tensor parallelism
Increasing TP divides parameter storage and execution across more GPUs. It is the direct remedy when one GPU cannot hold the weights. The trade-off is cross-GPU synchronization and communication; inadequate interconnect bandwidth can reduce throughput or increase token latency. Ensure each participating GPU has comparable capacity and that the container can see all of them.
Pipeline parallelism
PP places successive model stages on different GPUs. It can make a large model deployable when TP alone is unsuitable, but pipeline bubbles and stage-to-stage transfers add latency, especially for small or low-concurrency workloads. Use it when the model topology and available GPUs justify the operational complexity.
| Remedy | Primary VRAM effect | Typical trade-off | Use it when |
|---|---|---|---|
Lower max-model-len |
Less worst-case KV cache | Longer requests may be rejected | Requests do not need the configured maximum context |
Lower max-num-seqs |
Fewer resident sequence caches | More queueing at high concurrency | Many concurrent sequences trigger preemption |
Lower max-num-batched-tokens |
Smaller scheduler batches | Potentially lower throughput | Large iterations cause cache pressure or latency spikes |
Raise gpu-memory-utilization |
More cache allocation if VRAM is free | Less safety margin; can worsen startup OOM | The GPU is otherwise idle and measured headroom is safe |
| Quantization | Smaller model weights | Quality and kernel compatibility must be validated | Weight allocation, not batching, is the blocker |
| Tensor parallelism | Splits weights and execution across GPUs | Synchronization and communication overhead | One GPU cannot hold the model |
| Pipeline parallelism | Splits model stages across GPUs | Pipeline latency and added configuration | Stage partitioning is preferable to wider TP |
A controlled recovery workflow
- Record a baseline. For each run, write down the model revision, precision, GPU model and count, TP and PP sizes,
max_model_len,max_num_seqs,max_num_batched_tokens, memory-utilization value, concurrency, prompt lengths, and output lengths. Without this record, an apparent improvement may simply be a different workload. - Classify the failure from the timing and logs. A crash while placing parameters is a weight problem. An initialization message saying the requested sequence length needs more cache is a context problem. A server that starts but reports preemption or swapped sequences has runtime cache pressure. A long pause before weight loading points to storage or host-memory pressure.
- Check GPU ownership and headroom. Confirm that no other process, graphics workload, or stale worker is consuming VRAM. Do not raise
gpu-memory-utilizationuntil this check is complete. - Reduce cache demand first. Lower
--max-model-lento the application’s real limit. If concurrency is the trigger, lower--max-num-seqs; if each scheduler step is too large, lower--max-num-batched-tokens. Restart and verify that the error changes, rather than stacking several unmeasured changes at once. - Address a weight-fit failure. Choose a supported quantized model or add TP/PP GPUs. Recalculate the per-GPU estimate, then leave room for runtime buffers and KV cache.
- Isolate loading. Keep the model on local storage and run the engine with
--load-format dummy. Compare startup time with a normal load. A large difference directs investigation toward the filesystem, download path, decompression, or host RAM. - Raise capacity only when justified. If the card has verified free VRAM and the reduced settings leave unused capacity, increase
gpu-memory-utilizationin small increments and restart. Stop when startup becomes marginal or another process needs the reserved headroom. - Benchmark one change. Use a fixed dataset and the same concurrency and prompt/output lengths. Intel’s documentation includes
vllm bench serveexamples; use an equivalent repeatable benchmark in your environment and keep the winning configuration with its measurements.
Separate prefill, decode, and queueing performance
Aggregate requests-per-second hides the cause of a slow service. Measure time to first token (TTFT), prefill time, decode time, inter-token latency, queue time, token throughput, cache utilization, and scheduler state. Keep prompt and output lengths in the report because they change the balance between prefill and decode.
| Observation | Likely pressure | Targeted action |
|---|---|---|
| Long prefill or high TTFT on long prompts | Prompt processing dominates | Shorten prompts or context, improve prompt batching, or use hardware with more prefill capacity. |
| Long decode or high inter-token latency | Memory bandwidth, cache pressure, or contention | Lower concurrency if the GPU is thrashing; inspect cache utilization and other GPU processes. |
High queue time or many WAITING requests |
Admission rate exceeds scheduling capacity | Adjust concurrency and batching, or add serving capacity. |
Frequent preemption or SWAPPED sequences |
Insufficient resident KV cache | Reduce cache demand or add memory/parallelism before increasing request timeouts. |
Understand the scheduler states
The monitor documentation defines RUNNING, WAITING, and SWAPPED. A stable workload should not spend much time swapping. Frequent SWAPPED states indicate VRAM exhaustion or memory-management pressure. Cache utilization above 90% is identified as a condition that may trigger eviction and increase latency. Treat that level as a warning boundary, not a target to sustain indefinitely.
Interpret batching trade-offs
Larger batches can improve arithmetic utilization and throughput when there is cache headroom. Once the cache is near exhaustion, they cause preemption, recomputation, and queueing that can reduce both throughput and tail latency. Lower the batch-token or sequence limit until swapping subsides, then test whether a modest increase improves throughput without returning the pressure.
Fix slow or unreliable model loading
Use local model storage
Place the checkpoint on local, fast storage when shared or network filesystems introduce variable read latency. Ensure the host has enough CPU memory for loading and conversion; operating-system swapping can make every phase appear frozen. Compare cold and warm starts so that filesystem cache effects are not mistaken for a configuration improvement.
Use dummy loading as an isolation test
Run the same engine configuration with --load-format dummy. If initialization is still slow, focus on GPU discovery, kernel compilation, parallelism setup, or container configuration. If dummy initialization is fast, focus on model reads, file metadata, download or extraction steps, and host RAM.
Collect useful diagnostics for hangs and crashes
Enable diagnostics only for the duration needed to capture the failure:
VLLM_LOGGING_LEVEL=DEBUGfor detailed engine messages.VLLM_LOG_STATS_INTERVAL=1for statistics at one-second intervals.CUDA_LAUNCH_BLOCKING=1to make CUDA errors surface at the operation that caused them; expect slower execution.NCCL_DEBUG=TRACEfor detailed collective-communication logs when TP or PP is involved.VLLM_TRACE_FUNCTION=1only for exceptionally difficult cases; it is extremely slow.
Capture the first OOM, not only the final shutdown message. Include free and used VRAM, scheduler states, cache utilization, and the exact flags. Disable the most expensive tracing after diagnosis so benchmark results represent normal operation.
Check generation behavior after image or model updates
Performance tuning cannot explain every output change. vLLM notes that version 0.8.0 and later use defaults from the model creator’s generation_config.json. If responses change after updating the model or image, compare a run using --generation-config vllm before attributing the difference to quantization, batching, or memory settings. Keep generation configuration constant while measuring performance.
Production rollout checklist
- Verify the exact
intel/llm-scaler-vllm:1.5image supports every flag and quantization format you plan to use; the cited guidance does not provide a PV 1.5-specific change log. - Confirm model weights fit the per-GPU estimate with room for runtime buffers and KV cache.
- Set
max-model-lenfrom measured application needs, not the model’s theoretical maximum. - Load-test at expected concurrency while watching TTFT, inter-token latency, queue time, prefill/decode time, cache utilization, and scheduler state.
- Investigate any recurring
SWAPPEDstate or cache utilization above 90% before increasing traffic. - Change one control at a time and retain the dataset, prompt lengths, output lengths, and hardware details for every comparison.
- Keep model files local where possible and verify that host RAM is not swapping.
- Remove debug-only environment variables after capturing a failure.
The practical decision rule is straightforward: weight OOM requires a smaller representation or more model-parallel memory; cache OOM requires less context or concurrency; preemption requires reducing resident demand or adding capacity; and slow startup requires separating file loading from engine initialization. Applying that rule before changing flags prevents a larger cache reservation from turning a solvable performance issue into a new startup failure.
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