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RTX Spark CUDA out-of-memory (OOM) errors are fixed by matching the remedy to the allocation stage that failed. Reduce context length and concurrency for vLLM KV-cache failures, lower --ctx-size or use a smaller quantization in llama.cpp, leave headroom for CUDA-graph capture, and treat host-memory pressure as GPU pressure because RTX Spark uses unified memory.
Do not assume a 128 GB system has 128 GB available to a model. The operating system, background applications, swap activity, model weights, KV cache, temporary buffers and runtime overhead all draw from the same physical memory pool.
Identify the stage that ran out of memory
The same “CUDA out of memory” wording can describe several different failures. Read the final 30–50 lines of the runtime log and note whether the error appears while loading weights, allocating the KV cache, warming up, capturing CUDA graphs or serving requests. Apply the first fix for that phase rather than changing every setting at once.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errors| Failure phase | Typical evidence | What is consuming memory | First remedy |
|---|---|---|---|
| Weight loading | The process fails while reading shards or constructing model tensors, before the server is ready. | Model weights plus temporary load and conversion buffers. | Use a smaller model or a smaller quantization; in llama.cpp, verify that enough layers are assigned to the GPU. |
| KV-cache allocation | Weights load successfully, but startup fails when the requested context or batch is reserved, or the first long request fails. | Attention keys and values for the configured context and active sequences. | Lower vLLM --max-model-len and --max-num-seqs; lower llama.cpp --ctx-size. |
| Allocator fragmentation | The log reports substantial memory reserved by PyTorch but still unallocated, or a relatively small contiguous allocation cannot be made. | Free memory split into blocks that do not satisfy a contiguous tensor request. | Set PYTORCH_ALLOC_CONF=expandable_segments:True and retry with a clean process. |
| CUDA-graph capture or warmup | Weights and KV cache are present, then the process fails during graph capture, compilation or warmup. | Extra buffers and graph-specific allocations on top of the steady-state model. | Lower the runtime memory-utilization target; if necessary disable graph capture or use eager execution. |
| Runtime request | Startup succeeds, but a larger prompt, more simultaneous users or a longer generation triggers OOM. | Additional KV cache, temporary activations and per-request workspace. | Reduce context or concurrency and enforce request limits before increasing capacity. |
This distinction matters because reducing context will not repair allocator fragmentation, while disabling graphs will not make a model’s weights fit.
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Why a 128 GB RTX Spark can still report OOM
RTX Spark and DGX Spark use a unified-memory architecture: the GPU and CPU share system DRAM. There is no permanently separate pool of 128 GB reserved only for CUDA. The operating system, desktop services, containers, file cache and other applications consume part of the same pool.
NVIDIA notes that DRAM reclamation through swap can make cudaMemGetInfo report less memory than may ultimately be allocatable. That does not make swap a practical extension of GPU memory: paging model data or KV-cache pages is slow and can destabilize a workload. Treat low host-memory availability and active swap as an immediate OOM warning.
Free the shared pool before changing model settings
- Stop other model servers, notebooks, compilers and containers that use substantial memory.
- Close memory-heavy graphical applications and browser tabs on a development system.
- Check host RAM and swap while the launch is running. A system approaching its RAM limit can fail even when the nominal Spark capacity appears large.
- Run one model server per test. A previous process that has not exited cleanly can retain allocations until its process is terminated.
- After an OOM, restart the failed server before retesting so that temporary allocations and fragmented pools are gone.
Only after the machine has adequate free memory should you interpret a reported GPU allocation limit as a model-fitting limit.
How much memory an LLM actually needs
Parameter count gives only a weight-memory baseline. For BF16, each parameter occupies approximately two bytes before runtime overhead. NVIDIA’s NIM memory guidance gives these reference points:
| Model or calculation | Weight-memory figure | What the figure excludes |
|---|---|---|
| 8 billion parameters in BF16 | Approximately 16 GB | KV cache, activations, temporary buffers, allocator overhead and the operating system. |
| 70 billion parameters in BF16 | Approximately 140 GB | All runtime overhead and memory needed by the host. |
| Qwen3.8-27B on RTX Spark | NVIDIA’s llama.cpp recipe targets 48–128 GB configurations. | The usable context, quantization-specific overhead and any CPU-offloaded layers still vary by configuration. |
Quantization reduces weight storage, but it does not eliminate KV-cache growth. Context length, concurrent sequences, precision, tokenizer and runtime implementation all affect the final allocation. A quantized model that fits at 4,096 tokens may still fail at a much larger context or with several simultaneous requests.
Fix vLLM OOM errors step by step
NVIDIA’s vLLM guidance identifies “context length too large / model too big” as a CUDA OOM cause. Apply the controls in this order and observe which change moves the failure.
1. Establish a conservative baseline
Start with one sequence and a moderate context. Add these options to the vLLM command you already use:
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--max-model-len 8192
--max-num-seqs 1
--gpu-memory-utilization 0.85The values are a diagnostic baseline, not a universal optimum. If the model loads and serves a short request, increase one limit at a time while watching host memory and the server log.
2. Lower --max-model-len when the KV cache is the problem
--max-model-len is the maximum token context vLLM reserves or supports. A long maximum can require a large KV cache before any user sends a long prompt. If weights load but startup fails during cache initialization, halve the value and retry. For example, move from 32,768 to 16,384, then to 8,192 or 4,096 if necessary.
This setting limits the usable prompt-plus-generation context. It does not change the model’s learned weights, but requests exceeding the limit will be rejected or truncated according to the serving configuration. Choose the smallest maximum that covers the application’s real prompts rather than the model card’s theoretical context.
3. Lower --max-num-seqs when concurrency is the pressure
--max-num-seqs controls how many sequences vLLM can schedule concurrently. Each active sequence contributes KV-cache demand. If a single short request works but a burst of users causes OOM, reduce this value before changing the model or quantization. A setting of 1 is useful for proving that the model can run at all; raise it gradually after a successful single-request test.
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vLLM’s utilization target is not additional memory. It tells the runtime how much of the visible pool it may plan to use. Lowering it leaves room for the operating system, temporary allocations and graph capture. NVIDIA gives 0.85 as an example when startup or capture needs more headroom.
Lowering the target can reduce the maximum KV cache and therefore throughput. If the model fails while loading its weights, this flag cannot make the weights smaller; use a smaller model, quantization or GPU placement change first.
5. Distinguish graph-capture failure from cache failure
If the log shows successful weight and KV-cache allocation followed by CUDA-graph capture or warmup OOM, keep the context and sequence settings that fit, then lower --gpu-memory-utilization. If capture still fails, run in eager mode with --enforce-eager. Eager execution avoids graph-capture allocations but generally sacrifices some throughput and may increase per-request launch overhead.
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For NVIDIA NIM deployments, the equivalent escape hatch is NIM_DISABLE_CUDA_GRAPH=1. Use it for a confirmed graph-capture failure, not as the first response to a model that cannot load.
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6. Reintroduce capacity carefully
- Confirm that a short prompt completes with one sequence.
- Increase
--max-model-lenuntil the application’s required context is reached or the next limit appears. - Increase
--max-num-seqsto the smallest concurrency that meets demand. - Raise
--gpu-memory-utilizationonly if extra cache capacity is needed and the host has reliable headroom. - Retest long prompts and simultaneous requests, because a successful health check does not prove that the maximum context is safe.
Fix llama.cpp and llama-server startup OOM
llama.cpp exposes different controls from vLLM, but the principles are the same: fit the weights first, then reserve a realistic context, and decide how much of the model should reside on the GPU.
Lower --ctx-size
When llama-server fails during initialization, start with a context size of 4,096 using --ctx-size 4096. This reduces the KV cache that must be created at launch. If that succeeds, increase the context in measured steps while testing the longest prompt your application actually needs.
A lower context limits the total prompt and generated-token window. It is often the least damaging change for a local assistant whose normal conversations are short, but it is unsuitable for workloads that genuinely require long documents in one request.
Choose a smaller quantization from the same model family
If the model still fails while weights are being loaded, use a smaller quantization of the same model, or choose a smaller parameter-count model. Quantization lowers weight memory and usually preserves the architecture, but lower-bit formats can change output quality. Test representative prompts after switching; a model that starts successfully is not automatically equivalent for your task.
Verify GPU-layer placement
Check the startup log for the number of layers placed on the GPU. A configuration that attempts to offload more layers than the shared memory can hold can fail before serving begins. Reducing the GPU-layer count allows more layers to remain on the CPU and may permit startup, but CPU execution usually reduces tokens-per-second and can increase latency.
Conversely, partial offload can be a deliberate fit strategy when the full model does not fit. Expect performance to vary with the number and location of CPU-resident layers, memory bandwidth and prompt length. Do not compare a partially offloaded run with a fully GPU-resident run as though they had the same hardware path.
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Use the RTX Spark recipe as a fit reference, not a guarantee
NVIDIA’s RTX Spark llama.cpp recipe places Qwen3.8-27B in a 48–128 GB target range. That range describes a supported configuration envelope, not a promise that every quantization, context size and concurrency level will fit at once. Start at the lower context and conservative GPU-layer setting, then expand after a clean launch.
Repair allocator fragmentation
PyTorch can reserve memory in segments that are too fragmented for a new contiguous tensor. NVIDIA’s GPU-memory troubleshooting guidance describes this pattern as substantial memory “reserved by PyTorch but unallocated.” The physical memory may exist, yet the allocator cannot satisfy the requested block.
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export PYTORCH_ALLOC_CONF=expandable_segments:TrueThen restart the server and retry. This changes how allocation segments grow; it does not increase physical memory or make an oversized model fit. If the failure persists, lower the context or concurrency as well, because fragmentation and genuine capacity pressure can occur together.
Do not evaluate fragmentation from a single stale process. Stop the server, ensure no worker remains, and launch a fresh process with the setting applied. Record whether the error moves from allocation to a later phase; that change tells you which constraint is now binding.
Handle CUDA-graph capture and warmup failures
CUDA graphs can improve steady-state performance, but capture requires extra allocations and a stable execution shape. A model can therefore load its weights and KV cache successfully and still fail during warmup.
- Lower the runtime’s memory-utilization target so unreserved memory remains available.
- Keep the context and sequence limits at values that already passed weight and cache allocation.
- For vLLM, retry with
--enforce-eagerif the log specifically identifies graph capture. - For NIM, set
NIM_DISABLE_CUDA_GRAPH=1for the same diagnosis. - Measure latency and throughput after disabling graphs; eager mode trades some performance for lower startup memory demand and often easier reproducibility.
If eager mode also fails while allocating the KV cache, the root cause is not graph capture. Return to context, sequence and host-memory limits.
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Use a controlled diagnosis instead of random flag changes
Change one variable per run and keep a short record of the phase, settings and result. This makes the eventual working configuration reproducible across software updates.
- Check competing processes: verify that the Spark host has adequate free RAM and no abandoned model server.
- Classify the phase: loading, KV cache, fragmentation, graph capture or request-time growth.
- Make the phase-specific change: quantization or GPU layers for weights; context or sequences for KV cache; allocator configuration for fragmentation; headroom or eager mode for graphs.
- Run a minimal request: use one short prompt and one active sequence.
- Stress the real workload: test the longest prompt, generation length and concurrency that users will send.
- Raise limits incrementally: stop at the first setting that causes OOM and keep the preceding value as the operational ceiling.
Record the runtime version, model revision, quantization, context, sequence limit, utilization target and whether graph capture was enabled. Small allocator and kernel changes between software versions can alter the margin even when the model files are identical.
Choose a fix by its trade-off
| Fix | Memory demand reduced | Quality impact | Speed impact | Best use |
|---|---|---|---|---|
Lower vLLM --max-model-len or llama.cpp --ctx-size |
KV cache and long-request workspace | Only requests needing the removed context are affected. | Often neutral or faster for shorter requests. | Cache allocation or long-prompt OOM. |
Lower vLLM --max-num-seqs |
Concurrent KV cache and scheduling buffers | No direct model-quality change. | Lower peak throughput and concurrency. | OOM during bursts or multi-user serving. |
Lower --gpu-memory-utilization |
Reserved pool size and graph-capture headroom | No direct quality change. | May reduce batching capacity. | Startup margin and graph capture. |
| Smaller quantization | Persistent weight storage | Can reduce accuracy or stylistic fidelity. | Depends on kernels; may improve fit but not always speed. | Weight-loading OOM. |
| Smaller model | Weights, activations and often KV-cache footprint | Potentially significant capability change. | May improve latency and throughput. | When the target model cannot fit at all. |
| Fewer GPU layers in llama.cpp | GPU-resident weights | No model change. | CPU offload generally lowers throughput and raises latency. | Making a borderline model start. |
PYTORCH_ALLOC_CONF=expandable_segments:True |
Does not reduce requested model memory; improves allocation behavior. | None. | Usually intended as a compatibility fix; measure your workload. | Reserved-but-unallocated fragmentation. |
| Eager execution | Graph-capture allocations | No model-quality change. | Usually lower throughput than captured graphs. | OOM during CUDA-graph capture or warmup. |
Common symptoms and the right branch
“It fails immediately while loading the model.”
Free host memory first. If the failure remains, use a smaller quantization or model. With llama.cpp, reduce GPU-layer placement so some layers stay on the CPU. Do not expect a lower request context to rescue a weight tensor that cannot be allocated.
“The server starts, but the first long prompt fails.”
Lower the context limit. In vLLM, reduce --max-model-len; in llama.cpp, reduce --ctx-size. Also check the requested generation limit, because prompt and output tokens share the model’s context window.
“One user works, several users cause OOM.”
Reduce --max-num-seqs in vLLM and test the actual concurrent request pattern. Keep a safety margin for bursts rather than sizing exactly to the average number of users.
“Weights and cache fit, then warmup reports OOM.”
Lower --gpu-memory-utilization. If the log identifies CUDA-graph capture, use eager mode or disable graphs for the deployment. Verify the resulting performance before adopting the workaround permanently.
“The log says memory is reserved but unallocated.”
Retry from a clean process with PYTORCH_ALLOC_CONF=expandable_segments:True. If the next failure is a straightforward capacity error, combine the allocator change with lower context or concurrency.
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That is expected on unified memory. The operating system and applications share DRAM with CUDA, and swap activity can reduce immediately available capacity. Close competing workloads and measure free host memory during launch.
Operating rules that prevent recurring OOMs
- Set a context limit based on measured application needs, not the largest theoretical context advertised for the model.
- Keep concurrency bounded at the server and queue excess work instead of allowing unlimited simultaneous KV-cache growth.
- Leave headroom for model startup, graph capture and ordinary operating-system activity.
- Warm up with a representative prompt length; a tiny health-check request cannot reveal a long-context limit.
- Monitor both host RAM and swap on RTX Spark because they are part of the GPU’s effective memory budget.
- Pin model, quantization and runtime versions for production, then retest after upgrades.
- Document whether llama.cpp uses full GPU placement or CPU offload so performance expectations remain clear.
The reliable strategy is to make the smallest change that addresses the observed phase, prove a minimal run, and then expand context or concurrency until the real workload—not an optimistic specification—defines the safe limit.
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