A local model that crashes near 32K tokens may be running out of memory, but that does not automatically mean you need a new GPU. Longer contexts require more memory for the model’s context state, while model weights, other workloads and simultaneous requests compete for the same resources. Check the runtime’s actual context allocation and reduce memory demand before buying hardware; the precise cause depends on your model, runtime, settings and system.
Why 32K tokens can cause a crash
Context length is a memory setting, not just a model capability. Ollama defines it as “the maximum number of tokens that the model has access to in memory” (Ollama context documentation). As the context grows, the runtime needs more memory for the context state, commonly managed as a KV cache. That memory is in addition to what the model weights and other active workloads use.
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A model’s advertised maximum context does not guarantee that a particular combination of runtime, model, quantization and hardware can allocate or sustain that context. Loading the model successfully at a shorter context is not proof that the system has enough headroom at 32K.
Concurrency can make the difference. Ollama documents required RAM as scaling with OLLAMA_NUM_PARALLEL multiplied by OLLAMA_CONTEXT_LENGTH (Ollama FAQ). More parallel requests and a larger context can therefore increase memory demand together.
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What Ollama’s context defaults tell you—and what they do not
Ollama’s rolling documentation lists these default context lengths by available VRAM:
| Available VRAM | Ollama default context length |
|---|---|
| Under 24 GiB | 4K |
| 24–48 GiB | 32K |
| 48 GiB or more | 256K |
These are Ollama defaults, not universal VRAM requirements or guarantees for every model and runtime. The documentation does not establish a single amount of VRAM that fixes all 32K crashes. Treat the defaults as a clue about Ollama’s configuration, not as a hardware-buying rule.
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Check the likely causes before changing hardware
- Record the setup and exact failure. Note the runtime and version, model and quantization, operating system, GPU and its VRAM, effective context length, concurrent request count, and the exact error. A crash near 32K alone is not enough to identify the cause.
- Verify the context the runtime actually allocated. In Ollama, run
ollama psto inspect context allocation and whether processing is on the GPU, CPU, or split between them. Compare the effective setting with what you expected; a model’s advertised maximum and the runtime’s active allocation are different things. See the Ollama context guide. - Reduce the workload and retry. Try a shorter context and fewer simultaneous requests. If that prevents the failure, memory pressure becomes a more plausible explanation. In Ollama, parallelism and context length both affect memory demand, as described in its FAQ.
- Review the runtime’s memory allocation controls. In vLLM,
gpu_memory_utilizationcontrols the share of GPU memory used for model weights, activations and KV cache; vLLM warns that setting it too high can cause an out-of-memory error. Itskv_cache_memory_bytesoption provides an explicit KV-cache allocation control. Consult the vLLM engine arguments documentation and adjust settings for your workload rather than assuming that more allocation is always safer. - For vLLM on Gaudi, inspect batch size and KV-cache capacity. The vLLM Gaudi performance guide says the default batch size may not suit long contexts and describes preemption when KV-cache space is insufficient. This advice is specific to that vLLM/Gaudi setup; it is not an Ollama setting or a universal fix.
When a hardware upgrade is justified
Consider more VRAM only after checking the actual context allocation, processor placement, concurrency and runtime memory settings. If reducing context or parallel work resolves the crash, configuration may be enough. If the system still cannot allocate the context your workload requires after those checks, a hardware limit may be the remaining constraint.
Do not buy a GPU based on a universal “32K needs X GiB” claim: the cited runtime documentation does not provide one. The memory needed depends on the model and quantization, runtime, context allocation, concurrency and what else is using memory. Compare those factors for your specific setup before choosing hardware.
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What to include when asking for a diagnosis
- Runtime name and version, model name and quantization
- Operating system, GPU and available VRAM
- Effective context length and processor/offload status
- Number of concurrent requests or applicable batch size
- Exact error and the settings used when it occurs
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