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A local model that crashes near 32k tokens may be running out of memory, but that is not the only possible cause—and it does not automatically mean you need a new GPU. Longer contexts require more memory for the model’s context state, while model weights, simultaneous requests, and other workloads use memory too. First check the runtime’s actual context allocation and memory settings; then reduce context or concurrency to see whether the failure changes.
Why can a local model fail near 32k tokens?
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-length documentation). Supporting a context length on paper does not guarantee that a particular runtime, configuration, and machine can allocate it successfully.
The likely issue is memory pressure: the model’s weights and its context state compete for available memory, and other workloads or concurrent requests add demand. A model loading at a shorter context does not prove that it can sustain 32k. The precise cause depends on the model, quantization, runtime and version, hardware, and settings. Implementation bugs or other non-memory causes are also possible; an error near 32k alone does not identify the cause.
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Check the effective context and memory placement first
Before changing hardware, note the runtime and version, model and quantization, operating system, GPU, exact error, effective context length, and number of simultaneous requests. These details help distinguish a configured limit from a memory-allocation failure.
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In Ollama, run ollama ps to inspect the context allocation and whether processing is on the GPU, CPU, or split between them. Compare the reported allocation and placement with what you intended to run; a configured maximum is not proof that the full context is actually allocated.
Reduce the load to test whether memory is the bottleneck
- Lower the context length. Retry with a shorter context, then increase it gradually. If the shorter setting works while the larger one fails under otherwise unchanged conditions, memory pressure becomes a stronger possibility.
- Reduce simultaneous requests. Ollama documents that required memory scales with
OLLAMA_NUM_PARALLELmultiplied byOLLAMA_CONTEXT_LENGTH(Ollama concurrency FAQ). More parallel requests can therefore increase demand even when each request uses the same context length. - Check other workloads. Close or pause workloads that compete for system or GPU memory, then retry with the same model and settings. This helps establish whether available memory, rather than the model’s advertised limit, is the constraint.
Check runtime-specific memory controls
Ollama context defaults
Ollama’s rolling documentation currently lists default context lengths of 4k for systems with under 24 GiB of VRAM, 32k for 24–48 GiB, and 256k for 48 GiB or more (Ollama context-length documentation). These are Ollama defaults, not universal VRAM requirements or guarantees for all models and runtimes. The documentation does not state a publication date, and defaults may change.
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vLLM GPU and KV-cache allocation
If you use vLLM, review its GPU memory allocation controls. The gpu_memory_utilization setting governs the share of GPU memory available for weights, activations, and KV cache; vLLM warns that setting it too high can cause an out-of-memory error. The kv_cache_memory_bytes setting provides an explicit KV-cache allocation control. Consult the vLLM engine arguments documentation for the options and behavior applicable to your version.
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For vLLM workloads on Gaudi, the vLLM Gaudi performance guide advises checking batch size for long contexts and describes preemption when KV-cache space is insufficient. This is Gaudi-specific guidance; do not treat it as an Ollama setting or as a universal instruction for other runtimes.
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How much VRAM do you need for 32k?
There is no universal VRAM figure supported for every model at 32k. Requirements vary with the model and quantization, runtime, actual context allocation, memory placement, parallel request count or batch size, and other GPU workloads. Ollama’s VRAM-based defaults describe its own behavior, not a guarantee that every model will fit or run reliably at that context. vLLM’s allocation controls likewise show that memory reserved for weights and KV cache matters alongside the context setting.
Consider more VRAM only after checking the effective allocation, reducing context and concurrency, and reviewing the relevant runtime controls. The sources do not establish that a particular GPU or amount of VRAM will fix every 32k crash.
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What to include when asking for help
- Runtime and version, model name, and quantization.
- Operating system, GPU, and available VRAM or system memory.
- Effective context length and processor placement, such as the output of
ollama pswhen using Ollama. - Parallel request count or batch size, relevant memory-allocation settings, and the exact error text.
- Whether the same model works at a shorter context with fewer simultaneous requests.
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