update demo of long context handling#4279
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| This cache allows the model to avoid recomputing attention for previous tokens when generating new tokens, greatly speeding up inference for long contexts. | ||
| For very long contexts or high concurrency, the KV cache can consume a large amount of memory (RAM or VRAM). | ||
| Compression reduces this memory usage, enabling longer prompts or more parallel requests without running out of memory. | ||
| This parameter is applicable only or pipelines with continuous batching and paged attention. It is not used with NPU device. |
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| This parameter is applicable only or pipelines with continuous batching and paged attention. It is not used with NPU device. | |
| This parameter is applicable only for pipelines with continuous batching and paged attention. It is not used with NPU device. |
przepeck
reviewed
Jun 10, 2026
przepeck
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Jun 10, 2026
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| | 50,000 | 945 | 0.7 | 985 | 1.5 | | ||
| | 100,000 | 1258 | 1.5 | 1713 | 3 | | ||
| Lower precision in KV Cache reduce the memory consumption and can also improve latency. |
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| Lower precision in KV Cache reduce the memory consumption and can also improve latency. | |
| Lower precision in KV Cache reduce the memory consumption and can also improve latency. |
przepeck
reviewed
Jun 10, 2026
Co-authored-by: Paweł Rzepecki <pawel.rzepecki@intel.com>
przepeck
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dtrawins
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Jun 10, 2026
Co-authored-by: Trawinski, Dariusz <dariusz.trawinski@intel.com>
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