Unverified50% confidenceFactExact time
llama.cpp allocates VRAM on demand, while vLLM pre-allocates a specified proportion of VRAM via the --gpu-memory-utilization parameter (including the KV cache and the model itself).
1
Sources
50%
Confidence
Long-term
Relevance
10/30/2026
First Seen
Sources
The Complete Guide to Running AI Models Locally: Inference Engines and Tool Stack Explained
youtubeNeuralNine10/30/2026
Related Entities
Related Claims
UnverifiedThe tiered cache system in vLLM is commonly referred to as Tiered KV Cache and can span multiple storage tiers including GPU HBM, CPU DRAM, NVMe SSD, or remote distributed caches.76% similarUnverifiedVRAM 成为本地 LLM 上下文瓶颈的根本原因在于 KV Cache 机制，7B 模型每 1K token 的 KV Cache 约需 0.5–1GB 显存75% similarUnverifiedBy default, vLLM plans 90% of GPU VRAM into the budget via the --gpu-memory-utilization parameter, and only the remainder is used as KV cache.73% similarUnverifiedThe deployment uses a llama.cpp server with 4-bit KV cache, context length set to 100,000, 32 layers offloaded to system memory, and lazy mode enabled to force the Ngram table to be read from SSD.71% similarVerifiedThe LLM decoding process is memory bound, with the GPU spending most of its time waiting for data transfers during token-by-token generation.71% similar
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