Quick Verdict: PagedAttention vs Standard KV Cache
Standard KV cache allocation reserves a contiguous memory block for each request based on the maximum possible sequence length. A request that could generate up to 4,096 tokens immediately claims all the VRAM for 4,096 tokens, even if the response is only 200 tokens long. PagedAttention, pioneered by vLLM, allocates KV cache in small non-contiguous pages, only using memory as tokens are actually generated. This eliminates 60-80% of wasted VRAM, enabling 2-4x more concurrent users on the same dedicated GPU hosting hardware.
How Standard KV Cache Works
Traditional inference engines allocate a contiguous block of GPU memory for each request’s key-value cache. The block size equals the maximum context length multiplied by the model’s hidden dimensions and number of layers. For a 70B model with 4K max context, each request reserves approximately 2GB of VRAM upfront, regardless of actual usage.
This pre-allocation creates severe memory fragmentation. Short requests waste most of their reserved memory. Variable-length requests cannot share unused space. The GPU runs out of KV cache memory long before the actual data would fill it.
How PagedAttention Works
PagedAttention divides KV cache memory into fixed-size pages (typically 16 tokens each). Pages are allocated on demand as the sequence grows, similar to how operating systems manage virtual memory. A request generating 200 tokens uses only 13 pages, not the 256 pages needed for a full 4K context block. Follow the vLLM production guide for optimal page configuration.
Memory Efficiency Comparison
| Metric | Standard KV Cache | PagedAttention |
|---|---|---|
| Memory Allocation | Pre-allocated contiguous blocks | On-demand paged allocation |
| Memory Waste (avg 500-token responses) | 60-80% wasted | Under 5% wasted |
| Max Concurrent Users (70B INT4, RTX 6000 Pro 96 GB) | 8-12 | 30-50 |
| Memory Fragmentation | High (external fragmentation) | Minimal (internal only) |
| Prefix Caching Support | Manual implementation | Native (shared pages) |
| Throughput at 50 Users | Cannot serve (OOM) | Full throughput |
Throughput Impact
PagedAttention does not make individual token generation faster. Its benefit is purely in memory efficiency, which translates to higher concurrency. At low concurrency (1-5 users), PagedAttention and standard allocation perform identically. At 20+ users, standard allocation hits out-of-memory errors while PagedAttention continues serving. This throughput difference is the reason vLLM dominates production LLM hosting. Check token speed benchmarks for concurrency-scaled data and engine comparisons for alternative implementations.
Prefix Caching Bonus
PagedAttention enables efficient prefix caching. When multiple requests share the same system prompt, the KV cache pages for that prompt are computed once and shared across all requests. A 500-token system prompt serving 50 users stores one copy of its KV pages instead of 50 copies. This saves gigabytes of VRAM and eliminates redundant computation. For multi-GPU deployments, prefix caching across replicas further multiplies the benefit. See the benchmarks section for prefix caching impact data.
Recommendation
Use PagedAttention through vLLM for all production inference. There is no valid reason to use standard KV cache allocation for serving concurrent users. The memory efficiency gains are free: no quality loss, no additional latency, and 2-4x better concurrency. Deploy vLLM on GigaGPU dedicated servers for maximum efficiency. Select your GPU based on inference requirements and explore the infrastructure blog for architecture patterns.
