vLLM KV Cache Memory Limits GPU Concurrency, Not Compute

A technical deep-dive into vLLM's KV cache reveals that memory, not compute, is the primary bottleneck for concurrent requests on GPUs. The KV cache, which stores attention keys and values for each token, grows linearly with sequence length and requires significant memory per token. For instance, Llama 3.1 8B with bfloat16 precision uses approximately 128 KiB per token. On a 24GB GPU, after accounting for model weights, only about 5GB remains for the KV cache, limiting concurrency to around 4 requests when each request utilizes a substantial portion of the context window. Optimizations like using FP8 precision or quantized weights can significantly reduce memory usage and increase the number of concurrent requests. AI

IMPACT Understanding KV cache limitations is crucial for optimizing LLM inference performance and cost on consumer hardware.

RANK_REASON Technical deep-dive into a specific optimization technique for an existing inference engine.

Read on dev.to — LLM tag →

• bfloat16
• FP8
• GQA
• graphics processing unit
• KV cache
• Llama 3.1 8B
• vLLM

• infra

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