RTX 5060 Ti 16GB with Chunked Prefill

Chunked prefill is vLLM’s scheduler feature that mixes prefill and decode work inside a single forward pass. On the RTX 5060 Ti 16GB at our dedicated GPU hosting, it massively improves tail latency for concurrent chat when one user pastes a long prompt.

Contents

• The problem it solves
• How it works
• Enabling
• Measured impact
• Interaction with other flags

The Problem

Without chunked prefill, vLLM alternates between prefill batches and decode batches. If user A sends a 32k-token prompt while users B-E are mid-stream decoding, the prefill blocks the decode batch for hundreds of milliseconds. Every active user sees a stall.

On a small GPU like the 5060 Ti this hurts doubly – prefill is already slow because compute is tight, so these stalls are visible.

How It Works

Chunked prefill splits prefill into fixed-size chunks (default 512 tokens). Each scheduler step processes one prefill chunk plus any decode steps that fit in the budget. Decode throughput stays continuous; prefill completes over multiple forward passes instead of one giant one.

Trade-off: total prefill time for the long request is slightly higher (more scheduler overhead), but p99 decode latency for other users drops sharply.

Enabling

python -m vllm.entrypoints.openai.api_server \
  --model meta-llama/Llama-3.1-8B-Instruct \
  --quantization fp8 \
  --enable-chunked-prefill \
  --max-num-batched-tokens 2048 \
  --max-model-len 32768 \
  --gpu-memory-utilization 0.90

Key knobs:

• --max-num-batched-tokens: total token budget per forward pass. 2048 is a good default for 16 GB; drop to 1024 if VRAM is tight.
• Chunked prefill is on by default in vLLM 0.6+ when max_model_len is high – explicit flag just guarantees it.

Measured Impact

8 concurrent users doing 2,000-token chat; one user periodically sends a 16,000-token prompt. Llama 3.1 8B FP8 on 5060 Ti 16GB:

Short-prompt users see vastly smoother experience. The long-prompt user pays a small prefill tax. Net win unless you are serving one batch-1 user with giant prompts – in which case disable it.

Interactions

• With prefix caching: complementary – cached blocks skip prefill entirely, chunked prefill smooths the non-cached remainder.
• With speculative decoding: chunked prefill takes precedence; speculative work is deferred until decode batch settles.
• With FP8 KV cache: independent – free to stack both.
• With long context (128k): essential – a 64k prefill without chunking freezes the server.

Recommendation: enable chunked prefill on any vLLM deployment serving more than one concurrent user. The p99 improvement is worth the small p50 regression on short prompts.

See also: context budget, FP8 Llama deployment, chatbot hosting.