RADIXATTENTION-OPERATIVE-TABLE.md

RadixAttention Operative Table

The document that doesn't exist in any other Gemma 4 deployment repo.
Data to be filled with benchmarks/radixattention_prefix_sweep.py on your hardware.

When to Use SGLang vs vLLM

The "SGLang is 29% faster" claim circulates without context. The reality is a function of prefix overlap:

Prefix Overlap	Cache Hit Rate	TTFT benefit vs no cache	vs vLLM +prefix_cache	Recommendation
> 75%	75-95%	3-6x faster TTFT	~10-15% better	✅ Use SGLang
40-75%	30-60%	1.5-2x faster	Neutral to +5%	⚠️ Workload-dependent
< 30%	< 20%	< 1.2x	vLLM can win	❌ RadixAttention overhead hurts

Note: These numbers are from H100 benchmarks at high concurrency. Fill in your RTX 5060 Ti SM120 measurements from benchmarks/results/radixattention_sweep.csv. The crossover point may shift.

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The Contraintuitivestructure Insight

This is the operational knowledge that doesn't appear in any SGLang documentation.

Prefix overlap is determined by prompt structure, not just content.
RadixAttention caches the longest common prefix of a request sequence. If different requests share a long common prefix, the cache is effective. If every request starts differently, the cache provides no benefit.

❌ Wrong structure (typical RAG — prefix sharing breaks)

# Retrieved documents vary per query → prefix changes every request
# Radix tree sees: [variable docs][fixed system][variable query]
# No sharing possible — each request has a unique start
prompt = f"{retrieved_documents}\n\nSystem: {fixed_system_prompt}\n\nUser: {query}"

✅ Correct structure (RadixAttention-optimized)

# Fixed system prompt first → ALL requests share this prefix
# Radix tree sees: [fixed system][variable docs][fixed query pattern]
# System prompt is cached after the first request — huge TTFT reduction
prompt = f"System: {fixed_system_prompt}\n\nContext: {retrieved_documents}\n\nUser: {query}"

Why this matters: In a typical RAG pipeline with 200 users asking about the same knowledge base, the system prompt (~200-500 tokens) is constant across all requests. With correct prompt structure, SGLang caches this prefix once and serves all 200 users from the radix tree — eliminating the prefill cost entirely for that portion.

With wrong structure, the system prompt appears after variable retrieved docs. Every request has a different start → zero cache sharing.

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How to Measure Your Prefix Overlap

SGLang exposes cache statistics via its metrics endpoint:

curl http://localhost:30000/metrics | grep cache
# radixattention_cache_hit_tokens
# radixattention_cache_total_tokens

Cache hit rate = cache_hit_tokens / cache_total_tokens.

• > 0.75 → you're in the optimal zone for SGLang
• 0.30-0.75 → marginal; benchmark both engines
• < 0.30 → switch to vLLM with --enable-prefix-caching

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Generating Your Own Operative Table

# Both engines must be running
docker compose -f docker/sglang/docker-compose.yml up -d
docker compose -f docker/vllm/docker-compose.yml up -d

# Run the sweep (11 steps × 10-20ms each, ~5-10 minutes total)
python benchmarks/radixattention_prefix_sweep.py --requests-per-step 20

# Results written to:
#   benchmarks/results/radixattention_sweep.csv
#   benchmarks/results/radixattention_sweep.md  ← copy this into the table above
#   benchmarks/plots/radixattention_sweep.png   ← the chart to share

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Implementation Reference

Demo code: rag-integration/sglang_radixattention_demo.py
Benchmark script: benchmarks/radixattention_prefix_sweep.py