semblend 0.3.9

Semantic KV cache reuse for LLM inference engines (vLLM, SGLang, TRT-LLM)

SemBlend

Semantic KV cache reuse for LLM inference engines.

SemBlend extends exact-prefix KV caching (vLLM, LMCache, SGLang) with semantic donor discovery. When a prompt is semantically similar to a cached one but lexically different — different instruction phrasing, sentence order, or template fields — SemBlend finds and reuses the cached KV tensors, replacing a multi-second prefill with sub-second KV retrieval.

vLLM + LMCache alone:        semantically similar prompt  →  0% hit   →  full prefill
vLLM + LMCache + SemBlend:                                →  83–100% hit  →  reuse donor KV

Performance

Measured on A10G GPU (0.85 utilization), Qwen2.5-7B-AWQ, vLLM 0.14.1 + LMCache. All results from live benchmarks on real HuggingFace datasets with fresh pod isolation (n=15 per cell).

TTFT speedup vs cold prefill

Context	Cold TTFT	SemBlend TTFT	Speedup
4K	2,102 ms	433 ms	4.9x
8K	3,816 ms	539 ms	7.1x
12K	5,655 ms	648 ms	8.7x
16K	7,635 ms	760 ms	10.0x
24K	11,977 ms	972 ms	12.3x

SemBlend TTFT stays under 1 second regardless of context length. Speedup scales linearly because cold prefill grows with context while SemBlend loads cached KV in constant time.

Multi-dataset validation

Identical speedups across content types -- SemBlend is content-agnostic:

Dataset	Content Type	8K Speedup	16K Speedup	24K Speedup
XSum	News summaries	7.1x	10.0x	12.3x
CNN/DailyMail	Long-form journalism	7.1x	9.4x	12.2x
MultiNews	Multi-document news	--	9.3x	--

Quality

Quality validated across 5 datasets, 4-5 context lengths each, with PPL ratio + LLM-as-judge faithfulness scoring (360 total runs):

Dataset	PPL Range	Status	Judge (Cold)	Judge (SemBlend)	Faithful
XSum	1.018-1.054	PASS	0.84	0.84	100%
CNN/DailyMail	1.011-1.049	PASS	0.87	0.86	97%
WikiHow	0.987-1.037	PASS	0.82	0.84	97%
MultiNews	0.958-1.064	PASS	0.79	0.78	100%
SAMSum	1.140-1.198	ELEVATED	0.78	0.86	87%

PPL < 1.065 for 4/5 datasets at all lengths. SAMSum shows elevated PPL due to short dialogue turns, but LLM-as-judge rates SemBlend output higher than cold (0.86 vs 0.78). 24 dataset-length cells, 360 total runs.

Installation

pip install semblend            # CPU-only core (numpy + rapidfuzz)
pip install semblend[vllm]      # + vLLM/LMCache integration
pip install semblend[sglang]    # + SGLang integration
pip install semblend[embedder]  # + sentence-transformers (MiniLM GPU)

Quick Start: vLLM + LMCache

Integrates via LMCache's KVConnectorBase_V1 — no patching required.

pip install semblend[vllm] vllm lmcache

vllm serve Qwen/Qwen2.5-7B-Instruct-AWQ \
  --kv-transfer-config '{
    "kv_connector": "SemBlendConnectorV1",
    "kv_connector_module_path": "semblend.integration.vllm.connector_v1",
    "kv_role": "kv_both"
  }'

CacheBlend support: For selective layer recomputation (CacheBlend), vLLM must expose the loaded model to KV connectors via initialize_worker_connector(). This is available in vLLM builds that include PR #37339. Without it, SemBlend's semantic matching and KV injection still work — only CacheBlend's per-layer recomputation is unavailable.

Quick Start: SGLang

pip install semblend[sglang] sglang

# CLI launcher — applies the RadixCache patch automatically
semblend-sglang --model-path Qwen/Qwen2.5-7B-Instruct --host 0.0.0.0 --port 8000

Or programmatically — call before SGLang initializes:

from semblend.integration.sglang.radix_patcher import patch_radix_cache
patch_radix_cache()
# ... start SGLang server ...

A first-class SemanticPrefixProvider interface (no patching) is in progress upstream.

Configuration

Variable	Default	Description
SEMBLEND_ENABLED	1	Enable semantic donor search
SEMBLEND_MIN_SIMILARITY	0.60	Cosine similarity threshold
SEMBLEND_EMBEDDER	minilm	minilm (auto GPU) · onnx_gpu
SEMBLEND_FUZZY_CHUNKS	0	Fuzzy chunk matching for shifted prefixes

How It Works

Request → Embed (2–15ms) → Search (1ms) → Align (1ms) → Inject KV
              ↓                 ↓              ↓
         MiniLM-L6-v2    cosine search   MD5 chunk hash
         GPU (ONNX RT)   donor store     256-token boundary
         segmented pool

1. Embed — full-document segmented embedding on GPU via ONNX-runtime. Long prompts are split into overlapping 256-token windows, embedded in parallel, and mean-pooled into a single vector. 100% content coverage at any prompt length (~2ms short, ~10ms at 8K, ~15ms at 32K).
2. Search — brute-force cosine similarity against the donor store (<1ms at 1K donors; CAGRA GPU ANN for larger pools)
3. Align — MD5 chunk hashing finds reusable 256-token KV chunks; optional fuzzy matching handles shifted boundaries
4. Inject — donor token IDs substituted into the request; LMCache/RadixCache retrieves cached KV; RoPE correction applied in-place on K tensors

When SemBlend Helps

Most effective when prompts share a large common context:

• Document Q&A / RAG — same retrieved passages, different questions
• Summarization — same article, different instruction phrasing
• Multi-turn dialogue — conversation history prefix reused across turns
• Code completion — shared repository context across requests

Dissimilar workloads (code generation from scratch, fully novel queries) see ~4% overhead with 0% hit — negligible in practice.

Contributing

See CONTRIBUTING.md.

License

Apache License 2.0.

Built at WorldFlow AI. For enterprise support contact [e-mail].