kvboost 0.2.0

Chunk-level KV cache reuse for faster HuggingFace inference

KVBoost

Chunk-level KV cache reuse for HuggingFace inference.
5-48x TTFT reduction on 3B+ models with repeated long context. 3 lines to integrate.

Quick Start • Benchmarks • Installation • API Reference • Examples • How it works • Docs

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When KVBoost Helps

Condition	Expected TTFT Speedup
Multi-turn conversation, 8+ turns, 3B+ model	10-48x
Code context / document reuse, 800+ tokens	15-21x
RAG document reuse, ~500 tokens	1-2x
System prompt reuse, ~250 tokens	0.3-0.5x (overhead > savings)
Any workload, 0.5B model	< 1x (overhead exceeds prefill)

Rule of thumb: Benefits appear on 3B+ models with 500+ token shared context. Below this, caching overhead exceeds prefill savings. The peak 47.9x is at 1350 tokens on Qwen2.5-3B — see benchmarks for full data.

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How it works

What normally happens inside an LLM

When you send a prompt to a language model, the model reads every token before it can write anything back. Internally, each layer of the model computes two tensors for every token: a key and a value (K and V). These K/V tensors are what the model uses to "remember" earlier parts of the text when deciding what comes next. The full set of them is called the KV cache.

For a 3B-parameter model reading 1,000 tokens, that first read (called prefill) takes roughly 1-3 seconds on a MacBook. The K/V tensors are computed, used to generate the first output token, and then kept around so the model doesn't have to re-read the prompt for subsequent tokens. Each new output token just adds one more K/V pair to the cache. That part is fast.

The problem is what happens on the next request. You send the same system prompt plus a different question. The model throws away everything from last time and reads the entire prompt again from scratch. Another 1-3 seconds of prefill, even though 90% of the prompt is identical. Multiply that by hundreds of requests and you're spending most of your GPU time re-reading text the model has already seen.

What KVBoost changes

KVBoost saves those K/V tensors after each request and reuses them on the next one. The mechanics of how it does that have a few moving parts, because "just save and reload" has correctness problems that will silently produce wrong outputs if you're not careful.

Step 1: Split the prompt into chunks

ChunkRegistry.split() in chunk_registry.py walks through the token list and cuts it into fixed-size blocks (default 128 tokens). A 1,000-token prompt becomes 7 full chunks plus a 104-token tail.

Step 2: Hash each chunk (two hashes, not one)

Each chunk gets two identifiers, computed in models.py:

prefix_hash  = SHA256(previous_chunk's_hash + this_chunk's_token_bytes)
content_hash = SHA256(this_chunk's_token_bytes)

Why two? Suppose the sentence "The transformer architecture uses self-attention" appears as chunk 3 in conversation A and chunk 1 in conversation B. The tokens are identical, so the content hash is the same. But the prefix hash is different because conversation A's hash includes chunks 1 and 2 chained before it.

This matters because the K/V tensors for that sentence in conversation A were computed with the model having already read conversation A's earlier text. Those tensors encode "what these tokens mean, given everything before them." Loading them into conversation B, where the preceding text is completely different, would be wrong.

The prefix hash is the primary lookup key. It only matches when the tokens and all preceding chunks are identical. The content hash is a fallback. It matches on the tokens alone but flags the result as "approximate" so the engine knows the stored data needs full correction, not just light touch-up.

Step 3: Look up what's already cached

KVCacheManager.find_matching_chunks() in cache_manager.py tries the prefix hash first. If that misses, it checks the content hash via a secondary index. The result comes back wrapped in a ChunkMatch object that carries an approximate flag (True if it was a content-hash fallback).

The cache itself is a Python OrderedDict. When it fills up, eviction is frequency-based: chunks that appeared in many requests (your system prompt) have a high count and stay put. Chunks that appeared once (a one-off document) stay at count 1 and get evicted first.

Step 4: Separate cached tokens from live tokens

PromptAssembler in prompt_assembler.py takes the cache lookup results and splits the prompt into two regions: the prefix covered by cache hits (stored K/V data exists) and the "live" tail (new tokens that the model hasn't seen before).

If chunks 1-7 all hit cache and only the last 104 tokens are new, those 104 tokens are the only ones the model needs to process. The cached K/V tensors for the first 896 tokens get loaded from memory instead of recomputed.

Step 5: Fix the stitching errors

This is the part that makes the difference between "works" and "produces subtly wrong text."

Each cached chunk was processed independently when it was first created. Token 129 (first token of chunk 2) never attended to token 1 (first token of chunk 1) during that original computation. Its K/V values reflect a model that only saw tokens 1-128, not the full prompt. When you stitch chunks 1 and 2 together and hand them to the model as if they were one continuous sequence, those values at the boundaries are slightly off.

KVBoost has two ways to correct this, configured via recompute_strategy:

"selective" (the default) re-runs the model on the last 16 tokens at each chunk boundary, this time with all preceding chunks visible. The corrected K/V values replace the stale ones. Simple, but it only fixes boundary tokens. A token in the middle of chunk 3 that happens to depend on something in chunk 1 won't get corrected.

"cacheblend" takes a different approach. It runs one forward pass through the entire stitched K/V, computes the cosine distance between each token's stored values and what the values would be with full context, and recomputes only the ~15% of tokens with the highest deviation. This catches problems inside chunks, not just at edges. The implementation is in cacheblend.py.

If any chunk was an approximate match (content hash hit, not prefix hash), CacheBlend runs automatically regardless of your configured strategy. When the position encodings are wrong, boundary-only repair isn't enough.

Step 6: Run the model on the live tokens only

The corrected cached K/V and the live suffix tokens go into a single model.forward() call in engine.py. HuggingFace models accept a past_key_values argument that tells them "pretend you already processed this many tokens." The model reads the live tokens, attends to the cached K/V as context, and produces the first output token. From there, autoregressive decoding continues token by token as normal.

After generation finishes, _store_prompt_chunks() saves any chunks that weren't already in cache. So the next request with overlapping text will hit cache without needing an explicit warm() call.

Why it produces identical outputs

Under greedy decoding (temperature=0, always pick the highest-probability token), the K/V tensors from a cached-and-corrected path are mathematically equivalent to the K/V tensors from a full re-read. The argmax token at every step is the same. The benchmarks verify this by running both paths on the same prompts and comparing outputs token by token.

Under sampling (temperature > 0), the outputs aren't identical because sampling is inherently random. But the probability distributions are the same, which you can verify by measuring KL divergence between the two paths' logit distributions.

Where the data lives

Cached K/V tensors sit in a Python dict in CPU RAM by default. When the model needs them, they're moved to the GPU.

If you set kv_cache_bits=8, the tensors get compressed to int8 before storage. Keys are quantized per-channel, values per-token (the asymmetry from the KIVI paper, ICML 2024). This halves RAM usage with near-zero accuracy loss. kv_cache_bits=4 is available for 4x compression but should be validated with verify_correctness() first.

When the in-memory cache fills up, evicted chunks are written to a single pre-allocated binary file on disk. A JSON index maps chunk hashes to byte offsets in that file. When a disk-tier chunk gets a cache hit, it's read back and promoted to RAM.

Full API docs: kvboost.readthedocs.io

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Features

Identical outputs	Greedy decoding produces the same text as baseline
3 lines to integrate	from_pretrained / warm / generate
11+ architectures	Llama, Qwen, Gemma, Mistral, Phi -- any RoPE model on CUDA, MPS, or CPU
Two recompute strategies	Selective boundary recompute or CacheBlend deviation-guided
Two-tier storage	Hot RAM (frequency-based eviction) + optional disk-backed cold tier
Prefix-chained keys	vLLM-style hash chaining for positional correctness

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Installation

pip install kvboost

From source:

git clone https://github.com/pythongiant/kvboost.git
cd kvboost
pip install -e .

Requirements: Python >= 3.9, PyTorch >= 2.1, Transformers >= 4.38

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Quick Start

from kvboost import KVBoost

# 1. Load any HuggingFace causal LM
engine = KVBoost.from_pretrained("Qwen/Qwen2.5-3B")

# 2. Cache your system prompt / document / few-shot examples once
engine.warm("You are a helpful coding assistant. Always provide concise answers...")

# 3. Generate -- cached prefix is reused automatically
result = engine.generate(
    "You are a helpful coding assistant. Always provide concise answers...\n\n"
    "User: How do I reverse a linked list?\n"
    "Assistant:",
    max_new_tokens=128,
)

print(result.output_text)
print(f"TTFT: {result.ttft_ms:.1f}ms | Cache reuse: {result.kv_reuse_ratio:.0%}")

---

Benchmarks

Qwen/Qwen2.5-3B (float16) on MacBook Air M-series, 16GB RAM, MPS backend. Chunk size 128, greedy decoding.

Multi-Turn Conversation

Baseline TTFT scales linearly with history. KVBoost stays flat at ~62ms.

Turn	Tokens	Baseline	KVBoost	Reuse	Speedup
1	232	35ms	31ms	0%	1.1x
2	353	149ms	79ms	36%	1.9x
3	495	194ms	60ms	52%	3.2x
4	621	374ms	62ms	62%	6.0x
5	762	658ms	57ms	67%	11.6x
6	946	1,228ms	63ms	68%	19.6x
7	1,113	1,737ms	64ms	81%	27.2x
8	1,353	2,970ms	62ms	76%	47.9x

Code Context Reuse (~800 tokens)

Query	Baseline	KVBoost	Reuse	Speedup
Q1 (cold)	1,670ms	2,292ms	0%	0.7x
Q2 (warm)	1,577ms	75ms	92%	21.1x
Q3 (warm)	2,133ms	128ms	92%	16.6x

System Prompt Reuse (~250 tokens)

Identical outputs, but prompts are too short for speedup at 3B scale:

Query	Baseline	KVBoost	Reuse	Speedup
Q1 (cold)	40ms	76ms	60%	0.5x
Q2	34ms	75ms	60%	0.4x
Q3	34ms	96ms	60%	0.4x
Q4	34ms	121ms	61%	0.3x

RAG Document Reuse (~500 tokens)

Query	Baseline	KVBoost	Reuse	Speedup
Q1 (cold)	72ms	48ms	0%	1.5x
Q2 (warm)	78ms	51ms	86%	1.5x
Q3 (warm)	47ms	55ms	85%	0.9x

Few-Shot Classification (~500 tokens)

Review	Baseline	KVBoost	Reuse	Speedup
Review 1	75ms	53ms	81%	1.4x
Review 2	52ms	54ms	81%	1.0x
Review 3	40ms	52ms	81%	0.8x

Pattern: At ~250-500 tokens, KVBoost is roughly break-even. The cache overhead (~60-100ms) matches the prefill savings. Speedups become dramatic above ~600 tokens where prefill dominates.

When Does It Help? (Model Size Matters)

The same examples on Qwen2-0.5B tell the opposite story -- cache overhead exceeds prefill savings because the model is too small for prefill to be a bottleneck.

Qwen2-0.5B results (click to expand)

Qwen/Qwen2-0.5B (float16), chunk_size=64, same MacBook Air.

RAG Document Reuse -- high reuse, but KVBoost is slower:

Query	Baseline	KVBoost	Reuse	Speedup
Q1 (cold)	244ms	12ms	0%	20.3x
Q2 (warm)	29ms	152ms	83%	0.2x
Q3 (warm)	27ms	141ms	81%	0.2x

Code Context Reuse -- same pattern:

Query	Baseline	KVBoost	Reuse	Speedup
Q1 (cold)	216ms	13ms	0%	16.6x
Q2 (warm)	32ms	94ms	74%	0.3x
Q3 (warm)	29ms	41ms	73%	0.7x

Multi-Turn -- never reaches the crossover:

Turn	Tokens	Baseline	KVBoost	Reuse	Speedup
1	23	47ms	12ms	0%	4.0x
2	48	12ms	12ms	0%	1.0x
3	83	55ms	12ms	0%	4.6x
4	110	197ms	100ms	58%	2.0x

Why? At 0.5B, prefill costs ~30ms after MPS kernel warmup -- there's nothing meaningful to save. The cache lookup + CPU-to-MPS transfer + selective recompute overhead (~100ms) exceeds the prefill it replaces.

	Qwen2-0.5B	Qwen2.5-3B
Prefill cost (500 tok)	~30ms	~400ms
Cache overhead	~100ms	~60ms
Break-even	Never (overhead > savings)	~350 tokens
Peak speedup	2.0x (110 tok)	47.9x (1353 tok)

Rule of thumb: KVBoost pays off on 3B+ models with 500+ token prompts. The bigger the model and the longer the prompt, the larger the win.

Methodology validation

Three properties confirm these results reflect genuine cache reuse:

1. Flat TTFT curve -- KVBoost TTFT stays ~62ms from 232 to 1,353 tokens. This is the signature of cache reuse: only live tokens (constant per turn) are processed.

2. Output correctness -- Under greedy decoding, baseline and KVBoost produce identical output text. Corrupted cache tensors would cause divergence.

3. Cold-start control -- Every first query shows 0% reuse and comparable TTFT. Speedup only appears after cache population, ruling out measurement bias.

KVBoost vs MLX LLM

Head-to-head against Apple's Metal-optimized MLX inference framework. Same model (Qwen2.5-3B), same hardware, same prompts.

Workload	Query	HF Baseline	KVBoost	MLX	KV vs HF	KV vs MLX
Chatbot	Q1 (cold)	52,122ms	81ms	60,527ms	640x	743x
	Q2	20,276ms	1,301ms	56,556ms	16x	44x
	Q3	19,571ms	1,308ms	52,906ms	15x	40x
Code	Q1 (cold)	63,369ms	493ms	49,342ms	129x	100x
	Q2	40,635ms	137ms	47,167ms	297x	344x
	Q3	39,529ms	152ms	48,592ms	260x	319x
Multi-turn	Q1	71,835ms	108ms	61,067ms	668x	568x
	Q2	41,743ms	349ms	45,344ms	120x	130x
	Q3	48,858ms	153ms	46,962ms	319x	307x
	Q4	40,834ms	233ms	62,975ms	175x	270x
	Q5	51,666ms	130ms	53,865ms	398x	415x
	Q6	52,664ms	153ms	50,860ms	345x	333x
	Q7	50,676ms	144ms	46,060ms	353x	321x

KVBoost is 100-743x faster than MLX on TTFT across all workloads. MLX shows no cross-request cache reuse -- each prompt is processed from scratch. KVBoost's chunk-level caching eliminates redundant prefill entirely.

Run it yourself

pip install mlx-lm
python benchmarks_and_experiments/benchmark_vs_mlx.py
python benchmarks_and_experiments/benchmark_vs_mlx.py --workload code

KVBoost vs vLLM Prefix Caching (vllm-mlx)

Head-to-head against vLLM-MLX prefix caching on Apple Silicon. vLLM caches system prompt KV and reuses on exact prefix match. KVBoost reuses any matching chunk, including non-prefix interior content.

KVBoost: Qwen2.5-3B float16 (MPS) | vLLM-MLX: Qwen2.5-3B 4-bit (MLX Metal)

Axis 1: Non-Prefix Interior Reuse (KVBoost's differentiator)

Document placed at the start, in the middle, or not at all:

Pattern	Query	HF Baseline	KVBoost	vLLM-MLX	KV vs vLLM
Exact prefix	Q1	51ms	58ms (89%)	1,722ms	29.6x
	Q2	673ms	291ms (90%)	928ms	3.2x
	Q3	226ms	72ms (88%)	856ms	11.9x
Interior	Q1	307ms	33ms (0%)	1,219ms	36.8x
	Q2	321ms	103ms (83%)	1,214ms	11.8x
	Q3	324ms	57ms (82%)	1,294ms	22.8x
No reuse	Q1	287ms	33ms	1,346ms	40.7x
	Q2	335ms	33ms	1,313ms	39.5x
	Q3	313ms	33ms	1,279ms	38.7x

Axis 2: Cold-Start Overhead

Cache State	HF Baseline	KVBoost	vLLM-MLX
Cold	206ms	32ms	777ms
Warm Q2	204ms	44ms (90%)	891ms
Warm Q3	210ms	62ms (88%)	942ms

Axis 3: Break-Even Prompt Length

Prompt Length	Baseline	KVBoost (cold)	KVBoost (warm)	vLLM (cold)	vLLM (warm)
~100 words	154ms	28ms	124ms (0%)	549ms	438ms
~250 words	244ms	33ms	37ms (89%)	1,039ms	849ms
~500 words	403ms	76ms	48ms (88%)	1,882ms	1,960ms
~1000 words	2,329ms	2,218ms	242ms (98%)	21,047ms	61,131ms
~2000 words	76,864ms	7,302ms	1,452ms (98%)	49,015ms	66,714ms

Key findings:

• KVBoost is 3-41x faster than vLLM-MLX on TTFT across all patterns
• On interior reuse (document in the middle), vLLM gets zero cache hits while KVBoost achieves 82-83% reuse -- this is the core differentiator
• At 2000 words warm, KVBoost is 46x faster than vLLM-MLX (1.5s vs 66.7s)
• Even with no reuse possible, KVBoost's HF baseline (33ms) beats vLLM-MLX (1.3s) due to MPS vs MLX Metal overhead differences
• Overall mean TTFT: KVBoost 564ms vs vLLM-MLX 9,928ms

Run it yourself

pip install vllm-mlx
python benchmarks_and_experiments/benchmark_vs_vllm.py
python benchmarks_and_experiments/benchmark_vs_vllm.py --axis non_prefix
python benchmarks_and_experiments/benchmark_vs_vllm.py --skip-vllm  # KVBoost only

---

API Reference

KVBoost.from_pretrained(model_name, **kwargs)

Factory method. Loads a HuggingFace model and tokenizer. Validates architecture compatibility at load time.

Parameter	Type	Default	Description
model_name	str	"TinyLlama/TinyLlama-1.1B-Chat-v1.0"	HF decoder-only causal LM (must use RoPE)
strict	bool	True	Raise on unsupported architectures, warn on untested
chunk_size	int	128	Tokens per cache chunk
max_chunks	int	128	Max chunks in RAM before LRU eviction
recompute_strategy	str	"selective"	"selective", "cacheblend", or "none" (see below)
recompute_overlap	int	16	Tokens to recompute at seams (selective only)
recompute_ratio	float	0.15	Fraction of tokens to recompute (cacheblend only)
kv_cache_bits	int	16	16 (float16), 8 (int8), or 4 (int4) -- see below
disk_cache_dir	str | None	None	Path for disk-backed cold storage
device	str | None	None	"cuda", "mps", "cpu", or auto-detect

KV cache quantization:

Compresses cached KV tensors using KIVI-style asymmetric quantization (ICML 2024): key cache is quantized per-channel (handles channel-specific outliers), value cache is quantized per-token (handles token-specific outliers).

Precision	Compression	Per-chunk (Qwen2.5-3B)	128 chunks	Quality
16 (float16)	1x	9.4 MB	1.2 GB	Baseline
8 (int8)	2x	4.7 MB	0.6 GB	Near-lossless (max error ~0.016)
4 (int4)	4x	2.4 MB	0.3 GB	Aggressive (validate with verify_correctness())

from kvboost import KVBoost

# int8 -- 2x RAM savings, near-lossless (recommended for memory-constrained)
engine = KVBoost.from_pretrained("Qwen/Qwen2.5-3B", kv_cache_bits=8)

# int4 -- 4x RAM savings, aggressive (validate before trusting)
engine = KVBoost.from_pretrained("Qwen/Qwen2.5-3B", kv_cache_bits=4)
assert engine.verify_correctness()

Recompute strategies:

Strategy	How it works	When to use
"selective"	Recomputes last R tokens at each chunk boundary	Default, safe baseline
"cacheblend"	Measures per-token KV deviation, recomputes only the ~15% that actually changed	Better quality/speed trade-off on long prompts
"none"	Skips recompute entirely	Maximum speed, acceptable when chunks are from the same original encoding

from kvboost import KVBoost

# Default: selective recompute at boundaries
engine = KVBoost.from_pretrained("Qwen/Qwen2.5-3B")

# CacheBlend: smarter, recomputes only deviated tokens
engine = KVBoost.from_pretrained("Qwen/Qwen2.5-3B", recompute_strategy="cacheblend")

# No recompute: fastest, use when chunks share original context
engine = KVBoost.from_pretrained("Qwen/Qwen2.5-3B", recompute_strategy="none")

engine.warm(text, position_offset=0) -> int

Pre-cache fixed-size chunks from text. Returns number of new chunks stored. Call this for content reused across requests: system prompts, documents, few-shot examples.

engine.generate(prompt, **kwargs) -> GenerationResult

Generate text with automatic KV cache reuse.

Parameter	Type	Default	Description
prompt	str	--	Full prompt including any cached prefix
max_new_tokens	int	64	Max tokens to generate
mode	GenerationMode	CHUNK_KV_REUSE	BASELINE, PREFIX_CACHE, or CHUNK_KV_REUSE
temperature	float	1.0	Sampling temperature
do_sample	bool	False	Greedy (False) or sampling (True)

GenerationResult

Field	Type	Description
output_text	str	Generated text
ttft_ms	float	Time to first token (ms)
total_ms	float	End-to-end latency (ms)
tokens_per_sec	float	Decode throughput
kv_reuse_ratio	float	Fraction of prompt served from cache
prompt_tokens	int	Total prompt token count
cached_tokens	int	Tokens served from cache

engine.cache_stats() -> dict

Returns: hot_chunks, hot_memory_mb, cache_hits, approximate_hits, cache_misses, hit_rate, exact_hit_rate.

engine.verify_correctness(max_new_tokens=32) -> bool

Runs a quick greedy-decode comparison (baseline vs cached) on a synthetic prompt. Returns True if outputs match. Use this to validate untested architectures.

engine = KVBoost.from_pretrained("some/untested-model", strict=False)
assert engine.verify_correctness(), "KV cache stitching produces wrong outputs!"

Model Compatibility

KVBoost's KV cache stitching requires RoPE positional encoding with explicit position_ids support. Models using ALiBi, learned absolute embeddings, or sliding window attention are not compatible.

Status	Architectures
Supported	Llama, Qwen2, Qwen2.5, Gemma, Gemma2, Mistral (full attn), Phi, Phi3, StableLM, InternLM
Unsupported	GPT-2, GPT-Neo, GPT-NeoX, MPT, Falcon, BLOOM
Conditional	Mistral with sliding_window != None -- blocked

# Supported model -- loads normally
engine = KVBoost.from_pretrained("Qwen/Qwen2.5-3B")

# Unsupported model -- raises ValueError with explanation
engine = KVBoost.from_pretrained("gpt2")
# ValueError: GPT-2 uses learned absolute positional embeddings...

# Unknown model -- warns, user can self-certify
engine = KVBoost.from_pretrained("some/new-rope-model", strict=False)
assert engine.verify_correctness()

# Skip all checks (you know what you're doing)
engine = KVBoost.from_pretrained("some/model", strict=False)

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Examples

The examples/ directory contains runnable demos for 5 real-world patterns. Configuration is driven by a .env file -- swap models without touching code.

cp examples/.env.example examples/.env   # configure model, device, etc.
python examples/run.py                    # run all examples
python examples/run.py --example rag      # run one
python examples/run.py --model Qwen/Qwen2.5-3B  # override model
python examples/run.py --list             # see all options

Example	Pattern	What it demonstrates
chatbot	System prompt reuse	Fixed instructions cached, reused across queries
rag	RAG document reuse	Same retrieved doc, multiple questions
fewshot	Few-shot classification	Cached examples, only new inputs need compute
multiturn	Multi-turn conversation	Growing history with increasing cache reuse
code	Code context reuse	Shared code file queried multiple times

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Experimental Results (TinyLlama 1.1B)

Expand full experiment suite (10 experiments)

All results on TinyLlama-1.1B-Chat, Apple Silicon (MPS). Full JSON data in benchmarks_and_experiments/results/.

---

Experiment 1: Scaling Across Modes

Workload	Mode	TTFT (ms)	Speedup
System prompt	Baseline	191.7	1.0x
System prompt	Prefix cache	66.5	2.9x
System prompt	Chunk KV reuse	68.9	2.8x
RAG document	Baseline	75.2	1.0x
RAG document	Prefix cache	95.7	0.8x
RAG document	Chunk KV reuse	25.5	3.0x

Chunk KV reuse delivers its biggest win on RAG workloads (3x) where non-prefix chunk matching kicks in.

---

Experiment 2: Latency Breakdown

Stage	Baseline	Chunk KV
Cache lookup	0.0	13.3
Selective recompute	0.0	553.8
Live token prefill	1431.8	703.1
Decode	747.6	605.4
Total	2179.4	1875.8

Selective recompute is the dominant overhead. Recompute optimization is the highest-leverage improvement opportunity.

---

Experiment 3: Hyperparameter Sweep

Chunk Size	Overlap	TTFT (ms)	Hit Rate	MB/chunk
64	0	21.2	0.750	0.79
64	16	20.3	0.750	0.79
128	0	19.7	0.429	2.23
128	16	16.1	0.429	2.23

Smaller chunks (64) achieve higher hit rates at lower memory cost.

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Experiment 4: Output Quality

Test	Result
Greedy output match (with recompute)	100%
Greedy output match (without recompute)	100%
Long-range dependency match	100%
Sampling Hellinger distance (temp=0.5)	Near 0

Under greedy decoding, chunk KV reuse produces identical outputs to baseline.

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Experiment 5: Realistic Workloads

Multi-turn (8 turns): KV reuse becomes faster at turn 4 (50% reuse), reaching 43% faster at turn 7 (90% reuse).

Server simulation (20 queries): 76% TTFT reduction, 2.3x throughput.

---

Experiment 6: Memory Analysis

Metric	Value
Speedup factor	29.9x
Cache memory	15.5 MB
Break-even	12 requests

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Experiment 7: Comparison with Existing Systems

Feature	KVBoost	vLLM	SGLang
Prefix caching	Y	Y	Y
Non-prefix chunk reuse	Y	N	Partial
Selective boundary recompute	Y	N	N
Content-addressable keys	Y	Y	N
Disk-backed cold storage	Y	N	N
Semantic chunking	Y	N	N
Continuous batching	N	Y	Y
PagedAttention	N	Y	Y

KVBoost is complementary to production serving features like PagedAttention.

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Experiment 8: Chunking Strategies

Strategy	System Prompt TTFT	RAG Hit Rate	Memory
Fixed	32.6ms	65.0%	28.4 MB
Semantic	20.9ms	61.9%	27.4 MB
Document	34.7ms	21.4%	39.9 MB

Semantic chunking is 36% faster for system prompts.

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Experiment 9: Cache Hit Rate Under Traffic

Pattern	Hit Rate	Mean TTFT
Uniform	22%	138.7ms
Zipfian	38%	68.5ms
Temporal	62%	65.0ms

Cache warms up in ~20 requests. Temporal locality matches real API traffic.

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Experiment 10: Statistical Rigor

Cold-start TTFT: 368ms (baseline) vs 118ms (KVBoost) = 3.1x improvement.

At TinyLlama scale, overhead can offset savings for short prompts. Benefits grow with model size where prefill cost dominates.

Summary of Findings

1. 47.9x TTFT speedup on multi-turn conversations with 1350+ tokens
2. 21x speedup on code context reuse (~800 tokens)
3. Identical outputs under greedy decoding (mathematically equivalent)
4. Cache pays for itself in 12 requests with only 15.5 MB overhead
5. Semantic chunking outperforms fixed by 36% for system prompts
6. Benefits scale with prompt length -- gains appear above ~500 tokens

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Running Experiments

cd benchmarks_and_experiments

python run_all.py              # full suite (~55 min)
python run_all.py --quick      # quick mode (~15 min)
python run_all.py --experiments 2,4,10  # specific experiments

Results are saved to benchmarks_and_experiments/results/.

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Contributing

Contributions are welcome! Areas of interest:

• Recompute optimization -- selective recompute is the current bottleneck
• Batch inference -- extending cache reuse to batched requests
• PagedAttention integration -- combining with vLLM-style memory management
• Quantized KV storage -- int8/int4 cache tensors for lower memory footprint

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License

MIT