gemma4-adaptive-router 0.1.0

Complexity + VRAM-aware routing for local dual-tier LLM deployments

gemma4-adaptive-router

Complexity + VRAM-aware routing for local dual-tier LLM deployments.
The only public implementation of sub-millisecond complexity scoring coupled to real-time VRAM scheduling for consumer GPU on-prem inference.

Install

pip install gemma4-adaptive-router

Quickstart

from adaptive_router import AdaptiveRouter, RoutingConfig
import time

config = RoutingConfig(
    complexity_threshold=0.65,   # score >= this → tier_high
    vram_headroom_gb=1.5,        # free VRAM below this → force tier_low
    latency_sla_ms=2000.0,       # EMA latency above this → force tier_low
    sla_warmup_seed_ms=800.0,    # seed EMA to avoid cold-start burst on tier_high
)

router = AdaptiveRouter(config)

# Route a query
tier = router.route("Explain the proof of Fermat's Last Theorem step by step")
# → "tier_high" (complex math query, VRAM available)

# After you get the response, report the actual latency so the SLA rule adapts
t0 = time.monotonic()
# ... call your LLM endpoint ...
router.observe(tier, latency_ms=(time.monotonic() - t0) * 1000)

# Clean up background VRAM monitor thread
router.shutdown()

Architecture

┌─────────────────────────────────────────────────────────────┐
│                      adaptive_router/                       │
│                                                             │
│  Layer 1: ComplexityScorer                                  │
│  ├── Rule-based, 6 dimensions, sub-ms, zero external calls  │
│  ├── math (0.25) · code (0.25) · depth (0.20)              │
│  ├── tokens (0.15) · entities (0.10) · negation (0.05)     │
│  └── Output: score in [0.0, 1.0]                           │
│                                                             │
│  Layer 2: VRAMMonitor (daemon thread)                       │
│  ├── pynvml direct — no subprocess nvidia-smi overhead     │
│  ├── Polling at 10-20ms with atomic shared state           │
│  └── Thread-safe VRAMState (free_gb, used_gb, util_pct)    │
│                                                             │
│  Layer 3: RoutingDecision (chain of rules)                  │
│  ├── complexity_rule: score < threshold → tier_low          │
│  ├── vram_rule: free_gb < headroom → tier_low               │
│  ├── sla_rule: EMA latency > SLA → tier_low                 │
│  └── Default fallthrough: tier_high                         │
└─────────────────────────────────────────────────────────────┘

Configuration reference

Field	Type	Default	Effect
complexity_threshold	float	0.65	Score ≥ this routes to tier_high
vram_headroom_gb	float	1.5	Free VRAM below this forces tier_low
latency_sla_ms	float	2000.0	EMA latency above this forces tier_low
vram_poll_interval_ms	int	15	VRAM polling frequency
sla_warmup_seed_ms	float	0.0	EMA seed at startup — set to p50 of tier_high to avoid cold-start burst

Load from YAML:

from adaptive_router.config import load_config

config = load_config("router_config.yaml")

# router_config.yaml
complexity_threshold: 0.65
vram_headroom_gb: 1.5
latency_sla_ms: 2000.0
vram_poll_interval_ms: 15
sla_warmup_seed_ms: 800.0

Deploy as FastAPI proxy

python -m adaptive_router.middleware \
    --tier-high-url http://llama-cpp:8080/v1 \
    --tier-low-url  http://vllm:8000/v1 \
    --port 9000

Exposes /v1/chat/completions (proxied), /health, and /metrics (Prometheus).

Custom routing rules

from adaptive_router import AdaptiveRouter, RoutingConfig, RouterState

def my_rule(query: str, state: RouterState):
    # Force tier_high for any query mentioning "contract"
    if "contract" in query.lower():
        return "tier_high"
    return None  # abstain, let next rule decide

router = AdaptiveRouter(config, rules=[my_rule])

Known limitations

1. 16GB does not fit 26B in vLLM natively. The router mitigates this by routing complex queries to llama.cpp. The real fix for 200 concurrent users wanting 26B is dual-GPU.
2. Blackwell SM120 rough edges. FP8 KV and MTP require specific workarounds. See VRAM-REALITY-CHECK.md.
3. EXL2 is not designed for multi-user production. TabbyAPI maintainers state this explicitly. It's in benchmarks for completeness only.
4. SGLang does not always win. RadixAttention benefits depend on prefix overlap. See RADIXATTENTION-OPERATIVE-TABLE.md.
5. MTP in SM120 is not plug-and-play. With BF16 + workarounds it works; with NVFP4 it produces garbage.
6. Cold-start burst on tier_high. With sla_warmup_seed_ms=0.0, the EMA starts at 0ms — below any SLA. The first ~15-20 complex queries all hit tier_high before the EMA reflects real latency. Set sla_warmup_seed_ms to your expected p50.
7. This is v0.1. Functional and tested, but not battle-tested at thousands of users. It's the starting point.

Running tests

pip install -e ".[dev]"
pytest tests/ -v  # No GPU required — pynvml is mocked in all fixtures