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Prefix caching lets LocalHFBackend store the key-value (KV) attention states from a forward pass and reuse them in later calls, skipping the prefill computation for content that hasn’t changed. This is useful when many calls share a large common prefix — a system prompt, a long document, or a fixed instruction header. Prerequisite: This feature is specific to LocalHFBackend. Server-side backends (Ollama, OpenAI, vLLM) manage their own KV caching internally.

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Enable caching on the backend

Pass a SimpleLRUCache to LocalHFBackend at construction time: 
from mellea.backends.huggingface import LocalHFBackend
from mellea.backends.cache import SimpleLRUCache

backend = LocalHFBackend(
    model_id="ibm-granite/granite-3.3-2b-instruct",
    cache=SimpleLRUCache(capacity=5),
)
capacity is the maximum number of cached KV blocks held in GPU memory at once. When the cache is full, the least recently used block is evicted and its GPU memory freed automatically. To disable caching entirely (useful for benchmarking): 
backend = LocalHFBackend(
    model_id="ibm-granite/granite-3.3-2b-instruct",
    use_caches=False,
)

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Mark a CBlock for caching

Caching is opt-in at the content level. Set cache=True on a CBlock to tell the backend to prefill that block and store its KV state: 
from mellea.core.base import CBlock

system_doc = CBlock("You are a medical triage assistant. Always respond in structured JSON.", cache=True)
On the first call that includes this CBlock, the backend runs a forward pass and stores the resulting DynamicCache. On subsequent calls containing the same block, the cached states are retrieved and merged with the non-cached suffix — no redundant prefill.

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How KV smashing works

When a prompt contains a mix of cached and uncached blocks, Mellea:
  1. Tokenises each block independently.
  2. Runs forward passes on uncached blocks.
  3. Retrieves stored DynamicCache for cached blocks.
  4. Smashes (concatenates) all KV caches along the time axis using merge_dynamic_caches().
  5. Passes the merged cache plus the combined input IDs to the generation step.
The result is identical to a single full-context forward pass, with the prefill cost of cached blocks paid only once.

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Practical example

A pipeline that applies the same long grounding document to many different queries: 
import mellea
from mellea.core.base import CBlock
from mellea.backends.huggingface import LocalHFBackend
from mellea.backends.cache import SimpleLRUCache
from mellea.stdlib.context import ChatContext

backend = LocalHFBackend(
    model_id="ibm-granite/granite-3.3-2b-instruct",
    cache=SimpleLRUCache(capacity=3),
)
m = mellea.MelleaSession(backend=backend, ctx=ChatContext())

# This large document block will be prefilled and cached on first use.
reference = CBlock(open("large_reference_doc.txt").read(), cache=True)

queries = [
    "What are the contraindications listed?",
    "Summarise the dosage table.",
    "List any drug interactions mentioned.",
]

for query in queries:
    result = m.instruct(
        "Using the reference document, answer: {{query}}",
        user_variables={"query": query},
        grounding_context={"reference": reference},
    )
    print(str(result))
    # Output will vary — LLM responses depend on model and temperature.
The reference block is prefilled once. Each subsequent query pays only for its own suffix tokens.

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Cache capacity and memory

Each cached block occupies GPU memory proportional to the block’s token count and the model’s number of layers and attention heads. Choose capacity conservatively:
  • 1–3 for large documents or long system prompts on a single GPU.
  • 5–10 for short, frequently reused blocks with ample VRAM.
The on_evict callback (used internally by LocalHFBackend) frees GPU tensors when a block is evicted, so the cache does not leak memory.

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Disable for benchmarking

To measure true generation time without cache benefits: 
backend.use_caches = False
Or pass use_caches=False at construction. The session behaviour is otherwise identical — disabling caching only affects whether prefill states are stored and reused. See also: HuggingFace Transformers | Intrinsics | LoRA and aLoRA Adapters