What Is the Size of the Sliding Window in Needle 2's Memory Management?

Needle 2 uses a 256‑token sliding window for its KV cache, capping total memory usage at approximately 28 MiB regardless of how long a conversation runs.

Needle 2, the open-source language model from Cactus Compute, implements a fixed-size context window to deliver efficient inference on resource-constrained devices. According to the cactus-compute/needle repository, this architectural decision pins tool definitions as KV sinks while rotating the remaining cache, keeping the model's footprint constant even during extended interactions.

The 256‑Token Sliding Window Architecture

The size of the sliding window in Needle 2's memory management is hardcoded to 256 tokens. This limit applies to the model’s KV (key-value) cache, which stores intermediate attention states during inference. By fixing the window at 256 tokens and pinning tool descriptions as permanent KV sinks, the system prevents memory growth as conversations lengthen. The repository’s README.md explicitly states that this design keeps "total memory stays near 28 MB no matter how long the conversation runs."

How the Window Size Is Defined in Code

Binary Export Format Configuration

The sliding window width is encoded directly into Needle 2’s serialized model files. In needle/model/export.py, the header structure contains a field named kv_window that records the window size used during training. Lines 27–30 of this file define how this metadata is written to the binary export, ensuring that downstream runtimes allocate the correct cache dimensions when loading a model.

Model Configuration Class

Developers can inspect or override the window size through the TransformerConfig class located in needle/model/architecture.py. This configuration object exposes a sliding_window attribute that defaults to 256 tokens. When instantiated, Needle agents read this value to initialize their KV cache buffers, though the binary exporter independently verifies the setting via the kv_window header field to maintain consistency across deployments.

Working with the Sliding Window in Practice

Default Configuration

The standard Needle 2 installation requires no manual tuning to utilize the 256-token window. Instantiating an agent with default parameters automatically applies the memory-efficient configuration:

import needle

# Uses the default 256-token sliding window automatically

agent = needle.Needle(tools=[...])
response = agent.run("What is the weather in London today?")
print(response["results"])

Customizing the Window Size

While 256 tokens is the recommended size for the 28 MiB memory target, you can specify a different window by constructing a custom TransformerConfig. Note that deviating from the default may alter the model's memory characteristics:

import needle
from needle.model.architecture import TransformerConfig

cfg = TransformerConfig(
    d_model=512,
    num_heads=8,
    num_layers=12,
    max_seq_len=1024,
    sliding_window=256,  # Matches default; modify only if necessary

)

agent = needle.Needle(config=cfg, tools=[...])

Runtime Inspection

To verify the active cache limits at runtime, access the sliding_window property of the loaded configuration:

print(f"KV cache window: {agent.config.sliding_window} tokens")

# → KV cache window: 256 tokens

Summary

• Needle 2 implements a fixed 256-token sliding window for KV cache management, as documented in the repository README and encoded in the binary export format.
• Tools are pinned as KV sinks within this window, ensuring the 28 MiB memory footprint remains constant regardless of conversation duration.
• The kv_window header field in needle/model/export.py stores the window size for serialized models, while TransformerConfig in needle/model/architecture.py governs runtime behavior.
• Developers can inspect or override the value via the configuration API, though the default 256-token setting is optimized for the architecture’s memory constraints.

Frequently Asked Questions

What is the default sliding window size in Needle 2?

The default is 256 tokens. This value is baked into the TransformerConfig defaults and the binary export header (kv_window) in needle/model/export.py, ensuring that fresh installations and exported models both respect this limit.

How does Needle 2 maintain constant memory usage during long conversations?

Needle 2 pins tool definitions as KV sinks that remain resident in the cache while rotating the 256-token sliding window for conversational context. Because older tokens are evicted rather than accumulated, the KV cache never exceeds its fixed allocation, keeping RAM usage near 28 MiB.

Can I change the sliding window size in Needle 2?

Yes. You can pass a custom sliding_window value when constructing a TransformerConfig object from needle.model.architecture. However, the default 256 tokens is specifically tuned to achieve the advertised 28 MiB memory target; larger windows will increase RAM consumption proportionally.

Where is the sliding window size stored in exported Needle 2 models?

The size is stored in the kv_window header field defined in needle/model/export.py. This field records the window width at export time, allowing inference engines to validate or allocate the correct cache size when loading the binary file.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}