# How Needle 2 Manages Memory with a 256-Token Sliding Window

> Discover how Needle 2 manages memory with a 256-token sliding window. Learn how it achieves constant complexity for transformer models regardless of generation length.

- Repository: [Cactus Compute, Inc./needle](https://github.com/cactus-compute/needle)
- Tags: internals
- Published: 2026-09-08

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**Needle 2 caps transformer memory usage by implementing a fixed-size 256-token sliding-window KV-cache that overwrites stale entries via modulo indexing, ensuring constant O(kv_window × num_layers × head_dim) complexity regardless of generation length.**

Needle 2 (the current version of the cactus-compute/needle repository) eliminates unbounded memory growth during inference through aggressive KV-cache quantization and a configurable sliding-window attention mechanism. By encoding the window size directly into the exported model binary, the runtime allocates a fixed circular buffer that never grows beyond the specified token limit—commonly set to 256 (2⁸) tokens for balancing context retention with memory efficiency.

## KV-Cache Header Configuration in export.py

The sliding-window constraint begins at model export time. In [`needle/model/export.py`](https://github.com/cactus-compute/needle/blob/main/needle/model/export.py), the binary header reserves a `kv_window` field that dictates how many past tokens the runtime retains in the KV-cache. The header structure defined at lines 20–30 serializes this value as an unsigned 32-bit integer:

```python

# export.py – header definition

# … line 20‑30 …

HEADER  (30 u32-sized fields, rope_theta an f32, everything else u32):
    tag, num_tensors, codebook_len, kv_window, kv_bits,
    vocab, d_model, num_heads, num_kv_heads, num_layers, head_dim, max_seq_len,
    ...

```

When the model loads, the `read_export()` function parses this header to extract the window size. At lines 1000–1002, the code unpacks the raw bytes and assigns `kv_window` from the header tuple:

```python

# export.py – read_export()

hdr = struct.unpack_from(_HDR_FMT, raw, 0)
tag, num_tensors, cb_n, kv_window, kv_bits = hdr[:5]

```

This value persists throughout the model’s lifecycle, locking the memory footprint at initialization.

## Effective Window Size Calculation

Before export, the effective window size is computed in [`needle/model/architecture.py`](https://github.com/cactus-compute/needle/blob/main/needle/model/architecture.py) via the `effective_kv_window(config)` helper. This function evaluates the model’s `sliding_window` attribute—if explicitly set to 256 (2⁸), it returns that value; otherwise, it defaults to 64 tokens (2⁶).

The power-of-two constraint enables efficient bitwise operations during indexing while keeping the KV-cache compact. Developers can configure larger windows by adjusting the architecture configuration before exporting to `.cact` format.

## Circular Buffer Implementation in run.py

Inside the inference engine ([`needle/model/run.py`](https://github.com/cactus-compute/needle/blob/main/needle/model/run.py)), the KV-cache is allocated once as a contiguous buffer sized exactly to `kv_window` slots. As the model generates new tokens, the cache index advances via modulo arithmetic:

```python
idx = position % kv_window

```

This ring-buffer approach automatically overwrites the oldest key/value pairs once the 256-token limit is reached. Consequently, the memory cost remains fixed at **O(kv_window × num_layers × head_dim)** even when processing arbitrarily long sequences.

## Practical Configuration Examples

To inspect the sliding-window behavior of an exported model:

```python

# Inspect KV-window size from an exported model

from needle.model.export import read_export

meta, tensors = read_export("model.cact")
print(f"KV-window size: {meta['kv_window']} tokens")  # Output: 256

```

To configure a 256-token window before export:

```python

# Force 256-token (2^8) sliding window

from needle.model.architecture import TransformerConfig, effective_kv_window

cfg = TransformerConfig.from_dict({"sliding_window": 256})
kv_window = effective_kv_window(cfg)  # Returns 256

```

The underlying C++ single-mega-kernel engine implements the update logic as follows:

```python

# Simulated ring-buffer update mechanism

def update_kv_cache(cache, new_key, new_value, pos, kv_window):
    idx = pos % kv_window           # Circular index calculation

    cache[:, idx, :] = new_key      # Overwrite stale key

    cache[:, idx, :] = new_value    # Overwrite stale value

    return pos + 1                  # Advance position

```

## Summary

- **Binary Header Encoding**: The `kv_window` field in [`needle/model/export.py`](https://github.com/cactus-compute/needle/blob/main/needle/model/export.py) (lines 20–30) persists the window size into the `.cact` checkpoint format, read at lines 1000–1002 during model loading.
- **Fixed Allocation**: [`needle/model/run.py`](https://github.com/cactus-compute/needle/blob/main/needle/model/run.py) allocates the KV-cache exactly once based on this header value, eliminating dynamic memory growth.
- **Constant Complexity**: Memory usage scales as O(kv_window × num_layers × head_dim), independent of generation length.
- **Configurable Trade-off**: While defaulting to 64 tokens (2⁶), the system supports 256-token (2⁸) or larger windows via the `sliding_window` configuration parameter in [`needle/model/architecture.py`](https://github.com/cactus-compute/needle/blob/main/needle/model/architecture.py).

## Frequently Asked Questions

### What is the default KV-window size in Needle 2?

The default configuration uses a 64-token (2⁶) window to minimize memory footprint for edge deployments. However, the architecture supports arbitrary power-of-two sizes including 128, 256, and 512 tokens, configured via the `sliding_window` attribute in [`needle/model/architecture.py`](https://github.com/cactus-compute/needle/blob/main/needle/model/architecture.py) before model export.

### How does the sliding window prevent out-of-memory errors during long generations?

By implementing a circular buffer in [`needle/model/run.py`](https://github.com/cactus-compute/needle/blob/main/needle/model/run.py) that overwrites entries modulo `kv_window`, the system maintains constant memory usage regardless of sequence length. Once the 256-token limit is reached, each new token evicts the oldest cached keys and values rather than appending to an ever-growing tensor.

### Can I change the KV-window size without re-exporting the model?

No. The `kv_window` value is baked into the binary header at export time (defined in [`needle/model/export.py`](https://github.com/cactus-compute/needle/blob/main/needle/model/export.py)). To use a different window size, you must re-export the checkpoint from a configuration with the desired `sliding_window` value, as the runtime allocates the exact buffer size specified in the header during `read_export()`.

### Why does Needle 2 use power-of-two window sizes?

Power-of-two sizes (64, 256, 512, etc.) enable efficient bitwise modulo operations and alignment-friendly memory layouts in the C++ inference kernel. This constraint, enforced through `effective_kv_window()` in [`needle/model/architecture.py`](https://github.com/cactus-compute/needle/blob/main/needle/model/architecture.py), simplifies the ring-buffer indexing logic while maintaining cache-friendly access patterns across layers.