# Needle KV Budget Calculation and Effective Window Size: Managing the 11 MiB Cache Limit

> Calculate your Needle KV budget and effective window size. Learn how the 11 MiB cache limit impacts performance and sequence length in your transformer models.

- Repository: [Cactus Compute, Inc./needle](https://github.com/cactus-compute/needle)
- Tags: deep-dive
- Published: 2026-08-15

---

**The effective KV window size in Needle is derived by dividing an 11 MiB KV budget by the per-token memory cost across all transformer layers and engram sites, clamping the result between a hardware minimum and the model's maximum sequence length, with optional user override via `config.kv_window`.**

Needle, an open-source transformer inference engine from Cactus Compute, implements a strict memory budgeting system for its key-value cache to enable efficient on-device inference. Understanding the **Needle KV budget calculation effective window size** is essential for optimizing model exports and preventing out-of-memory errors during inference. The architecture uses two helper functions in [`needle/model/architecture.py`](https://github.com/cactus-compute/needle/blob/main/needle/model/architecture.py) to determine exactly how many tokens can remain cached given the fixed memory constraint.

## Understanding the KV Budget Constants

The KV cache calculation relies on several architectural constants defined in the codebase. **KV_BUDGET_BYTES** is set to approximately 11 MiB, representing the total memory pool shared across all layers, attention heads, and cache positions. This budget must accommodate both standard KV projections and specialized **engram layers** that store additional activation states.

The calculation also respects **KV_GROUP**, a grouping constant used for memory alignment, and **KV_WINDOW_MIN**, a hardware-dependent minimum cache size. These values ensure that the calculated window remains practical for the target deployment hardware while maximizing the available context length.

## Calculating the Budget-Derived Window with `kv_budget_window`

The `kv_budget_window(config)` function in [`needle/model/architecture.py`](https://github.com/cactus-compute/needle/blob/main/needle/model/architecture.py) (lines 603-612) computes the absolute upper bound on sequence length that fits within the memory budget. It accounts for the unique storage requirements of each layer, including grouped-query attention heads and engram storage sites.

```python
def kv_budget_window(config):
    head_dim = (getattr(config, "attn_dim", 0) or config.d_model) // config.num_heads
    kv = config.num_kv_heads * head_dim
    d, L = config.d_model, config.num_layers
    sites = len(tuple(getattr(config, "engram_layers", (2, 15))))
    per_pos = (L * (2 * kv + 2 * (kv // KV_GROUP) * 4)
               + sites * (d + (d // KV_GROUP) * 4))
    window = (KV_BUDGET_BYTES // per_pos) // KV_GROUP * KV_GROUP
    return max(KV_WINDOW_MIN, min(window, config.max_seq_len))

```

The function performs four critical calculations:

1. **Head dimension derivation** – Calculates `head_dim` from either `attn_dim` or `d_model` divided by `num_heads`, determining the vector size per attention head.

2. **KV size computation** – Multiplies `num_kv_heads` by `head_dim` to get the total KV vector size, which varies for grouped-query attention architectures.

3. **Per-position cost calculation** – Computes `per_pos` by summing the storage cost across all `num_layers` (the `L` term) and adding the overhead from engram layers (the `sites` term), with specific arithmetic accounting for KV grouping and quantization overhead.

4. **Window alignment and clamping** – Divides the total budget by the per-position cost, aligns the result to `KV_GROUP` boundaries, and clamps between `KV_WINDOW_MIN` and `config.max_seq_len` to ensure hardware compatibility.

## Respecting User Overrides with `effective_kv_window`

While `kv_budget_window` determines the theoretical maximum, `effective_kv_window(config)` (lines 614-617) returns the actual cache size that will be used during inference. This function respects user configuration while preventing memory overcommitment.

```python
def effective_kv_window(config):
    budget = kv_budget_window(config)
    return min(budget, config.kv_window) if config.kv_window else budget

```

The logic is straightforward but critical for deployment flexibility. If `config.kv_window` is explicitly set, the function returns the smaller of the budget-derived value and the user request. If no override exists, it returns the full budgeted window, ensuring the model utilizes all available KV cache memory.

## Integration Points in the Needle Pipeline

The effective KV window calculation propagates through multiple stages of the Needle workflow:

- **Model Export** – In [`needle/model/export.py`](https://github.com/cactus-compute/needle/blob/main/needle/model/export.py) (lines 558-560), `effective_kv_window(config)` determines the cache size embedded in the exported `.cact` file, ensuring the runtime allocator reserves the correct memory footprint.

- **Finetuning** – The finetuning script in [`needle/model/finetune.py`](https://github.com/cactus-compute/needle/blob/main/needle/model/finetune.py) (lines 407-430) calls the function when building checkpoints, aligning the training cache size with inference constraints to prevent deployment mismatches.

- **Validation** – The test suite in [`tests/test_build.py`](https://github.com/cactus-compute/needle/blob/main/tests/test_build.py) (lines 43-53) verifies that exported models report the expected KV window, validating that the budget calculations translate correctly to binary artifacts.

## Practical Code Examples

### Querying the Effective KV Window

To determine the cache capacity for a specific model configuration:

```python
from needle.model.architecture import TransformerConfig, effective_kv_window
import pickle
import pathlib

# Load a checkpoint to extract configuration

ckpt_path = pathlib.Path("tests/fixtures/tiny_checkpoint.pkl")
with ckpt_path.open("rb") as f:
    ckpt = pickle.load(f)

config = TransformerConfig(**ckpt["config"])

# Calculate the effective window size

kv_window = effective_kv_window(config)
print(f"KV cache can hold {kv_window} tokens within the 11 MiB budget")

```

### Overriding the Window During Export

To force a smaller context window while respecting the hardware budget:

```python
from needle.model.export import write_export
from needle.model.tokenizer import get_tokenizer
from needle.model.architecture import effective_kv_window

# Assume params and config are loaded from a checkpoint

tokenizer = get_tokenizer(config.vocab_size)

# Cap at 256 tokens or the budget limit, whichever is smaller

custom_window = min(256, effective_kv_window(config))

write_export(
    params,
    config,
    out_path="optimized_model.cact",
    bits=4,
    tokenizer=tokenizer,
    kv_window=custom_window,
)

```

## Summary

- **KV_BUDGET_BYTES** (~11 MiB) is the fixed memory pool shared across all layers and positions in Needle's transformer architecture.
- **`kv_budget_window`** calculates the theoretical maximum tokens by dividing the budget by per-position costs, including standard attention KV pairs and engram layer storage.
- **`effective_kv_window`** applies user overrides via `config.kv_window` while preventing allocation beyond the hardware budget.
- The calculation accounts for grouped-query attention through **KV_GROUP** alignment and respects minimum thresholds via **KV_WINDOW_MIN**.
- Both functions reside in [`needle/model/architecture.py`](https://github.com/cactus-compute/needle/blob/main/needle/model/architecture.py) and are consumed by export, finetuning, and testing pipelines to ensure consistent memory planning.

## Frequently Asked Questions

### What happens if I set `config.kv_window` larger than the budget allows?

Needle's `effective_kv_window` function will return the smaller of the two values, effectively clamping your request to the budget-derived maximum. According to the implementation in [`needle/model/architecture.py`](https://github.com/cactus-compute/needle/blob/main/needle/model/architecture.py) (lines 614-617), the explicit `min(budget, config.kv_window)` check prevents memory overcommitment that would cause runtime allocation failures.

### How do engram layers affect the KV budget calculation?

Engram layers increase the per-token memory cost significantly. In `kv_budget_window`, the `sites` variable counts the configured engram layers (defaulting to indices 2 and 15), and the formula adds `sites * (d + (d // KV_GROUP) * 4)` to the per-position cost. This means models with more engram sites will have proportionally smaller effective context windows to keep the total cache within the 11 MiB budget.

### Can I modify KV_BUDGET_BYTES for my specific hardware?

The constant is hardcoded in [`needle/model/architecture.py`](https://github.com/cactus-compute/needle/blob/main/needle/model/architecture.py) based on target device constraints. While you could fork the repository and adjust the value, doing so requires corresponding changes to the runtime memory allocator in the inference engine. For most use cases, use the `kv_window` configuration parameter to reduce the cache size rather than increasing the budget, as exceeding physical limits will cause out-of-memory errors during model loading.

### Why does the window calculation use integer division with KV_GROUP?

The alignment to **KV_GROUP** boundaries (achieved via `(KV_BUDGET_BYTES // per_pos) // KV_GROUP * KV_GROUP`) ensures that the KV cache memory layout matches the grouped-query attention implementation. This optimization reduces memory fragmentation and improves cache locality during attention computation, particularly important for quantized models where grouped heads share key-value projections.