# Memory Requirements for Different Model Quantizations in DS4

> Discover DS4 model quantization memory requirements. Explore 2-bit to 16-bit formats, memory scaling, and estimate total consumption with ds4_context_memory_estimate().

- Repository: [Salvatore Sanfilippo/ds4](https://github.com/antirez/ds4)
- Tags: performance
- Published: 2026-08-08

---

**DS4 supports GGUF-style quantization formats ranging from 2-bit to 16-bit precision, with memory usage scaling linearly from approximately 0.25 KB to 2 KB per 1,024 parameters, and provides the `ds4_context_memory_estimate()` function to calculate total consumption including KV-cache overhead.**

The `antirez/ds4` repository implements efficient inference for large language models using GGUF-style quantization formats that trade numerical precision for reduced memory footprint. Understanding the memory requirements for different model quantizations is essential for deploying models on resource-constrained hardware or multi-GPU configurations. DS4 provides specific APIs and lookup tables in [`gguf-tools/quants.c`](https://github.com/antirez/ds4/blob/main/gguf-tools/quants.c) that map quantization types to their exact bit-per-weight ratios, enabling precise memory budgeting before model loading.

## Quantization Formats and Memory Footprint

DS4 implements several quantization strategies defined in [`gguf-tools/quants.c`](https://github.com/antirez/ds4/blob/main/gguf-tools/quants.c), each offering a different trade-off between model fidelity and memory consumption. The memory occupied by model weights scales directly with the bits-per-weight ratio.

| Quantization | Bits per weight | Approx. Bytes per 1K parameters |
|--------------|----------------|----------------------------------|
| **Q8_0** | 8 | ~1 KB |
| **Q4_K** | 4 | ~0.5 KB |
| **Q2_K** | 2 | ~0.25 KB |
| **IQ2_XXS** | ~2.5 | ~0.31 KB |
| **FP16** | 16 | ~2 KB |
| **BF16** | 16 | ~2 KB |
| **FP8 E4M3** | 8 | ~1 KB |

Values represent storage for 1,024 parameters. Actual inference memory includes additional overhead for the KV-cache, which stores intermediate activations for each token in the context window.

## Memory Calculation Formula

The total memory required for a DS4 model can be estimated using the formula implemented in [`ds4_context.c`](https://github.com/antirez/ds4/blob/main/ds4_context.c):

```

memory ≈ (model_parameters × bits_per_weight / 8) + KV-cache overhead

```

The **KV-cache overhead** depends on the context length, number of transformer layers, attention heads, and the selected quantization type. Notably, the KV-cache uses the same quantization format as the model weights, meaning a Q4_K model will store cached keys and values using 4-bit quantization. The `ds4_context_memory_estimate()` function aggregates these factors to return a total byte estimate, accounting for device-specific alignment and padding requirements.

## Programmatic Memory Estimation

DS4 exposes C APIs to calculate memory requirements before allocating GPU or CPU buffers. These functions are utilized throughout the test suite, particularly in [`tests/test_engine_mgpu_placement.c`](https://github.com/antirez/ds4/blob/main/tests/test_engine_mgpu_placement.c), to validate that requested contexts fit within available device memory.

### Estimating Context Memory with ds4_context_memory_estimate

The `ds4_context_memory_estimate()` function, defined in [`ds4_context.c`](https://github.com/antirez/ds4/blob/main/ds4_context.c), computes total memory consumption given a device type, desired context length, and quantization bit depth.

```c
#include "ds4.h"
#include "ds4_context.h"

int main(void) {
    /* 7-B model ≈ 7,000,000,000 parameters */
    const int64_t model_params = 7LL * 1000 * 1000 * 1000;

    /* Q4_K quantization = 4 bits per weight */
    const int quant_bits = 4;

    /* Desired context length in tokens */
    const int64_t ctx_len = 4096;

    /* Estimate memory for CUDA device */
    size_t mem_bytes = ds4_context_memory_estimate(DS4_DEVICE_CUDA,
                                                   ctx_len,
                                                   quant_bits);
    printf("Estimated memory: %.2f GiB\n", 
           mem_bytes / (1024.0 * 1024 * 1024));
    return 0;
}

```

### Calculating Per-Token Memory Overhead

For custom memory planners, DS4 provides bit-depth constants in [`gguf-tools/quants.c`](https://github.com/antirez/ds4/blob/main/gguf-tools/quants.c) that allow manual calculation of storage requirements per token block.

```c
#include <stdio.h>

static void print_mem_per_token(int bits) {
    double bytes_per_1k = (bits / 8.0) * 1024.0 / 1024.0; /* Convert to KB */
    printf("  %2d-bit → %.3f KB per 1K tokens\n", bits, bytes_per_1k);
}

int main(void) {
    printf("Per-token memory usage by quantization:\n");
    print_mem_per_token(8);   /* Q8_0 */
    print_mem_per_token(4);   /* Q4_K */
    print_mem_per_token(2);   /* Q2_K */
    print_mem_per_token(3);   /* IQ2_XXS approximated */
    return 0;
}

```

## Key Source Files

The quantization system and memory estimation logic are implemented across the following source files in `antirez/ds4`:

- **[`gguf-tools/quants.c`](https://github.com/antirez/ds4/blob/main/gguf-tools/quants.c)**: Implements low-level quantization routines (`ds4q_quantize_*`) and defines bit-per-weight constants for each format.
- **[`gguf-tools/deepseek4-quantize.c`](https://github.com/antirez/ds4/blob/main/gguf-tools/deepseek4-quantize.c)**: Provides the high-level quantization pipeline and validation checks via `ds4q_can_quantize()`.
- **[`ds4_context.c`](https://github.com/antirez/ds4/blob/main/ds4_context.c)**: Contains `ds4_context_memory_estimate()`, which aggregates model parameters, quantization bits, and KV-cache requirements.
- **[`tests/test_engine_mgpu_placement.c`](https://github.com/antirez/ds4/blob/main/tests/test_engine_mgpu_placement.c)**: Uses memory estimation functions to validate context placement across multiple GPUs.

## Summary

- DS4 supports **Q2_K through FP16** quantization formats, with memory usage ranging from ~0.25 KB to ~2 KB per 1,024 parameters.
- **Memory scales linearly** with bit depth: Q4_K uses 50% less memory than Q8_0, and 75% less than FP16.
- The **`ds4_context_memory_estimate()`** function in [`ds4_context.c`](https://github.com/antirez/ds4/blob/main/ds4_context.c) provides device-aware total memory calculation, including KV-cache overhead.
- **KV-cache quantization** matches the weight quantization type, affecting total memory proportionally with context length.
- Reference **[`gguf-tools/quants.c`](https://github.com/antirez/ds4/blob/main/gguf-tools/quants.c)** for the authoritative definitions of bit-per-weight ratios used in all calculations.

## Frequently Asked Questions

### What is the most memory-efficient quantization format available in DS4?

**Q2_K** provides the smallest footprint at approximately 0.25 KB per 1,024 parameters. For scenarios requiring slightly higher precision, **IQ2_XXS** offers a compromise at ~0.31 KB per 1,024 parameters by utilizing extra scaling factors to reduce quantization error while maintaining near-2-bit storage efficiency.

### How does DS4 calculate total GPU memory requirements for inference?

The repository uses the `ds4_context_memory_estimate()` function implemented in [`ds4_context.c`](https://github.com/antirez/ds4/blob/main/ds4_context.c). This function accepts the target device (`DS4_DEVICE_CUDA` or `DS4_DEVICE_CPU`), desired context length, and quantization bit depth, then returns the total bytes required for both model weights and the KV-cache.

### Does the KV-cache use the same quantization precision as the model weights?

Yes. According to the implementation in [`gguf-tools/quants.c`](https://github.com/antirez/ds4/blob/main/gguf-tools/quants.c) and the estimation logic in [`ds4_context.c`](https://github.com/antirez/ds4/blob/main/ds4_context.c), the KV-cache is stored using the identical quantization type selected for the model weights. Therefore, choosing Q4_K quantization reduces both model storage and context cache memory by 50% compared to Q8_0.

### What is the practical memory difference between Q8_0 and FP16 quantization?

**Q8_0** consumes exactly 50% less memory than **FP16**. Specifically, Q8_0 requires approximately 1 KB of storage per 1,024 parameters (8 bits per weight), while FP16 requires 2 KB per 1,024 parameters (16 bits per weight). This translates to a ~7 GB savings for a 7-billion parameter model when using Q8_0 instead of FP16.