DFQS v1.0: DeepSeek Flash Quantization Standard | IEEE-Style Specification Paper

Abstract

DFQS (DeepSeek Flash Quantization Standard) defines a structured deployment and evaluation framework for ultra-low-bit Mixture-of-Experts (MoE) language models operating under constrained-memory inference environments. This paper introduces DFQS-IQ1_S-XL, a reference implementation based on DeepSeek-V4-Flash, quantized and deployed in a single-file GGUF format (61.6GB). The system targets CPU-feasible inference while maintaining deterministic routing behavior and long-context operational stability. DFQS formalizes deployment constraints, behavioral expectations, and evaluation interfaces for reproducible large-scale MoE inference under compression regimes beyond conventional quantization standards.

1. Introduction

Large-scale Mixture-of-Experts (MoE) models typically require GPU-intensive inference and high memory bandwidth. Existing quantization methods (Q4–Q8, FP8, and hybrid compression techniques) fail to consistently define stable deployment behavior under extreme compression.

DFQS introduces a standardized framework for:

ultra-low-bit MoE deployment
deterministic inference behavior under compression
CPU-feasible execution of large-scale models
reproducible evaluation across constrained systems

2. DFQS Design Objectives

DFQS is designed around four core objectives:

Deterministic Execution: Ensure consistent inference behavior under constrained runtime conditions.
Compression Stability: Maintain routing coherence in ultra-low-bit MoE configurations.
CPU Feasibility: Enable inference without dependency on high-end GPU infrastructure.
Reproducible Evaluation: Standardize benchmarking across models and hardware environments.

3. DFQS-IQ1_S-XL Reference Implementation

DFQS-IQ1_S-XL is a concrete implementation of the DFQS standard.

3.1 Model Specification

Property	Value
Architecture	Mixture-of-Experts (MoE)
Total Parameters	284B
Active Parameters	13B
Format	GGUF (single-file)
Size	61.6GB
Runtime	llama.cpp

3.2 Deployment Class

IQ1_S-XL defines an ultra-low-bit compression boundary optimized for constrained-memory execution, CPU-first inference environments, and long-context reasoning stability.

4. DFQS Positioning Model

DFQS defines a hierarchical compression spectrum:

                FP16 / FP8 (Frontier Models)

                → Q4–Q6 (Production GGUF Systems)

                → IQ2 (Experimental Compression Layer)

                → DFQS-IQ1_S-XL (Reference Deployment Class)

This positioning defines DFQS as a boundary layer between production quantization and experimental compression regimes.

5. Behavioral Specification

DFQS systems are defined by expected runtime behavior under compression constraints.

5.1 Stable Properties

Property	Behavior
Routing Consistency	Stable under constrained inference
Deterministic Execution	Preserved across runtimes
CPU Feasibility	Supported
Expert Activation	Coherent under compression

5.2 Failure Modes

- Context saturation → increased output variance
- Token pressure → degraded routing precision
- Memory saturation → latency spikes or truncation behavior

6. Evaluation Framework

DFQS defines a standardized evaluation interface for all compliant implementations.

6.1 Required Metrics

reasoning_score: float
code_score: float
context_stability_curve: list[float]
cpu_tokens_per_sec: float
failure_boundary_tokens: int

6.2 Evaluation Conditions

All evaluations MUST be performed under CPU-only baseline (unless specified), llama.cpp runtime, standardized prompt sets, reproducible inference conditions.

6.3 Evaluation Categories

Reasoning (short + medium context), Code generation (llama.cpp prompts), Long-context stability (8K–64K degradation curve), CPU inference throughput benchmarks.

7. DFQS Packaging Procedure (Non-Normative)

The IQ1_S-XL artifact uses a sequential merge process: shard ingestion, binary concatenation, GGUF header validation, post-validation cleanup. This procedure is implementation-specific and not part of DFQS compliance requirements.

8. Deployment Specifications

8.1 llama.cpp

llama-server -hf persadian/DeepSeek-V4-Flash-IQ1_S-XL

8.2 Python

                from llama_cpp import Llama

                llm = Llama.from_pretrained(

                    repo_id="persadian/DeepSeek-V4-Flash-IQ1_S-XL",

                    filename="DeepSeek-V4-Flash-IQ1_S-XL.gguf",

                )

9. Hardware Envelope

Component	Minimum	Recommended
RAM	80GB	128GB
GPU VRAM	22GB	24GB+
Storage	60GB	150GB

Note: CPU inference remains the primary execution mode.

10. Validation Status

GGUF integrity: validated | Single-file structure: confirmed | llama.cpp compatibility: verified | CPU inference: operational

11. Environmental Efficiency Context

DFQS compression reduces computational overhead compared to standard MoE deployments. This results in lower memory footprint, reduced inference energy consumption, and improved deployment accessibility in constrained environments.

12. Conclusion

DFQS defines a structured deployment and evaluation standard for ultra-low-bit Mixture-of-Experts systems. DFQS-IQ1_S-XL serves as the reference implementation demonstrating feasibility of CPU-first, deterministic MoE inference under extreme compression constraints.

References

Persadh, D.R. (2026). DFQS-IQ1_S-XL: Ultra-Low-Bit MoE Deployment Standard. Hugging Face. DOI: 10.57967/hf/8853 https://doi.org/10.57967/hf/8853

DeepSeek-AI. (2025). DeepSeek-V4-Flash: Technical Report.

llama.cpp contributors. (2025). GGUF format specification & MoE kernel optimizations.

Manuscript received May 19, 2026; accepted for publication as IEEE-style specification.

DFQS v1.0: DeepSeek Flash Quantization Standard for Ultra-Low-Bit MoE Deployment