Andrii Bidochko

• Updated: July 23, 2026
• 6 min read

Signed Symmetric Quantization for Few-Bit Integers

Direct Answer

Signed Symmetric Quantization (SSQ) introduces a lightweight, few‑bit integer format that places the extra negative value of a signed alphabet on the dominant outlier tail, eliminating the clipping that plagues standard symmetric quantizers. By keeping the zero point at zero while intelligently flipping the scale sign, SSQ delivers up to 9 % memory savings and more than 2× higher inference throughput without sacrificing model accuracy.

Background: Why This Problem Is Hard

Large language models (LLMs) such as Llama 3, Qwen 3, and their successors demand gigabytes of memory and massive compute bandwidth during inference. Quantization—compressing floating‑point weights into low‑bit integers—has become the de‑facto strategy for fitting these models onto commodity CPUs and edge accelerators. However, the quantization pipeline is riddled with trade‑offs:

• Symmetric quantization fixes the scale to a positive value and forces the zero point to zero. This simplicity yields fast integer arithmetic but leaves one extra negative code unused, causing the most extreme positive outliers to be clipped.
• Asymmetric quantization adds a learnable zero point, shifting the integer grid to better match the data distribution. While this reduces clipping, it introduces per‑tensor offset handling that slows down kernels and inflates memory footprints.
• At few‑bit precision (4‑bit, 5‑bit), the quantization error introduced by clipping becomes non‑trivial, directly degrading perplexity and downstream task performance.

Existing solutions either accept the accuracy loss of symmetric quantization or pay a runtime penalty for asymmetric formats. For production‑grade LLM services—where latency, cost, and hardware utilization are tightly coupled—this dilemma is a critical bottleneck.

What the Researchers Propose

The authors present Signed Symmetric Quantization (SSQ), a third quantization paradigm that preserves the fast, zero‑point‑free execution profile of symmetric quantization while eliminating its dominant source of error. SSQ operates on the same signed integer alphabet (e.g., –8 … 7 for 4‑bit) but applies a simple sign‑selection rule:

• Compute the absolute maximum (absmax) of a weight group.
• If the distribution’s negative tail contains the larger magnitude outlier, keep the scale positive (standard symmetric).
• If the positive tail dominates, flip the scale sign, effectively moving the extra negative code to the positive side where it can capture the outlier.

This “signed absmax grid” retains a zero point of zero, meaning integer arithmetic remains unchanged, yet the extra representable value is always allocated to the side of the distribution that needs it most.

How It Works in Practice

Implementing SSQ in an LLM inference stack follows a straightforward workflow:

1. Weight Grouping: Model weights are partitioned into groups (e.g., per‑channel or per‑block) to capture local distribution characteristics.
2. Absmax Computation: For each group, the absolute maximum magnitude is identified.
3. Sign Decision: A lightweight rule checks whether the negative or positive side holds the larger outlier. If the positive side is larger, the scale is negated.
4. Quantization: Weights are scaled by the (potentially negated) absmax and rounded to the nearest signed integer within the fixed alphabet.
5. Runtime Execution: Because the zero point stays at zero, existing symmetric integer kernels can be reused without modification, preserving cache‑friendly memory layouts and SIMD‑friendly instruction streams.

The key differentiator is the sign selection rule, which costs only a few extra comparisons per weight group—negligible compared to the overall quantization overhead. No additional metadata (such as per‑tensor zero points) needs to be stored, keeping the memory footprint identical to standard symmetric formats.

Evaluation & Results

The authors benchmarked SSQ on three state‑of‑the‑art LLM families: Qwen 3, Qwen 3.5, and Llama 3. Experiments covered both perplexity on standard language modeling datasets and few‑shot accuracy on downstream tasks (e.g., MMLU, GSM‑8K). The evaluation pipeline included:

• Quantizing each model to 4‑bit and 5‑bit signed integers using three schemes: standard unsigned symmetric, asymmetric, and SSQ.
• Measuring memory consumption, inference throughput (tokens per second) on an AMD EPYC “Turin” CPU, and end‑to‑end latency for a 2048‑token prompt.
• Comparing the quantized models against the full‑precision baseline to isolate the impact of quantization error.

Key findings:

• Memory Savings: SSQ matches the unsigned symmetric format, using up to 9 % less memory than the asymmetric counterpart.
• Throughput Boost: On the same hardware, SSQ achieved up to 2.45× higher token‑per‑second rates compared to asymmetric quantization, thanks to the zero‑point‑free kernel path.
• Accuracy Gains: Across 88‑99 % of weight groups, the signed absmax grid satisfied a conditional bound‑optimality criterion, translating into lower perplexity (average reduction of 0.12) and modest but consistent improvements in few‑shot task scores (≈0.5‑1 % absolute gain).
• Scalability: The sign‑selection rule held for all tested models, indicating that SSQ scales from 7‑B to 70‑B parameter regimes without additional tuning.

These results demonstrate that SSQ delivers the best of both worlds: the speed and memory efficiency of symmetric quantization, plus the accuracy robustness traditionally associated with asymmetric formats.

Why This Matters for AI Systems and Agents

For engineers building AI agents, chatbots, or autonomous decision‑making pipelines, inference latency and cost are often the decisive factors in production rollout. SSQ directly addresses these constraints:

• Lower Infrastructure Costs: By reducing memory pressure, more model instances can be packed onto a single CPU or GPU, decreasing the number of required nodes.
• Higher Throughput for Real‑Time Agents: Faster token generation enables smoother conversational experiences, especially in multi‑user settings where dozens of sessions share a single inference server.
• Preserved Model Fidelity: The modest accuracy uplift means agents retain nuanced reasoning capabilities, crucial for tasks like code generation, legal drafting, or medical triage.
• Seamless Integration: Because SSQ reuses existing symmetric kernels, developers can adopt it without rewriting low‑level inference libraries, accelerating time‑to‑market.

Practically, teams can embed SSQ into their deployment pipelines and immediately benefit from the UBOS platform overview, which offers plug‑and‑play quantization modules. Moreover, the Workflow automation studio lets data scientists orchestrate quantization, validation, and rollout as a single reproducible workflow, reducing human error and ensuring compliance with internal model governance.

What Comes Next

While SSQ closes a major performance gap, several avenues remain open for exploration:

• Mixed‑Precision Strategies: Combining SSQ for weight tensors with higher‑precision activations could further tighten the accuracy‑throughput trade‑off.
• Hardware‑Specific Optimizations: Tailoring the sign‑selection rule to exploit SIMD lane patterns on emerging AI accelerators may unlock additional speedups.
• Dynamic Quantization: Extending SSQ to runtime‑adaptive scaling could allow agents to adjust precision on‑the‑fly based on input difficulty.
• Broader Model Families: Testing SSQ on vision‑language models, diffusion pipelines, and reinforcement‑learning agents will validate its universality.

Organizations interested in experimenting with SSQ can start by leveraging the Enterprise AI platform by UBOS, which provides out‑of‑the‑box support for custom quantization schemes. For startups seeking rapid prototyping, the UBOS for startups program offers sandbox environments and expert consulting to integrate SSQ into production stacks. Finally, developers building conversational bots can enrich their pipelines with the ChatGPT and Telegram integration, enabling low‑latency, cost‑effective deployments powered by SSQ‑quantized models.

References

• Signed Symmetric Quantization for Few-Bit Integers (arXiv)
• Colbert, I., Dash, E., Monteagudo‑Lago, P., et al. (2026). Signed Symmetric Quantization for Few‑Bit Integers. arXiv:2607.08779v1.

Andrii Bidochko

CTO UBOS

Andrii Bidochko is an AI entrepreneur and researcher focused on AI agents, reinforcement learning, and autonomous systems. He writes about the technologies shaping the future of machine intelligence, from frontier models and agent architectures to real-world AI applications.