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Model Card for Gemma-3-1B-IT (Aria Quant Bundle, q8)

Name: gemma-3-1b-it_q8
Availability: InStock

Model Details

Model Description

Gemma-3-1B-IT is a ~1.1-billion-parameter instruction-tuned, dense Transformer decoder-only language model developed by Google, part of the Gemma 3 family. It features GeGLU activation, Grouped Query Attention (GQA), and 32K native context length. Pre-trained on diverse web-scale corpora and aligned via instruction tuning + RLHF. This distribution is provided by Aria Compute as an aria-quant-bundle — a quantized package using Hadamard pre-processing + per-channel quantization. Optimized for CPU-only, on-device inference on mobile phones, edge devices, and single-board computers via the Aria Engine runtime. No GPU or cloud connection is required.

Developed by: Google
Quantized and distributed by: Aria Compute
Model type: Dense Transformer decoder-only (language, instruction-tuned)
Language(s): English (primary), Chinese, and 30+ additional languages
License: Apache 2.0
Finetuned from model: google/gemma-3-1b-it

Model Sources

Original Repository: google/gemma-3
Original Paper: Gemma 3 Technical Report
Aria Compute Dashboard: ariacompute.com/dashboard/models
Aria Engine: ariacompute.com

Uses

Direct Use

This quantized bundle is intended for on-device, offline text-generation tasks on resource-constrained hardware, including:

On-device chat and conversational assistants
Real-time text completion and multi-line code generation
Instruction-following tasks for mobile and IoT applications
Lightweight text embeddings for on-device retrieval and classification
Short-to-medium form summarization of notifications, messages, and local content
Local document analysis up to 32K context (chunked)

Target Devices

Platform	Runtime Memory	Feasibility
High-end smartphone (8 GB)	~1.25 GB	✅ Recommended
Mid-range smartphone (4–6 GB)	~1.25 GB	✅
Budget phone (2–3 GB)	~1.25 GB	⚠️ Tight, functional
Raspberry Pi 5 / SBC (4–8 GB)	~1.25 GB	✅
IoT gateway (1–2 GB)	~1.25 GB	⚠️ Tight, functional for short contexts
Wearable (1 GB)	~1.25 GB	❌ Too large for practical use

Memory breakdown (q8, at 4K context): ~1.1 GB quantized model weights (mmap) + ~60 MB KV cache + ~45 MB runtime overhead + ~45 MB per-channel metadata overhead ≈ ~1.25 GB.

Note: KV cache is compact due to aggressive GQA (26 layers × 3 KV heads × head_dim=128), ~40% of an equivalent full-attention model's cache.

Out-of-Scope Use

Long-form creative writing (>4K tokens per generation)
Mathematical theorem proving or formal verification
Full program/application synthesis
Multimodal input (this model is text-only)
Real-time audio/speech processing (use Aria speech models)
Safety-critical decision systems without human oversight

How to Get Started with the Model

Download from Aria Compute

Authenticated dashboard users can download the bundle via: https://ariacompute.com/dashboard/models

Quantization Recipe

This bundle uses a per-channel quantization recipe, one of several precision options in the Aria Compute lineup:

Component	Quantization Strategy	Details
Attention Q/K/V/O weights	8-bit	Per-channel codebooks, Hadamard pre-processing
FFN gate/up/down weights	8-bit	Per-channel codebooks, Hadamard pre-processing
RMSNorm weights	FP16	Preserved at full precision
Embedding table	FP16	Preserved at full precision

Bundle size: ~1.1 GB (BF16 original: ~2.2 GB)
Generation quality: Awaiting gen_quant_eval audit. Per-channel quantization preserves per-output-channel distribution characteristics. Formal quality benchmarks against FP16 and other Aria quant recipes are pending
Calibration-free: Hadamard pre-processing + per-channel quantization, no task-specific calibration data required
Other precision options: Also available for Gemma-3-1B-IT: gemma-3-1b-it_q326_channel and gemma-3-1b-it_q4_channel (per-channel 4-bit)

Model Architecture

Gemma-3-1B-IT employs a dense Transformer decoder architecture with GeGLU activation and Grouped Query Attention:

Parameter	Gemma 2 2B	Gemma 3 1B
Layers	26	26
Hidden size	2,304	1,152
FFN intermediate size	9,216	4,608
Attention heads (Query)	8	9
Attention heads (KV)	4 (GQA, group 2)	3 (GQA, group 3)
Head dimension	288	128
Activation	GeGLU	GeGLU
Position encoding	RoPE (θ = 10,000)	RoPE (θ = 10,000)
Normalization	RMSNorm (pre-norm)	RMSNorm (pre-norm)
Vocabulary size	256,128	256,128
Max context length	8,192	32,768

Design highlights (shared with Gemma family):

GQA (Grouped Query Attention): 3 KV heads serving 9 query heads (group 3) — KV Cache memory is 3× smaller than full attention
GeGLU activation: GELU with tanh approximation gating, efficient for on-device inference
RoPE position encoding: Standard 10K base frequency supporting 32K context length
RMSNorm pre-normalization: Lightweight normalization before each sub-layer

Key architecture differences from Gemma 2 2B:

Half hidden width (1,152 vs 2,304) and half FFN width (4,608 vs 9,216) — the primary source of the ~1B parameter reduction from the 2B class
4× context length (32K vs 8K) — improved long-context capability despite smaller model size
Smaller head_dim (128 vs 288) — lighter per-head computation with more query heads (9 vs 8)
More aggressive GQA (group 3 vs group 2) — further KV cache compression to compensate for longer context
Instruction-tuned variant — this bundle is based on the IT (instruction-tuned) checkpoint, not the base pre-trained model, providing better instruction-following capability out of the box

Bias, Risks, and Limitations

Limitations

Reasoning depth: Multi-step logical reasoning (≥3 steps) is limited for a 1B-class model. Verify outputs in high-stakes scenarios.
Mathematics: Simple arithmetic and GSM8K-level problems are partially supported; advanced quantitative reasoning is unreliable. Use larger models for mathematical precision.
Code generation: Capable of single-function completions and basic snippets; unreliable for multi-file program synthesis or complex algorithms.
Factual knowledge: Limited world knowledge due to ~1B parameter scale. Always verify factual claims against authoritative sources.
Instruction following: Handles simple single-constraint instructions reliably. Complex multi-constraint prompts may cause degradation at longer contexts.
Quantization drift: Per-channel quantization may exhibit generation drift versus FP16, especially on ambiguous or open-ended prompts. For smaller bundles, see gemma-3-1b-it_q326_channel and gemma-3-1b-it_q4_channel.

Bias and Risks

Bias: As with all large language models trained on web-scale data, Gemma 3 may reflect societal biases present in its training corpus. Evaluate outputs before deployment in sensitive domains (hiring, healthcare, law).
Toxicity: The instruction-tuned model has been safety-aligned with RLHF. However, no safety filter is exhaustive. Consider an additional output classifier in high-risk environments.
Hallucination: May generate plausible-sounding but factually incorrect information. Implement output verification for critical applications.
Dual-use risk: Text-generation capabilities could be misused for spam, disinformation, or impersonation. Deploy responsibly and in accordance with the Apache 2.0 license terms.

Recommendations

Users (both direct and downstream) should be made aware of the above risks, biases, limitations, and constraints of the model. We recommend:

Adding a lightweight output safety classifier for user-facing deployments
Verifying factual claims with external knowledge bases
Not using the model for high-stakes decisions without human review

ariacompute/gemma-3-1b-it_q8