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Secure AI virtual data roomsgranite-3.1-2b-instruct-quantized.w4a16
granite-3.1-2b-instruct-quantized.w4a16 is a 2.7B parameter language model optimized for inference via INT4 weight quantization. It reduces memory footprint by ~75% compared to the base model while maintaining 99.3% accuracy recovery on OpenLLM benchmarks. Designed for vLLM serving on resource-constrained hardware; gated=false and Apache 2.0 licensed.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | RedHatAI |
| Parameters | 2.7B |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 52.6k |
| Likes | 0 |
| Last updated | 2025-02-28 |
| Source | RedHatAI/granite-3.1-2b-instruct-quantized.w4a16 |
What granite-3.1-2b-instruct-quantized.w4a16 is
INT4-quantized variant of IBM Granite 3.1 2B instruction-tuned model, created by Neural Magic using llm-compressor (GPTQ method, group_size=64). Weights quantized to 4-bit, activations remain 16-bit (w4a16 scheme). Trained on 1024 calibration samples from neuralmagic/LLM_compression_calibration dataset. Evaluated against OpenLLM V1/V2 leaderboards and HumanEval; supports vLLM ≥0.5.2 with OpenAI-compatible serving. Context length not specified; benchmarks suggest max_model_len=4096 in deployment examples.
Run granite-3.1-2b-instruct-quantized.w4a16 locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="RedHatAI/granite-3.1-2b-instruct-quantized.w4a16")out = pipe("Explain retrieval-augmented generation in one sentence.", max_new_tokens=128)print(out[0]["generated_text"])Swap in vLLM or Ollama for production-grade serving. DEV.co can stand up the inference stack.
How you'd run it
A typical self-hosted path — open weights, an inference server, your application.
DEV.co builds each layer — from GPU infrastructure to the application.
Best use cases
Running & fine-tuning it
ESTIMATE (unverified): ~1.3 GB GPU VRAM (INT4 weights, 2.67B params ≈ 10.7 GB full precision / 8 ≈ 1.3 GB). Inference on single GPU feasible (A100 40GB, RTX 4090, L40S, or A10G). Tensor parallelism examples suggest tp_size=1 baseline. CPU inference via llama.cpp or similar possible but not documented.
Not documented. Quantized INT4 weights complicate standard backprop. LoRA/QLoRA adapters potentially compatible but require validation with llm-compressor integration. Full fine-tuning likely impractical without dequantization. Recommend testing on base model (ibm-granite/granite-3.1-2b-instruct) before adapting to quantized variant.
When to avoid it — and what to weigh
- Extended context or long-document reasoning required — Context length not specified in card; deployment examples use max_model_len=4096. Not suitable for long-context RAG or document summarization beyond ~4K tokens.
- High-accuracy math, coding, or knowledge-heavy tasks — OpenLLM V2 Math-Hard: 8.77%, GPQA: 28.56%, HumanEval pass@1: 52.30%. Not a primary choice for production code generation or expert knowledge retrieval.
- Multilingual or specialized domain requirements — Model tagged 'en' only; training corpus and domain specialization not disclosed. Not suitable for non-English or vertical-specific applications without validation.
- Fine-tuning with hardware-agnostic frameworks — Quantized weights may complicate gradient updates. LoRA/QLoRA feasibility not documented; requires testing with llm-compressor or specialized adapters.
License & commercial use
Apache License 2.0 (OSI-approved, permissive). Allows commercial use, modification, and distribution with attribution and liability disclaimer. Full text at opensource.org/licenses/Apache-2.0.
Apache 2.0 explicitly permits commercial use without royalty. Attribution required in distribution. Derivative models (fine-tuned versions) also under Apache 2.0 if redistributed with source. No additional licensing agreements stated. Recommend review of any downstream model licensing if integrating into proprietary products.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Low |
| DEV.co fit | Strong |
| Assessment confidence | High |
No explicit security audit or adversarial robustness testing documented. Quantization may reduce model's ability to detect certain prompt injections or jailbreak attempts (reduced expressivity). Standard LLM considerations apply: prompt injection vectors, data leakage through training data memorization. Recommend sandboxing in untrusted environments and input validation. No known CVEs or reported exploits.
Alternatives to consider
ibm-granite/granite-3.1-2b-instruct (unquantized base)
Higher accuracy (OpenLLM V1 avg 61.98 vs 61.54 quantized), better for fine-tuning or accuracy-critical tasks. ~10.7 GB VRAM vs ~1.3 GB for quantized variant.
microsoft/phi-3-mini (3.8B, quantized)
Similar size class, Microsoft-backed, alternative quantization pipelines. Stronger on coding/math (if benchmark data available). Requires cross-comparison.
TinyLlama/TinyLlama-1.1B-Chat-v1.0 (1.1B)
Ultra-lightweight alternative for extreme memory constraints (<500 MB). Lower accuracy; suitable for highly latency-sensitive edge deployments.
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granite-3.1-2b-instruct-quantized.w4a16 FAQ
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