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Model Detail

unsloth logo

granite-4.1-8b-GGUF

▲ 6.5%
Provider: unslothCategory: otherParameters: 8B
DB Score
3.8
Downloads
18K
Likes
31
Day
+6.5%
Week
+182.8%
Month
+0.0%
Overview

granite-4.1-8b-GGUF is an AI model with 8B parameters released by unsloth. And supports text->text inputs, distributed under the permissive apache-2.0 license.

Pricing & Throughput

granite-4.1-8b-GGUF is priced at $0.05/M input tokens and $0.1/M output tokens. Operationally the model offers a 131K-token context window, which matters when sizing it for prompt-heavy or latency-sensitive workloads. At this input rate the model sits in the commodity tier and is suitable for high-volume workloads where per-call cost dominates the decision.

Technical

granite-4.1-8b-GGUF ships with 8B parameters, distributed as a quantized weight variant for lower-VRAM inference. The apache-2.0 license is permissive, allowing commercial deployment and derivative work without per-seat fees, though attribution requirements still apply.

Trending Signal

Downloads of granite-4.1-8b-GGUF have moved +6.5% over the past 24 hours, +182.8% over the trailing seven days. That puts the model in active uptrend territory; a sustained move of this size usually reflects a recent release, a viral integration, or a benchmark surprise rather than steady-state demand. These numbers are signal, not guarantee — week-over-week download counts on Hugging Face also reflect mirror traffic, CI scrapes, and one-off benchmarking runs.

Read about databubble_score →
Use Cases

granite-4.1-8b-GGUF is best fit for general-purpose AI workloads, and high-volume batch jobs where per-call cost dominates the budget. Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.

Download History
Pricing
Input ($/M tokens)
$0.05
Output ($/M tokens)
$0.1
Context Window
131K
Research Paper
arXiv: 0000.00000→
Model Info
Licenseapache-2.0
Modalitytext->text
Recent newsView all news →
Related News
arxiv9d ago

Granite.Trust Policy Tools: Shareable, Actionable Policies for Generative AI Applications

arXiv:2608.23870v1 Announce Type: new Abstract: When it comes to safety policies for generative AI, one size does not fit all. Each organization and use case needs to mitigate different risks depending on the application context, regulatory environment, organizational values, and user personas. Yet,

huggingface11d ago

Granite 4.2 LLMs: How They're Built

arxiv95d ago

GRANITE : a Byzantine-Resilient Dynamic Gossip Learning Framework

arXiv:2504.17471v2 Announce Type: replace-cross Abstract: Gossip Learning (GL) is a decentralized learning paradigm where users iteratively exchange and aggregate models with a small set of neighboring peers. Recent approaches rely on dynamic communication graphs built using Random Peer Sampling (RP

huggingfaceneutral114d ago

Granite Embedding Multilingual R2: Open Apache 2.0 Multilingual Embeddings with 32K Context — Best Sub-100M Retrieval Quality

huggingfaceneutral129d ago

Granite 4.1 LLMs: How They’re Built

huggingface158d ago

Granite 4.0 3B Vision: Compact Multimodal Intelligence for Enterprise Documents

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