Granite-3.0-8B-Instruct
IBM's flagship enterprise open-weight model with 128K context, trained under strict copyright curation and released with full Apache 2.0 licensing and IP indemnity.
The brutal truth
Built specifically for enterprise legal departments terrified of training data copyright lawsuits. Solid RAG performance with true Apache 2.0.
Files and Versions
Direct inspected tree artifacts from Hugging Face. Verified filenames, exact byte sizes, model weight shards, and configuration definitions.
Files and versions are hosted on Hugging Face. You can view all branches, commits, model weights, and tokenizer artifacts directly.
Inspect ibm-granite/granite-3.0-8b-instruct /tree/main ↗Inspect /tree/main Repository ↗Decision matrix
✓ RUN / USE THIS IF…
- •Your legal team demands Apache 2.0 and copyright provenance
- •You run enterprise RAG pipelines
- •You have standard 8GB GPUs
- •Your 24GB GPU comfortably fits the model weights (~5GB VRAM) with dedicated headroom for inference.
✕ WALK AWAY IF…
- •You want maximum consumer coding benchmarks (use Qwen2.5)
- •Your GPU has less than 5GB VRAM and you cannot tolerate slow CPU/system RAM offload paging.
- •You prefer a plug-and-play web app interface and don't want to manage local model checkpoints, drivers, or runtimes.
- •You need specialized frontier reasoning that exceeds this model's parameter envelope.
True cost of ownership
About
IBM's flagship enterprise open-weight model with 128K context, trained under strict copyright curation and released with full Apache 2.0 licensing and IP indemnity.
What it does
Download the weights from Hugging Face, pick a quantisation that matches your GPU, and run it locally with Ollama, llama.cpp, vLLM, or a compatible runtime. Enterprise legal compliance & enterprise RAG runs on your own hardware — no per-token bill, no third-party data handling.
Key features
Enterprise legal indemnity and vetted training data sources
128K context window with strong RAG performance
Pure Apache 2.0 without community riders
Use cases
Synthesise dozens of sources with citations you can check.
Build structured comparisons from live web results.
Summarise filings and identify inconsistencies.
Turn scattered notes into a coherent argument.
Quantization & Hardware Fit
VRAM Hardware Compatibility & Memory Allocation
| Quant | VRAM | Quality / Fit on 24GB | Install / Run Command (OLLAMA) | |
|---|---|---|---|---|
| Q4_K_M● ACTIVE | 5 GB | Standard · ✓ Fits (19GB spare) | ollama run granite3-dense:8b | |
| Q5_K_M | 6 GB | Standard · ✓ Fits (18GB spare) | ollama run granite3-dense:8b | |
| Q8_0 | 9 GB | Standard · ✓ Fits (15GB spare) | ollama run granite3-dense:8b | |
| FP16 | 16 GB | Standard · ✓ Fits (8GB spare) | ollama run granite3-dense:8b |
Buyer doubts, cleared
Can I actually run Granite-3.0-8B-Instruct on my 24GB GPU?
What's the fine print on the license?
Does the 128K tokens actually hold up in practice?
User reviews
We evaluated Granite-3.0-8B-Instruct against two hosted alternatives. It won on cost-per-output but lost on latency. For batch work it is a clear yes.
The Q4_K_M build is the sweet spot. Anything larger needs serious hardware for marginal quality gains.
Used Granite-3.0-8B-Instruct for a client project. Documentation was solid, though the fine-tuning path is less polished than base inference.
Solid for enterprise legal compliance & enterprise rag. The learning curve is real but the payoff is there. Start with the smaller build first.
