Granite 3.3
IBM's enterprise-grade Granite. Apache 2.0, 128K context, strong on RAG and tool use. Designed for IBM customers but free to use.
Best for enterprise on-prem with Apache 2.0 license
Sizes 2B · 8B
Context 128K
License Apache 2.0
Min VRAM (default size, Q4) 2 GB
Rec VRAM 8 GB
What is Granite 3.3?
Granite 3.3 is IBM's open-weights language model. Released in 2024-12, it ships in 2 sizes (2B, 8B) and is licensed Apache 2.0. The most popular use case is enterprise on-prem with Apache 2.0 license.
VRAM and hardware
The smallest 2B size needs at least 2 GB VRAM; the largest 8B needs around 2 GB.
| Size | Min VRAM (Q4_K_M) | Recommended VRAM | Tokens/sec on 3090 |
|---|---|---|---|
| 2B | 2 GB | 3 GB | ~31 tok/s |
| 8B | 6 GB | 9 GB | ~24 tok/s |
How to run Granite 3.3 locally
Option 1: Ollama (simplest)
ollama pull granite-3-3
ollama run granite-3-3 Option 2: Mullama (production)
mullama pull granite-3-3
mullama run granite-3-3 Option 3: llama.cpp (CLI)
# Download a GGUF from Hugging Face (search "granite-3-3 gguf")
./llama-cli -m granite-3-3.Q4_K_M.gguf -p "Hello, AI!" Option 4: Python with Mullama or llama-cpp-python
from mullama import Model, Context
model = Model.load("granite-3-3.Q4_K_M.gguf", n_gpu_layers=99)
ctx = Context(model, n_ctx=4096)
print(ctx.generate("Hello, AI!", 256)) What you can build with Granite 3.3
- enterprise RAG
- code
- tools
Hardware it fits on
- Apple Silicon (8GB): 2B in MLX
- Apple Silicon (16GB): 8B in MLX
- Apple Silicon (32GB): 8B in MLX or GGUF
- Apple Silicon (64GB+): 8B in MLX
- RTX 3090 (24GB): 8B at Q4_K_M
- RTX 4090 (24GB): same as 3090, ~30% faster
- RTX 5090 (32GB): 8B at Q8_0
- 2× RTX 6000 Ada (96GB total): 8B at Q4_K_M
Related models in the Granite family
This is the only Granite-family model in the catalog right now.
Other models you might consider
- Llama 3.1 (Meta) — 8B, general-purpose chat, agentic workflows, code
- Llama 3.2 (Meta) — 1B, low-end hardware, mobile, edge deployment
- Llama 3.2 Vision (Meta) — 11B, vision + chat on desktop GPUs
- Llama 3.3 (Meta) — 70B, best-in-class 70B for desktops
- Llama 4 (Meta) — Scout 17B (109B MoE), vision + long context (10M tokens)
- Qwen 2.5 (Alibaba) — 0.5B, strongest 7B-32B on consumer hardware