Granite 3.3 8B is an 8 billion parameter language model developed by IBM, designed for robust text generation tasks. With a context length of 8192 tokens, it excels in handling long-form content creation, summarization, and conversational applications. The model’s architecture is optimized for efficiency, making it a strong contender in its size class. It offers a balance between performance and resource consumption, which is particularly beneficial for users with moderate hardware setups. Compared to other models in the same parameter range, Granite 3.3 8B punches above its weight in terms of both quality and efficiency. It delivers high-quality outputs while requiring less VRAM, ranging from 5.1 to 8.6 GB, which is more accessible for a broader range of users.
Ideal for developers, researchers, and businesses looking to deploy a powerful yet efficient language model locally, Granite 3.3 8B is suitable for a variety of applications, from content generation and chatbots to document summarization and translation. The model’s availability in quantized formats (Q4_K_M, Q8_0) further enhances its accessibility, allowing it to run smoothly on a wide range of hardware, including GPUs with limited VRAM. Users with mid-range GPUs and a few gigabytes of VRAM can confidently deploy this model without significant performance degradation.
| Quantization | Bits | File Size | VRAM Needed | RAM Needed | Quality |
|---|---|---|---|---|---|
| Q4_K_M | 4.5 | 4.603 GB | 5.1 GB | 5.6 GB | 85% |
| Q8_0 | 8 | 8.088 GB | 8.59 GB | 9.09 GB | 98% |
Context window & KV cache
Adds 1.00 GB to VRAMLong chats and RAG inputs cost real memory. Drag to see how 32K vs 128K context shifts your grade.
Model native max: 8K tokens. KV-cache estimate is approximate (±30 %); real usage depends on attention layout.
How to run Granite 3.3 8B
Pick a runtime — copy & paste. Commands are pre-filled with this model’s repo.
Easiest. Single command. OpenAI-compatible API on :11434.
Ollama home →- 1
Pull the model
ollama pull granite3.3:8b - 2
Chat
ollama run granite3.3:8b - 3
Use as API
curl http://localhost:11434/api/chat \ -d '{"model":"granite3.3:8b","messages":[{"role":"user","content":"Hi"}]}'
Community benchmarks
Real tokens/sec reports from people running Granite 3.3 8B on actual hardware.
No community runs yet for this model. Be the first to submit your numbers.
Self-host serving plan
Want to host Granite 3.3 8Bfor many users? Or run it on a card that’s technically too small? Slide the knobs.
VRAM needed
6.3 GB
5.1 GB weights + 0.7 GB KV
Aggregate tok/s
31
across 1 user
Per-user tok/s
31
8 B dense
✅ Fits in 24 GB VRAM with 17.7 GB headroom. Pure-GPU inference — full speed.
Throughput is a sub-linear estimate: doubling users adds ~70 % of single-user TPS until ~8, then plateaus on memory bandwidth. MoE models scale concurrency much better because each user activates a different subset of experts.
See It In Action
Real model outputs generated via RunThisModel.com — watch responses stream in real time.
Outputs generated by real AI models via RunThisModel.com. Generation speed shown is from cloud inference. Local speeds vary by hardware — check your device.
how much VRAM do I need to run Granite 3.3 8B?
Granite 3.3 8B requires 5.1 GB VRAM minimum with Q4_K_M quantization. For full precision you need 8.59 GB.
which quant should I pick?
Q4_K_M is the best quality/VRAM balance — ~92% of FP16 quality at ~25% the footprint. Q8_0 is near-lossless if you have the headroom.
What GPU do I need to run Granite 3.3 8B?
To run Granite 3.3 8B, you need a GPU with at least 5.1 GB of VRAM, but 8.6 GB is recommended for better performance, especially with higher precision.
Is Granite 3.3 8B good for coding?
Yes, Granite 3.3 8B is well-suited for coding tasks due to its enterprise-quality and large context length of 8192 tokens, which allows it to understand complex code structures.
Granite 3.3 8B vs Llama 3.1 8B?
Granite 3.3 8B has a larger context length (8192 tokens) compared to Llama 3.1 8B (typically 2048 tokens), making it better for tasks requiring longer context understanding.
Can I run Granite 3.3 8B on a Mac?
Yes, you can run Granite 3.3 8B on a Mac with an M1 or later Apple Silicon chip, provided you have the necessary VRAM and system resources.
How much VRAM does Granite 3.3 8B need?
Granite 3.3 8B requires between 5.1 GB and 8.6 GB of VRAM, depending on the quantization level used.
Is Granite 3.3 8B censored?
No, Granite 3.3 8B is not censored. It is designed to provide open and unrestricted responses, but it includes safeguards to prevent harmful content.
Is Granite 3.3 8B commercial-use allowed?
Yes, Granite 3.3 8B is licensed under Apache-2.0, which allows for both commercial and non-commercial use.
Granite 3.3 8B context length?
The context length of Granite 3.3 8B is 8192 tokens, which is significantly longer than many other models, allowing it to handle more complex and detailed inputs.
Does Granite 3.3 8B support function calling?
Yes, Granite 3.3 8B supports function calling, enabling it to interact with external systems and APIs for enhanced functionality.
Granite 3.3 8B quantization options?
Granite 3.3 8B supports various quantization options, including 8-bit, 4-bit, and 2-bit, which can reduce VRAM usage and improve inference speed.
Can Granite 3.3 8B run on CPU?
While Granite 3.3 8B can run on a CPU, it will be significantly slower compared to running on a GPU. A high-end CPU with multiple cores is recommended for better performance.
Granite 3.3 8B fine-tuning?
Yes, Granite 3.3 8B can be fine-tuned on your own data to improve performance on specific tasks or domains.
Granite 3.3 8B system requirements?
To run Granite 3.3 8B, you need a system with at least 16 GB of RAM, a GPU with 5.1 GB to 8.6 GB of VRAM, and a multi-core CPU. SSD storage is also recommended for faster loading times.
Granite 3.3 8B performance benchmark?
Granite 3.3 8B can process around 100-200 tokens per second on a high-end GPU like the RTX 3090, with performance varying based on quantization and batch size.
Granite 3.3 8B for RAG?
Yes, Granite 3.3 8B is suitable for Retrieval-Augmented Generation (RAG) tasks due to its large context length and ability to integrate external information effectively.
Granite 3.3 8B for agents?
Granite 3.3 8B can be used to create intelligent agents, thanks to its support for function calling and its ability to handle complex, multi-step interactions.
Granite 3.3 8B for coding vs general?
Granite 3.3 8B excels in both coding and general tasks, but its large context length and function calling support make it particularly strong for coding applications.
Granite 3.3 8B vs ChatGPT?
Granite 3.3 8B offers a larger context length (8192 tokens) and is open-source, while ChatGPT has a more extensive training dataset and is optimized for conversational tasks.
Granite 3.3 8B download size?
The download size of Granite 3.3 8B varies based on the quantization level, ranging from approximately 4 GB (8-bit) to 16 GB (full precision).
Best quant for Granite 3.3 8B?
The best quantization for Granite 3.3 8B depends on your use case. 8-bit quantization provides a good balance between performance and resource usage, while 4-bit is suitable for systems with limited VRAM.