Granite 3.0 2B Instruct — Hardware Requirements & GPU Compatibility
Chat
Granite-3.0-2B-Instruct is IBM's 2-billion-parameter chat model, fine-tuned from Granite-3.0-2B-Base on a mix of permissively licensed open instruction datasets and IBM's own synthetic data, using supervised fine-tuning, reinforcement-learning alignment, and model merging. It is one of several Granite 3.0 sizes IBM released together, aimed at enterprise use cases such as summarization, question answering, and retrieval-augmented generation. It supports dialogue in twelve languages, primarily English, though multilingual performance trails English performance. At 2B parameters, it is small enough to run on a laptop CPU or any consumer GPU, including edge deployments. Context length is 4,096 tokens. It is released under the Apache 2.0 license, permitting unrestricted commercial and research use, and was published in October 2024. It was later superseded by the Granite 3.1 model family.
5.2K downloads
48 likesSpecifications
- Publisher
- IBM
- Family
- Granite
- Parameters
- 2.6B
- Architecture
- GraniteForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 49,155
- Release Date
- 2024-10-02
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Granite 3.0 2B Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 1.6 GB | 1.8 GB | 1.12 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 1.6 GB | 1.8 GB | 1.15 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 1.8 GB | 1.9 GB | 1.28 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 1.8 GB | 1.9 GB | 1.32 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 2.0 GB | 2.2 GB | 1.58 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 2.3 GB | 2.5 GB | 1.88 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 2.6 GB | 2.8 GB | 2.17 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 3.1 GB | 3.3 GB | 2.63 GB | 8-bit quantization, near-lossless |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Granite 3.0 2B Instruct?
Q4_K_M · 2.0 GBGranite 3.0 2B Instruct (Q4_K_M) requires 2.0 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 3+ GB is recommended. Using the full 4K context window can add up to 0.2 GB, bringing total usage to 2.2 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Granite 3.0 2B Instruct?
Q4_K_M · 2.0 GB59 devices with unified memory can run Granite 3.0 2B Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download Granite 3.0 2B Instruct
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Benchmarks
Benchmark details →Related Models
Frequently Asked Questions
- How much VRAM does Granite 3.0 2B Instruct need?
Granite 3.0 2B Instruct requires 2.0 GB of VRAM at Q4_K_M, or 5.7 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 2.6B × 4.8 bits ÷ 8 = 1.6 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 0.6 GB (at full 4K context)
VRAM usage by quantization
Q4_K_M2.0 GBQ4_K_M + full context2.2 GB- What's the best quantization for Granite 3.0 2B Instruct?
For Granite 3.0 2B Instruct, Q4_K_M (2.0 GB) offers the best balance of quality and VRAM usage. Q4_K_L (2.1 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XS at 1.3 GB.
VRAM requirement by quantization
IQ2_XS1.3 GBQ3_K_S1.6 GBIQ4_XS1.9 GBQ4_K_M ★2.0 GBQ5_K_S2.3 GBBF165.7 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Granite 3.0 2B Instruct on a Mac?
Granite 3.0 2B Instruct requires at least 1.3 GB at IQ2_XS, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Granite 3.0 2B Instruct locally?
Yes — Granite 3.0 2B Instruct can run locally on consumer hardware. At Q4_K_M quantization it needs 2.0 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Granite 3.0 2B Instruct?
At Q4_K_M, Granite 3.0 2B Instruct can reach ~2342 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~320 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 2.0 × 0.65 = ~2537 tok/s
Estimated speed at Q4_K_M (2.0 GB)
~2537 tok/s~320 tok/s~2537 tok/s~2342 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Granite 3.0 2B Instruct?
At Q4_K_M, the download is about 1.58 GB. The full-precision BF16 version is 5.27 GB. The smallest option (IQ2_XS) is 0.79 GB.
- Which GPUs can run Granite 3.0 2B Instruct?
52 consumer GPUs can run Granite 3.0 2B Instruct at Q4_K_M (2.0 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Granite 3.0 2B Instruct?
59 devices with unified memory can run Granite 3.0 2B Instruct at Q4_K_M (2.0 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.