Granite 3.0 2B Instruct — Hardware Requirements & GPU Compatibility
ChatFunctions
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 It was later superseded by the Granite 3.1 model family.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 4K)Best on 24 GB (Q4_K_M · ~320 tok/s · 4K)Macs from 8 GB (Q4_K_M · ~35 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Granite 3.0 2B Instruct
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
ollama run hf.co/bartowski/granite-3.0-2b-instruct-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 21.8 GB free · 4K context
Download: granite-3.0-2b-instruct-Q4_K_M.gguf1.60 GBbartowski
Ways to runOther ways
each opens step-by-stepRun in cloud
Your own private copy · Q4_K_M · 24 GB GPU
- The easiest way to try this model
- Running in a few minutes, easy setup
- Chat right here or connect any app
$5+ bonus for new users
Runs on RTX 3080 Ti (12 GB) (9 GB headroom) · Q4_K_M
- Generation speed
- ~289 tok/s
- generation speed
- Cost per 1M output tokens
- $0.12
- per 1M output tokens
How Much VRAM Does Granite 3.0 2B Instruct Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 1.6 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 4K context · ~412 tok/s | |
| 1.6 GB | — | 3-bit small — 3-bit small quantization | Fits: 4K context · ~404 tok/s | |
| 1.8 GB | — | 3-bit medium — 3-bit medium quantization | Fits: 4K context · ~374 tok/s | |
| 1.8 GB | — | 4-bit · legacy — 4-bit legacy quantization | Fits: 4K context · ~368 tok/s | |
| 2.0 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 4K context · ~320 tok/s | |
| 2.3 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 4K context · ~280 tok/s | |
| 2.6 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Fits: 4K context · ~248 tok/s | |
| 3.1 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 4K context · ~211 tok/s |
Selected Q4_K_M
4-bit medium quantization2.2GBat 4K context
- Weights
- 1.6 GB
- KV cache
- 0.3 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 21.8 GB free · ~320 tok/s
≥50 tok/s needs ≥158 GB/s · RTX 5090 ~568, RTX 3090 Ti ~320
Which Hardware 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. 163 devices with unified memory can run Granite 3.0 2B Instruct, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 5.8 GB free · up to 4K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~241Simulate output on RTX 3080RTX 2080 Ti~195Simulate output on RTX 2080 TiRTX 3070 Ti~193Simulate output on RTX 3070 TiM3*8 GB~35Simulate output on iMac 24" M3 (8 GB)Orin Nano~32Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~23Simulate output on iPhone 1720 GB
fits · 17.8 GB free · up to 4K ctx
1 GPU
RX 7900 XT~234Simulate output on RX 7900 XT32–40 GB
fits · 29.8 GB free · up to 4K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~568Simulate output on RTX 5090M5 Max36 GB~157Simulate output on Mac Studio M5 Max (36 GB)M532 GB~52Simulate output on MacBook Air 13" M5 (32 GB)Orin~65Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~43Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
How good is Granite 3.0 2B Instruct?
llmrun Score blends public benchmark boards into one number; 100 = the average open model.
Not enough data yet for an llmrun Score.
A Score needs results on enough independent boards. How the Composite Score is built
Benchmarks
This is the single board showing results for this model; badges show who ran the test.
- LMArena Text
LMArena · Oct 2026Arena
Arena1156.7
#166/188 open
Alternatives to Granite 3.0 2B Instruct
Granite family · 28 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| WeVisOne VL 2BTencent | — | 3.6 GB | ~328 | same size |
| MiniCPM5 2BOpenBMB | — | 2.5 GB | ~345 | same size |
| North Micro Vision InstructCohere | — | 3.7 GB | ~323 | same size |
| LFM2.5 2.6BLiquid AI | — | 2.3 GB | ~321 | same size |
| Gemma 2 2B ITGoogle | — | 2.5 GB | ~379 | same size |
| Phi 2Microsoft | — | 2.6 GB | ~248 | same size |
Specifications
- 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
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)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 0.6 GB (at full 4K context)
VRAM usage by quantization
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_XXS at 1.2 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Granite 3.0 2B Instruct on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Granite 3.0 2B Instruct. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 2.0 GB of usable unified memory (RAM minus macOS overhead).
Can I run Granite 3.0 2B Instruct locally?
Yes — Granite 3.0 2B Instruct can run locally on consumer hardware. 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 ÷ 2.05 × 0.65 = ~2537 tok/s
Estimated speed at Q4_K_M (2.0 GB)
Real-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_XXS) is 0.72 GB.
Which GPUs can run Granite 3.0 2B Instruct?
52 consumer GPUs can run Granite 3.0 2B Instruct at Q4_K_M. 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?
163 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, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (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.