Granite 3.1 2B Instruct — Hardware Requirements & GPU Compatibility
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Granite 3.1 2B Instruct is IBM's 2.5-billion-parameter dense instruction-tuned model, fine-tuned from Granite-3.1-2B-Base on permissively licensed open instruction datasets plus internally generated synthetic data aimed at long-context problems. It targets business-assistant work such as summarization, classification, extraction, question answering, retrieval-augmented generation, code tasks and function calling, and supports twelve languages including English, German, Spanish, French, Japanese, Arabic and Chinese. At this size it runs on almost any consumer GPU, and on a laptop CPU once quantized. Context length is 131,072 tokens. It is released under the Apache 2.0 license, permitting unrestricted commercial and research use, and was published in December 2024. IBM has since superseded it with Granite 3.3 2B Instruct, which keeps the same size class.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 64K)Best on 24 GB (Q4_K_M · ~329 tok/s · 128K)Macs from 8 GB (Q4_K_M · ~36 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Granite 3.1 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-3.1-2b-instruct-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 20.8 GB free · 16K context
Download: granite-3.1-2b-instruct-Q4_K_M.gguf1.55 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
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$5+ bonus for new users
Runs on RTX 3060 12GB (8 GB headroom) · Q4_K_M
- Generation speed
- ~118 tok/s
- generation speed
- Cost per 1M output tokens
- $0.14
- per 1M output tokens
How Much VRAM Does Granite 3.1 2B Instruct Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 1.5 GB | 4.1 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 128K context · ~426 tok/s | |
| 1.6 GB | 4.1 GB | 3-bit small — 3-bit small quantization | Fits: 128K context · ~415 tok/s | |
| 1.7 GB | 4.2 GB | 3-bit medium — 3-bit medium quantization | Fits: 128K context · ~385 tok/s | |
| 1.7 GB | 4.3 GB | 4-bit · legacy — 4-bit legacy quantization | Fits: 128K context · ~379 tok/s | |
| 2.0 GB | 4.5 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 128K context · ~329 tok/s | |
| 2.3 GB | 4.8 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 128K context · ~289 tok/s | |
| 2.6 GB | 5.1 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 128K context · ~256 tok/s | |
| 3 GB | 5.5 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 128K context · ~218 tok/s |
Selected Q4_K_M
4-bit medium quantization3.2GBat 16K context
- Weights
- 1.5 GB
- KV cache
- 1.3 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 20.8 GB free · ~329 tok/s
≥50 tok/s needs ≥153 GB/s · RTX 5090 ~585, RTX 3090 Ti ~329
Which Hardware Can Run Granite 3.1 2B Instruct?
Q4_K_M · 2.0 GBGranite 3.1 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 131K context window can add up to 10.6 GB, bringing total usage to 12.6 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run Granite 3.1 2B Instruct, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 4.8 GB free · up to 64K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~248Simulate output on RTX 3080RTX 2080 Ti~201Simulate output on RTX 2080 TiRTX 3070 Ti~199Simulate output on RTX 3070 TiM3*8 GB~36Simulate output on iMac 24" M3 (8 GB)Orin Nano~33Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~24Simulate output on iPhone 1720 GB
fits · 16.8 GB free · up to 128K ctx
1 GPU
RX 7900 XT~241Simulate output on RX 7900 XT32–40 GB
fits · 28.8 GB free · up to 128K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~585Simulate output on RTX 5090M5 Max36 GB~162Simulate output on Mac Studio M5 Max (36 GB)M532 GB~54Simulate output on MacBook Air 13" M5 (32 GB)Orin~67Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~44Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
How good is Granite 3.1 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
Here are all 2 boards that have results for this model, sorted by their weight in the llmrun Score; badges show who ran the test.
- LMArena Text
LMArena · Oct 2026Arena
Arena1178.9
#157/188 open
- MMLU-Pro
TIGER-LabIndependent
Independent32.0%
#117/146 open
Alternatives to Granite 3.1 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 |
| Ouro 2.6B ThinkingByteDance | — | 12.1 GB | ~102 | same size |
Specifications
- Publisher
- IBM
- Family
- Granite
- Parameters
- 2.5B
- Architecture
- GraniteForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 49,155
- Release Date
- 2024-12-06
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does Granite 3.1 2B Instruct need?
Granite 3.1 2B Instruct requires 2.0 GB of VRAM at Q4_K_M, or 5.5 GB at BF16. Full 131K context adds up to 10.6 GB (12.6 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 2.5B × 4.8 bits ÷ 8 = 1.5 GB
KV Cache + Overhead ≈ 0.5 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 ≈ 11.1 GB (at full 131K context)
VRAM usage by quantization
What's the best quantization for Granite 3.1 2B Instruct?
For Granite 3.1 2B Instruct, Q4_K_M (2.0 GB) offers the best balance of quality and VRAM usage. Q4_K_L (2.0 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 3.1 2B Instruct on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Granite 3.1 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 3.1 2B Instruct locally?
Yes — Granite 3.1 2B Instruct can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Granite 3.1 2B Instruct?
At Q4_K_M, Granite 3.1 2B Instruct can reach ~2412 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~329 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 ÷ 1.99 × 0.65 = ~2613 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 3.1 2B Instruct?
At Q4_K_M, the download is about 1.52 GB. The full-precision BF16 version is 5.07 GB. The smallest option (IQ2_XXS) is 0.70 GB.
Which GPUs can run Granite 3.1 2B Instruct?
52 consumer GPUs can run Granite 3.1 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 3.1 2B Instruct?
163 devices with unified memory can run Granite 3.1 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.