Granite Guardian 3.2 8B Factuality Detection — Hardware Requirements & GPU Compatibility
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Granite Guardian 3.2 8B Factuality Detection is a 8.2B-parameter open language model from IBM in the Granite family. It supports a context window of up to 131,072 tokens. At Q4_K_M it needs about 5.54 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 16K)Best on 24 GB (Q4_K_M · ~118 tok/s · 64K)Macs from 16 GB (Q4_K_M · ~25 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Granite Guardian 3.2 8B Factuality Detection
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
vllm serve ibm-granite/granite-guardian-3.2-8b-factuality-detection --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 3080 Ti (12 GB) (4 GB headroom) · Q4_K_M
- Generation speed
- ~107 tok/s
- generation speed
- Cost per 1M output tokens
- $0.32
- per 1M output tokens
How Much VRAM Does Granite Guardian 3.2 8B Factuality Detection Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 4.1 GB | 9.1 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 64K context · ~159 tok/s | |
| 4.6 GB | 9.7 GB | 3-bit medium — 3-bit medium quantization | Fits: 64K context · ~142 tok/s | |
| 5.5 GB | 10.6 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 64K context · ~118 tok/s | |
| 6.5 GB | 11.5 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 64K context · ~101 tok/s | |
| 7.4 GB | 12.4 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 64K context · ~89 tok/s | |
| 8.8 GB | 13.8 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 64K context · ~74 tok/s | |
| 17.0 GB | 22 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 32K context · ~39 tok/s |
Selected Q4_K_M
4-bit medium quantization7.9GBat 16K context
- Weights
- 4.9 GB
- KV cache
- 2.7 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 16.1 GB free · ~118 tok/s
≥50 tok/s needs ≥426 GB/s · RTX 5090 ~210, RTX 3090 Ti ~118
Which Hardware Can Run Granite Guardian 3.2 8B Factuality Detection?
Q4_K_M · 5.5 GBGranite Guardian 3.2 8B Factuality Detection (Q4_K_M) requires 5.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 8+ GB is recommended. Using the full 131K context window can add up to 21.1 GB, bringing total usage to 26.7 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti. 151 devices with unified memory can run Granite Guardian 3.2 8B Factuality Detection, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), NVIDIA Jetson Orin Nano 8GB (Super).
≤ 11 GB
≤ 11 GB tier →fits · 0.1 GB free · up to 16K ctx
16 GPUs · 1 device family
RTX 3080~89Simulate output on RTX 3080RTX 2080 Ti~72Simulate output on RTX 2080 TiRTX 3070 Ti~71Simulate output on RTX 3070 TiOrin Nano~12Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)20 GB
fits · 12.1 GB free · up to 64K ctx
1 GPU
RX 7900 XT~87Simulate output on RX 7900 XT32–40 GB
fits · 24.1 GB free · up to 128K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~210Simulate output on RTX 5090M5 Max36 GB~58Simulate output on Mac Studio M5 Max (36 GB)M532 GB~19Simulate output on MacBook Air 13" M5 (32 GB)Orin~24Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~16Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Granite Guardian 3.2 8B Factuality Detection
Granite family · 28 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| ALoDLM 8BAmazon | — | 7.6 GB | ~119 | same size |
| Llm Jp 4.1 8B Thinkingllm-jp | — | 7.6 GB | ~115 | same size |
| Ling 3.0 TinyInclusion AI | — | 5.5 GB | ~364 | same size |
| LFM2.5 8B A1BLiquid AI | — | 5.6 GB | ~335 | same size |
| BitCPM CANN 8BOpenBMB | — | 5.6 GB | ~127 | same size |
| Hy MT2 7BTencent | — | 7.3 GB | ~122 | same size |
Specifications
- Publisher
- IBM
- Family
- Granite
- Parameters
- 8.2B
- Architecture
- GraniteForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 49,155
- Release Date
- 2025-11-10
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does Granite Guardian 3.2 8B Factuality Detection need?
Granite Guardian 3.2 8B Factuality Detection requires 5.5 GB of VRAM at Q4_K_M, or 17.0 GB at BF16. Full 131K context adds up to 21.1 GB (26.7 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 8.2B × 4.8 bits ÷ 8 = 4.9 GB
KV Cache + Overhead ≈ 0.6 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 ≈ 21.8 GB (at full 131K context)
VRAM usage by quantization
What's the best quantization for Granite Guardian 3.2 8B Factuality Detection?
For Granite Guardian 3.2 8B Factuality Detection, Q4_K_M (5.5 GB) offers the best balance of quality and VRAM usage. Q5_K_M (6.5 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 4.1 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Granite Guardian 3.2 8B Factuality Detection on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run Granite Guardian 3.2 8B Factuality Detection. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 5.5 GB of usable unified memory (RAM minus macOS overhead).
Can I run Granite Guardian 3.2 8B Factuality Detection locally?
Yes — Granite Guardian 3.2 8B Factuality Detection can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Granite Guardian 3.2 8B Factuality Detection?
At Q4_K_M, Granite Guardian 3.2 8B Factuality Detection can reach ~866 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~118 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 ÷ 5.54 × 0.65 = ~939 tok/s
Estimated speed at Q4_K_M (5.5 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Granite Guardian 3.2 8B Factuality Detection?
At Q4_K_M, the download is about 4.90 GB. The full-precision BF16 version is 16.34 GB. The smallest option (Q2_K) is 3.47 GB.
Which GPUs can run Granite Guardian 3.2 8B Factuality Detection?
52 consumer GPUs can run Granite Guardian 3.2 8B Factuality Detection at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 40 GPUs have plenty of headroom for comfortable inference.
Which devices can run Granite Guardian 3.2 8B Factuality Detection?
151 devices with unified memory can run Granite Guardian 3.2 8B Factuality Detection at Q4_K_M (5.5 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.