Granite Guardian 3.2 8B Factuality Detection — Hardware Requirements & GPU Compatibility
Chat
170 downloads
3 likesSpecifications
- Publisher
- IBM
- Parameters
- 8.2B
- Architecture
- GraniteForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 49,155
- Release Date
- 2026-03-03
- License
- Apache 2.0
Get Started
How Much VRAM Does Granite Guardian 3.2 8B Factuality Detection Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 17.0 GB | 38.1 GB | 16.34 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Granite Guardian 3.2 8B Factuality Detection?
BF16 · 17.0 GBGranite Guardian 3.2 8B Factuality Detection (BF16) requires 17.0 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 23+ GB is recommended. Using the full 131K context window can add up to 21.1 GB, bringing total usage to 38.1 GB. 6 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run Granite Guardian 3.2 8B Factuality Detection?
BF16 · 17.0 GB21 devices with unified memory can run Granite Guardian 3.2 8B Factuality Detection, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Granite Guardian 3.2 8B Factuality Detection need?
Granite Guardian 3.2 8B Factuality Detection requires 17.0 GB of VRAM at BF16. Full 131K context adds up to 21.1 GB (38.1 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 8.2B × 16 bits ÷ 8 = 16.3 GB
KV Cache + Overhead ≈ 0.7 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 21.8 GB (at full 131K context)
VRAM usage by quantization
BF1617.0 GBBF16 + full context38.1 GB- Can I run Granite Guardian 3.2 8B Factuality Detection on a Mac?
Granite Guardian 3.2 8B Factuality Detection requires at least 17.0 GB at BF16, 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 Guardian 3.2 8B Factuality Detection locally?
Yes — Granite Guardian 3.2 8B Factuality Detection can run locally on consumer hardware. At BF16 quantization it needs 17.0 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Granite Guardian 3.2 8B Factuality Detection?
At BF16, Granite Guardian 3.2 8B Factuality Detection can reach ~172 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~39 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 17.0 × 0.55 = ~172 tok/s
Estimated speed at BF16 (17.0 GB)
AMD Instinct MI300X~172 tok/sNVIDIA GeForce RTX 4090~39 tok/sNVIDIA H100 SXM~128 tok/sAMD Instinct MI250X~106 tok/sReal-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 BF16, the download is about 16.34 GB.
- Which GPUs can run Granite Guardian 3.2 8B Factuality Detection?
6 consumer GPUs can run Granite Guardian 3.2 8B Factuality Detection at BF16 (17.0 GB). Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XT, AMD Radeon RX 7900 XTX. 1 GPU have plenty of headroom for comfortable inference.
- Which devices can run Granite Guardian 3.2 8B Factuality Detection?
21 devices with unified memory can run Granite Guardian 3.2 8B Factuality Detection at BF16 (17.0 GB), including Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.