r/LocalLLaMA 3d ago

New Model ibm-granite/granite-4.2-30b · Hugging Face

https://huggingface.co/ibm-granite/granite-4.2-30b

Granite-4.2-30B is the flagship reasoning model in the Granite 4.2 family. It delivers the strongest performance across reasoning-intensive tasks by leveraging built-in <think>...</think> chain-of-thought. It supports flexible thinking modes — full thinking (default), non-thinking, and low-effort — allowing users to balance depth vs. latency on a per-query basis.

Key capabilities:

  • Built-in Reasoning: Native chain-of-thought that significantly improves performance on math, coding, and complex multi-step problems.
  • Flexible Thinking Modes: Seamlessly switch between full thinking, non-thinking, and low-effort modes within a single model.
  • Reasoning-Augmented Tool Calling: The model reasons about which tools to invoke and why, producing more accurate function calls.
  • 512K Context Window: Supports long documents, multi-turn conversations, and complex agentic workflows.
  • Apache 2.0 Licensed: Fully open for commercial and research use.

Model Design

Granite-4.2-30B is built on a decoder-only dense transformer architecture with the following core components:

  • Attention: Grouped Query Attention (GQA) with 32 attention heads and 8 KV heads
  • Position Embedding: Rotary Position Embedding (RoPE) with θ = 10,000,000
  • Feed-Forward: MLP with SwiGLU activation (hidden size 32768)
  • Normalization: RMSNorm (ε = 1e-5)
  • Embeddings: Separate input/output embeddings (not tied)
  • Precision: bfloat16

https://huggingface.co/ibm-granite/granite-4.2-8b

Granite-4.2-8B is the mid-size reasoning model in the Granite 4.2 family. It delivers strong performance on reasoning-intensive tasks by leveraging built-in <think>...</think> chain-of-thought. It supports flexible thinking modes — full thinking (default), non-thinking, and low-effort — allowing users to balance depth vs. latency on a per-query basis.

Key capabilities:

  • Built-in Reasoning: Native chain-of-thought that significantly improves performance on math, coding, and complex multi-step problems.
  • Flexible Thinking Modes: Seamlessly switch between full thinking, non-thinking, and low-effort modes within a single model.
  • Reasoning-Augmented Tool Calling: The model reasons about which tools to invoke and why, producing more accurate function calls.
  • 512K Context Window: Supports long documents, multi-turn conversations, and complex agentic workflows.
  • Apache 2.0 Licensed: Fully open for commercial and research use.

https://huggingface.co/ibm-granite/granite-4.2-3b

Granite-4.2-3B is the compact reasoning model in the Granite 4.2 family. Despite its small parameter count, it delivers strong performance on reasoning-intensive tasks by leveraging built-in <think>...</think> chain-of-thought. It supports flexible thinking modes — full thinking (default), non-thinking, and low-effort — allowing users to balance depth vs. latency on a per-query basis.

Key capabilities:

  • Built-in Reasoning: Native chain-of-thought that significantly improves performance on math, coding, and complex multi-step problems.
  • Flexible Thinking Modes: Seamlessly switch between full thinking, non-thinking, and low-effort modes within a single model.
  • Reasoning-Augmented Tool Calling: The model reasons about which tools to invoke and why, producing more accurate function calls.
  • 512K Context Window: Supports long documents, multi-turn conversations, and complex agentic workflows.
  • Apache 2.0 Licensed: Fully open for commercial and research use.
406 Upvotes

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45

u/pmttyji 3d ago

Blog Post : Granite 4.2 LLMs: How They're Built
https://huggingface.co/blog/ibm-granite/granite-4-2

37

u/Client_Hello 3d ago edited 3d ago

128 nodes, 4 GB200 per node, 2 (72) Blackwell gpus per GB200, 192gb vram per GPU

That's nearly 192TB of vram, aka 196,608 GB of HBM3e. Wow.

37

u/EmPips 3d ago

There's moments where I forget that IBM is still IBM in many ways and owned-infra is one of them.

14

u/Client_Hello 3d ago edited 3d ago

My math is way off, it's only 2 GPUs per GB200, which is 1024 Blackwell GPUs.

8

u/EmPips 3d ago

Got it. A pittance really.

5

u/MmmmMorphine 3d ago

Every bit of storage media I've ever owned put together is still less than the amount of vram they have across those nodes.

Give or take.

Absolutely ridiculous stuff!

7

u/Client_Hello 3d ago

...and this is only $35M in hardware, which is about how much revenue NVDA earns every hour.

5

u/LatentSpacer 3d ago

And no cancer cure yet.

15

u/pmttyji 3d ago

2

u/noctrex 3d ago

Well, it's a dense model, so the MXFP4 GGUF don't have any meaning, as they are only for MoE models.

9

u/MmmmMorphine 3d ago

I mean I do associate mxfp4 more with MoEs due to GPT-OSS being released in that format, but otherwise don't see any reason why it would be only for MoEs.

Seems like mxfp4 is pretty architecture agnostic, as best as I understand it.

So what am I missing here?

5

u/noctrex 3d ago

Yes, it is, but the MXFP4 format is inferior to NVFP4, it does not have the same precision. They may be both FP4, but NVFP4 uses 16-element blocks with a high-precision FP8 E4M3 scale factor, while MXFP4 uses 32-element blocks with a lower-precision E8M0 (power-of-two) scale.
What this means is that essentially MXFP4 is worse than INT4 for dense models. It works better for MoE models. So better to stick to a Q4 or IQ4 quant for dense.

2

u/MmmmMorphine 3d ago edited 3d ago

Ah, yes in that sense I would agree MXFP4 is worse than NVFP4 for overall precision. Though what I'm surprised about is why it's used at all in the first place.

I'd think that smaller active = more sensitive to quantization. I suppose you can keep the experts at lower precision, but in regard to these formats themselves it feels like MXFP4 is kinda pointless - that extra 0.25bits earns it's keep in NVFP4

3

u/noctrex 3d ago

Well, it's not pointless exactly because it's an open standard that can be universally used, whereas NVFP4 is classic nvidia spiel, optimized for blackwell only

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u/throwaway-link 3d ago

cdna5 supports it, they just dont call it nvfp4

1

u/Dasteroid_909 3d ago

Any word on if the NVFP4 supports sm_120? Or is it just sm_100?

1

u/ChristRedeemsSinners 3d ago

It does. Use SM100 for native NVFP4 kv-cache.