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How to Run Granite 4.2 30B Locally — Setup Guide (September 2026)

Granite 4.2 30B is IBM's Apache-2.0 enterprise model, and unusually for late 2026 it is a straightforward dense transformer with conventional full attention on every layer. That makes it predictable to fit — but it also means its KV cache grows the old-fashioned way, so context length costs real memory here in a way it does not on the hybrid-attention models nearby.

Numbers you can trust

We don't print guessed speeds. Every measured number on this page came from TurboLLM's own auto-tuner running Granite 4.2 30B on real hardware — labeled as such. When you run it yourself, TurboLLM benchmarks it on your exact GPU and shows a VRAM-fit verdict before you load, and the real measured tokens/sec once it's running.

Does it fit your GPU?

29.3B dense parameters, Apache-2.0, 64 layers with full attention throughout (8 KV heads, 128 head dimension) and a 131K native context. IBM publishes first-party GGUFs, so there is no waiting on a community conversion.

Granite 4.2 30B
IBM · GGUF
Dense · full attention
toolscode
VRAMFit
16 GBQ3_K_M (14.1 GB) fits only at a short context — budget for the KV cache · 131K context maximum
24 GBQ4_K_M (17.7 GB) or Q5_K_M (20.8 GB) in VRAM with a usable context · 131K context
32 GBQ6_K (24.0 GB) in VRAM with room for long context · 131K context
48 GB+Q8_0 (31.1 GB) fully in VRAM at full context · 131K context
ibm-granite/granite-4.2-30b-GGUF
View on Hugging Face →

How to run it

  1. Install TurboLLM

    npx turbollm — no install step, works on Windows, macOS, and Linux. It detects your GPU and auto-provisions a matching llama-server build (no CUDA toolkit, no Python, no compiler).

  2. Get the model

    Paste ibm-granite/granite-4.2-30b-GGUF into TurboLLM's in-app Hugging Face search — it lists every quant with a VRAM-fit verdict against your real free VRAM, and downloads are resumable and SHA-256 verified.

  3. Let auto-fit pick the setup

    Flip the auto-fit toggle and TurboLLM chooses the GPU/CPU layer split (and, for a Mixture-of-Experts model, the expert-offload split) for your card — the same manual flag-hunting every other guide walks you through by hand.

  4. Load it

    TurboLLM benchmarks the model on your exact GPU as it loads and shows the real measured tokens/sec — not a number copied from someone else's card.

  5. Use it

    Chat in the built-in UI, or point any OpenAI- or Anthropic-compatible tool (including Claude Code) at TurboLLM's local API — see the API docs.

One honest caveat

Full attention on all 64 layers makes context expensive. Where a hybrid-attention model of the same size keeps a growing KV cache on only a handful of layers, Granite keeps one on every layer — so a 32K context costs about 8.6 GB of KV at f16, on top of the weights. Dropping the KV cache to q8_0 roughly halves that, and is usually the difference between this fitting your card and not.

FAQ

Will Granite 4.2 30B run on my GPU?

Check the VRAM table above for the tier closest to your card. If you're not sure which tier you're in, or want picks tailored to your exact hardware, use What can I run? — enter your VRAM (or Apple unified memory) and it suggests the model, quant, and offload split that fits, the same logic TurboLLM's auto-fit runs when you load a model.

Why does Granite 4.2 30B suddenly get slow or stutter?

The usual cause is silent VRAM spill: once a model's weights plus its KV cache stop fitting in VRAM, the driver quietly offloads the overflow to system RAM over PCIe, and that path runs 5–10× slower with no error message — just a sudden slowdown. TurboLLM's auto-fit sizes the quant and GPU/CPU split against your card's real free VRAM (including KV-cache growth at your chosen context) specifically to avoid this, and shows a VRAM-fit verdict before you load rather than after it's already crawling. Full mechanics in VRAM spill explained.

Which quant should I use for Granite 4.2 30B?

Pick the highest quant that fits your VRAM with room left over for the KV cache and compute buffer — not just the model weights. As a starting point, Q4_K_M is usually the sweet spot (small quality loss for a large size drop); step up to Q5_K_M/Q6_K/Q8_0 if you have headroom, or down to Q3_K_M/an IQ quant if you don't. TurboLLM shows a VRAM-fit verdict for every quant against your card's real free VRAM before you download — see Quantization explained for what the letters and numbers actually mean, or llama.cpp flags that actually matter for the other settings worth knowing.

Run it now

$ npx turbollm

One command detects your GPU, provisions the right engine, and opens the UI. New here? Start with Install & first run and Quantization explained. On different hardware, see RTX 3090, RTX 4090 or RTX 5080. Also see how to run it or how to run it.