Granite 4.2 30B
30BIBM Granite
The largest model in IBM's Granite 4.2 family, released in August under Apache 2.0. Like the 8B it is a plain dense transformer rather than Granite 4.0's hybrid design, so all 64 layers keep a full cache and context is not cheap: at Q4_K_M it needs about 19.6 GB at 4K, 27.3 GB at 32K and 53.7 GB across the whole 128K window. A 24 GB card runs it comfortably up to 8K and tightly at 16K; 32K needs a 32 GB card, and the full window an 80 GB card or a 96 GB Mac. Q8_0 (about 33.6 GB at 4K) wants a 48 GB card or a 64 GB Mac. Sizes come from IBM's own GGUF files; IBM publishes no perplexity by quantization level, so no quality-loss figure is shown.
131K
Max Context
4
Quant Variants
GGUF Q8_0
Best Quality
—
Accuracy Retained
Quantization Variants
Per-quant VRAM, quality loss, and inference speed on RTX 4090
Measured = run on this site · Estimated = not run here: calculated, or a figure this site has not verified · Community = public reports
Similar models
Compare with Granite 3.1Granite 3.1 8B Instruct
IBM Granite
IBM's enterprise-grade 8B. Strong RAG and tool-use; permissive Apache 2.0 license.
Granite 4.2 8B
IBM Granite
IBM's dense 8B reasoning model from the Granite 4.2 family (3B, 8B, 30B), released in August under Apache 2.0. Thinking is on by default; a non-thinking and a low-effort mode are chosen through the chat template. Unlike Granite 4.0's hybrid models it is a plain transformer, so llama.cpp runs it as `granite` and every one of its 40 layers keeps a full cache: about 6.3 GB at Q4_K_M with a 4K context, 11.1 GB at 32K and 27.6 GB across the whole 128K window. That makes it an 8 GB card model at short contexts and a 16 GB one for long documents. The Q4_K_M size comes from a published GGUF file; IBM publishes no perplexity by quantization level, so no quality-loss figure is shown.
Granite 4.2 3B
IBM Granite
The smallest model in IBM's Granite 4.2 family, released in August under Apache 2.0: a dense 3.7B transformer for laptops, small cards and CPU servers. At Q4_K_M it needs about 2.7 GB at 4K context, 5.1 GB at 32K and 13.3 GB across the whole 128K window, so an 8 GB card runs it comfortably up to 32K and tightly at 64K. Q8_0 is only 4.4 GB at 4K, small enough that there is little reason to go lower on a GPU. Sizes come from IBM's own GGUF files; IBM publishes no perplexity by quantization level, so no quality-loss figure is shown.
Seed-OSS 36B Instruct
ByteDance Seed
Dense 36B with a native 512K context. Q4 weights are ~22GB, which lands on a 24GB card or a 2×16GB split — but the context is the real cost: 512K of KV cache is roughly 128GB on its own, so budget context first and weights second.
Running Granite 4.2 30B locally
At Q4_K_M and 4K of context, Granite 4.2 30B needs about 19.6 GB — 16.8 GB of weights, 1.00 GB of KV cache and a 1.8 GB activation buffer. The smallest cards in this index that clear that comfortably are 24 GB ones — 4 of them, from the RTX 3090 up — and 31 of the 78 cards here do. These are calculated figures, not measurements: the estimate stops counting a card as comfortable at 88% of its VRAM, which is roughly the room a desktop session needs.
What longer context costs
Going from 4K to 32K adds about 7.00 GB, taking the total to 27.3 GB. Weights do not move with context — only the KV cache does, and it grows linearly, so this is the number to watch when planning for long documents. The model's native window is 128K; holding all of it at Q4_K_M would need about 54 GB.
Which build to download
This index only tracks Granite 4.2 30B in GGUF, across 4 levels (Q4_K_M, Q5_K_M, Q6_K, Q8_0). No per-level perplexity sweep has been published for this model, so the quality column is empty rather than estimated — within one model, more bits per weight is the only ordering the data supports. No throughput has been measured for this model on this site's hardware. GGUF runs on llama.cpp and Ollama across NVIDIA, AMD and Apple silicon; AWQ and GPTQ target vLLM on CUDA and ROCm; EXL2 is ExLlamaV2 — CUDA only, and now archived, so local use rather than serving.
Common questions
- How much VRAM does Granite 4.2 30B need?
- About 19.6 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 27.3 GB at 32K. That figure is weights plus KV cache plus a 10% activation buffer, calculated from the model's architecture rather than measured on a card.
- Will Granite 4.2 30B run on a 24GB GPU?
- Yes — at Q4_K_M and 4K context it needs about 19.6 GB, which leaves 4.4 GB spare on a RTX 3090, or on any of the other 24 GB cards here (4 in all). That is the smallest size in this index that clears it comfortably; 31 of 78 do.
- Which quantization of Granite 4.2 30B should I use?
- No published quality comparison exists for this model, so pick by footprint: Q4_K_M is the level most people run, and a higher bits-per-weight level is more faithful. All 4 levels in the index are Q4_K_M, Q5_K_M, Q6_K, Q8_0.
Where this model fits
Sized at Q4_K_M with a 4K context window, smallest card first. Comfortable means the estimate uses at most 88% of the memory.
- Runs comfortably on
- RTX 309024GB · +4.4Radeon RX 7900 XTX24GB · +4.4RTX 409024GB · +4.4Tesla P40 24G24GB · +4.4Mac M5 32G32GB · +4.4
- Tight but possible
- Radeon RX 7900 XT20GB
- Ships in
- GGUF
- Try it yourself
- Size it in the calculatorGenerate the run command
How to actually run this
Deployment guides for this model and this class of hardware.
This page's figures change when the model or the runtime does.Last updated 2026-10-18 RSS → /feed.xml