FitMyLLM
IBM/Dense

IBMGranite 4.2 30B

Granite 4.2 30B — the largest Granite 4.2 model: a dense 30B with built-in reasoning, given an extra agentic fine-tuning phase over its smaller siblings. Apache 2.0, 128K native context.

chatcodingreasoningmultilingualmathtool_useagentic
29.28B
Parameters
128K
Context length
10
Benchmarks
10
Quantizations
32K
HF downloads
Architecture
Dense
Released
2026-08-25
Layers
64
KV Heads
8
Head Dim
128
Family
granite

Quantization Options

Context length:
QuantBitsVRAM @ 16KQuality
Q3_K_M4
18.1 GB
15.1 + 3.0 KV
low
Q3_K_L4.3
19.2 GB
16.2 + 3.0 KV
moderate
IQ4_XS4.46
19.8 GB
16.8 + 3.0 KV
moderate
Q4_K_S4.67
20.6 GB
17.6 + 3.0 KV
moderate
Q4_K_M4.89
21.4 GB
18.4 + 3.0 KV
good
Q5_K_S5.57
23.9 GB
20.9 + 3.0 KV
good
Q5_K_M5.7
24.4 GB
21.4 + 3.0 KV
good
Q6_K6.56
27.5 GB
24.5 + 3.0 KV
excellent
Q8_08.5
34.6 GB
31.6 + 3.0 KV
lossless
FP1616
62.0 GB
59.0 + 3.0 KV
lossless

Select your GPU above to see speed estimates and compatibility for each quantization.

Deploying for a team or in production? Size GPUs, cost & scaling in Enterprise →
▸ READY TO RUN THIS?RENT BY THE HOUR

RENT A GPU AND RUN GRANITE 4.2 30B NOW

Spin up an A100 / H100 / 4090 in ~60s. Pay by the second. Cancel anytime.

Community Ratings

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Benchmarks (10)

AIME89.2
MMLU-PRO77.6
LiveCodeBench75.8
GPQA Diamond64.4
SWE-bench57.0
AA Long Context49.0
SciCode37.8
Terminal-Bench 2.126.6
HLE11.2
Terminal-Bench 40.0

Run this model

▸Easiest way to get started·Beginners
DOCS ↗
curl -fsSL https://ollama.com/install.sh | sh
$ollama run granite4.2:30b-q4_K_M

Downloads and runs automatically. Add --verbose for speed stats.

▸ SETUP GUIDE
>_

Auto-setup with fitmyllm CLI

Detects your GPU, recommends the best model, downloads it, and starts chatting — zero config. Benchmarks your speed and contributes anonymous data to improve predictions.

pip install fitmyllmthen run fitmyllmLearn more
Auto-detect GPULive tok/s in chatSpeed benchmarks9 inference engines

GPUs that can run this model

At Q4_K_M quantization. Sorted by minimum VRAM.

NVIDIA RTX A4500
20 GB VRAM • 640 GB/s
NVIDIA
$2000
Apple M4 Pro (24GB)
24 GB VRAM • 273 GB/s
APPLE
$1399
NVIDIA L4 24GB
24 GB VRAM • 300 GB/s
NVIDIA
$2500
Apple M2 (24GB)
24 GB VRAM • 100 GB/s
APPLE
$999
Apple M3 (24GB)
24 GB VRAM • 100 GB/s
APPLE
$999
Apple M4 (24GB)
24 GB VRAM • 120 GB/s
APPLE
$699
NVIDIA Tesla M40 24 GB
24 GB VRAM • 288 GB/s
NVIDIA
NVIDIA Tesla P10
24 GB VRAM • 694 GB/s
NVIDIA
NVIDIA Tesla P40
24 GB VRAM • 347 GB/s
NVIDIA
NVIDIA RTX A5000
24 GB VRAM • 768 GB/s
NVIDIA
$2500
NVIDIA L40 CNX
24 GB VRAM • 864 GB/s
NVIDIA
$5000

Find the best GPU for Granite 4.2 30B

Build Hardware for Granite 4.2 30B
▸ SPEC SHEET

Granite 4.2 30B — 29.28B Dense.

▸ SPECIFICATIONS
PARAMETERS
29.28B
ARCHITECTURE
Dense Transformer
CONTEXT LENGTH
128K tokens
CAPABILITIES
chat, coding, reasoning, multilingual, math, tool_use, agentic
RELEASE DATE
2026-08-25
PROVIDER
IBM
FAMILY
granite
▸ VRAM REQUIREMENTS
QUANTBPWVRAMQUALITY
Q3_K_M415.1 GB88%
Q3_K_L4.316.2 GB90%
IQ4_XS4.4616.8 GB92%
Q4_K_S4.6717.6 GB93%
Q4_K_M4.8918.4 GB94%
Q5_K_S5.5720.9 GB96%
Q5_K_M5.721.4 GB96%
Q6_K6.5624.5 GB97%
Q8_08.531.6 GB100%
FP161659.0 GB100%
§ 01BENCHMARK SCORES
MMLU-PRO77.6
GPQA Diamond64.4
LiveCodeBench75.8
AIME89.2
HLE11.2
aa_scicode37.8
aa_lcr49.0
SWE-bench57.0
aa_terminal_bench_v226.6
aa_terminal_bench_v40.0
§ 02RUN COMMAND

Run Granite 4.2 30B locally with Ollama — needs 18.4 GB VRAM at Q4_K_M:

$ollama run granite4.2:30b
§ 03COMPATIBLE GPUs
30 @ Q4_K_M