FitMyLLM
IBM/Dense

IBMGranite 4.2 8B

Granite 4.2 8B — the mid-size Granite 4.2 model: dense, with thinking on by default and switchable per request, tuned for tool calling and agentic work. Apache 2.0.

chatcodingreasoningmultilingualmathtool_useagentic
8.79B
Parameters
128K
Context length
10
Benchmarks
6
Quantizations
71K
HF downloads
Architecture
Dense
Released
2026-08-25
Layers
40
KV Heads
8
Head Dim
128
Family
granite

Quantization Options

Context length:
QuantBitsVRAM @ 16KQuality
Q4_K_M4.89
7.7 GB
5.9 + 1.9 KV
good
Q5_K_S5.57
8.5 GB
6.6 + 1.9 KV
good
Q5_K_M5.7
8.6 GB
6.8 + 1.9 KV
good
Q6_K6.56
9.6 GB
7.7 + 1.9 KV
excellent
Q8_08.5
11.7 GB
9.8 + 1.9 KV
lossless
FP1616
19.9 GB
18.1 + 1.9 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 8B NOW

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

Community Ratings

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Benchmarks (10)

AIME86.7
MMLU-PRO74.0
LiveCodeBench73.2
GPQA Diamond63.1
SWE-bench47.7
AA Long Context45.0
SciCode31.5
Terminal-Bench 2.118.4
HLE9.7
Terminal-Bench 40.0

Run this model

▸Easiest way to get started·Beginners
DOCS ↗
curl -fsSL https://ollama.com/install.sh | sh
$ollama run granite4.2:8b-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 Tesla C2070
6 GB VRAM • 143 GB/s
NVIDIA
NVIDIA Tesla C2075
6 GB VRAM • 150 GB/s
NVIDIA
NVIDIA Tesla C2090
6 GB VRAM • 177 GB/s
NVIDIA
NVIDIA Tesla M2070
6 GB VRAM • 150 GB/s
NVIDIA
NVIDIA Tesla M2070-Q
6 GB VRAM • 150 GB/s
NVIDIA
NVIDIA Tesla M2075
6 GB VRAM • 150 GB/s
NVIDIA
NVIDIA Tesla M2090
6 GB VRAM • 177 GB/s
NVIDIA
NVIDIA Tesla X2070
6 GB VRAM • 177 GB/s
NVIDIA
NVIDIA Tesla X2090
6 GB VRAM • 177 GB/s
NVIDIA
NVIDIA Tesla K20X
6 GB VRAM • 250 GB/s
NVIDIA
NVIDIA Tesla K20Xm
6 GB VRAM • 250 GB/s
NVIDIA

Find the best GPU for Granite 4.2 8B

Build Hardware for Granite 4.2 8B
▸ SPEC SHEET

Granite 4.2 8B — 8.79B Dense.

▸ SPECIFICATIONS
PARAMETERS
8.79B
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
Q4_K_M4.895.9 GB94%
Q5_K_S5.576.6 GB96%
Q5_K_M5.76.8 GB96%
Q6_K6.567.7 GB97%
Q8_08.59.8 GB100%
FP161618.1 GB100%
§ 01BENCHMARK SCORES
MMLU-PRO74.0
GPQA Diamond63.1
LiveCodeBench73.2
AIME86.7
HLE9.7
aa_scicode31.5
aa_lcr45.0
SWE-bench47.7
aa_terminal_bench_v218.4
aa_terminal_bench_v40.0
§ 02RUN COMMAND

Run Granite 4.2 8B locally with Ollama — needs 5.9 GB VRAM at Q4_K_M:

$ollama run granite4.2:8b
§ 03COMPATIBLE GPUs
30 @ Q4_K_M