Granite 4.2 30B GGUF
Certified GGUF precision quantization ladder for ibm-granite/granite-4.2-30b dense enterprise architecture.
Certified GGUF precision variants (Q8_0, Q4_K_M, Q2_K) of ibm-granite/granite-4.2-30b evaluated across standard reasoning, mathematics, and science benchmarks (ARC-MC, GSM8K, MATH-500) under reproducible seed controls.
Architecture
- Parameters
- 29.30B
- Active
- 29.30B (100%)
- Layers
- 64
- Quantization ladder
- Certified GGUF
Downloads
Exact release files
3 immutable artifacts
granite-4.2-30b.q4_k_m.gguf
Size: 18 GB
20–24 GB reference capacity. Balanced agentic performance and high reasoning retention.
SHA-256 Checksum
cd7fc4faec0920df60054703092cec50051286a329b61214bc174ec58699482d
granite-4.2-30b.q8_0.gguf
Size: 31 GB
32–48 GB reference capacity. Near-lossless precision for production and critical reasoning.
SHA-256 Checksum
5b14fe1d83c20af0ae3d98c2f4b68570cecafbf401bc39064b19def20a4e5d9f
granite-4.2-30b.q2_k.gguf
Size: 11 GB
12–16 GB reference capacity. Memory-constrained consumer workstations.
SHA-256 Checksum
db24b9204e916b7ad18a5fbce0b463c22f9d10e3e391c608867b7f6f4f2388ed
Deployment
Quickstart & CLI
llama.cpp & Ollama
Stream weights directly from Hugging Face via Ollama CLI:
ollama run hf.co/Omega-Factory/granite-4.2-30b-GGUF:Q4_K_MRun inference locally using llama.cpp with the recommended artifact:
llama-cli -hf Omega-Factory/granite-4.2-30b-GGUF -m granite-4.2-30b.q4_k_m.gguf -p "Explain how grouped-query attention and low-bit quantization reduce inference memory bandwidth." -n 512Launch a local OpenAI-compatible HTTP server:
llama-server -hf Omega-Factory/granite-4.2-30b-GGUF -m granite-4.2-30b.q4_k_m.gguf --port 8080 -ngl 99Evidence
Measured outcomes & precision ladder
Precision & Accuracy Matrix vs. BF16 Baseline
Holdout evaluation split (N=200 per category, seed 42) comparing all quant variants to unquantized BF16 baseline.
ARC-MC
92.0%
GSM8K
94.0%
MATH-500
54.5%
ARC-MC
92.0%
GSM8K
94.0%
MATH-500
54.5%
ARC-MC
91.0%
-1.0 pp
GSM8K
92.5%
-1.5 pp
MATH-500
54.5%
ARC-MC
86.5%
-5.5 pp
GSM8K
68.0%
-26.0 pp
MATH-500
38.0%
-16.5 pp
Item-Level McNemar Discordance & Flips vs. BF16 Baseline
Per-item analysis measuring individual test questions that flipped answer status relative to the unquantized BF16 baseline (N=200).
Recommended Release Rung Scoped Verification (Q4_K_M)
Formal cryptographic release marker evaluation records with 95% confidence intervals and sample counts.
ARC-Challenge Science Reasoning
granite-4.2-30b-arc-mc-v1 · accuracy_pct
Base
92.00%
Release
91.00%
Delta
-1.00 pp
Holdout evaluation set (N=200, seed 42) comparing Q4_K_M quantized release against uncompressed BF16 baseline under standard multiple-choice log-likelihood scoring.
95% CI [-3.00, 0.50] pp · 200 samples · 1 seeds · artifact cd7fc4faec0920df60054703092cec50051286a329b61214bc174ec58699482d
GSM8K Grade School Math
granite-4.2-30b-gsm8k-v1 · accuracy_pct
Base
94.00%
Release
92.50%
Delta
-1.50 pp
Holdout evaluation set (N=200, seed 42) comparing Q4_K_M quantized release against uncompressed BF16 baseline under greedy decoding.
95% CI [-5.00, 2.00] pp · 200 samples · 1 seeds · artifact cd7fc4faec0920df60054703092cec50051286a329b61214bc174ec58699482d
MATH-500 Advanced Mathematics
granite-4.2-30b-math500-v1 · accuracy_pct
Base
54.50%
Release
54.50%
Delta
+0.00 pp
Holdout evaluation set (N=200, seed 42) comparing Q4_K_M quantized release against uncompressed BF16 baseline under greedy decoding.
95% CI [-5.50, 5.50] pp · 200 samples · 1 seeds · artifact cd7fc4faec0920df60054703092cec50051286a329b61214bc174ec58699482d
Literature & Architecture
Foundational Research & Citations
Primary scientific publications & architecture preprints
Granite Code Models: A Family of Open Foundation Models for Code Intelligence
IBM Research Granite Team
Introduces IBM Granite decoder-only foundation models trained on 116 programming languages with rigorous data governance, enterprise safety compliance, and tool calling.
Which Quantization Should I Use? A Unified Evaluation of llama.cpp Quantization
Empirical GGUF Research Study
Comprehensive empirical analysis of llama.cpp GGUF quantization schemes (Q8_0, Q4_K_M, Q2_K) across reasoning benchmarks, memory footprints, and CPU/GPU throughput.
Limitations
- DENSE_ARCHITECTURE — Dense Transformer with Grouped-Query Attention (32 Q heads, 8 KV heads) and native context window of 131,072 tokens.
- VRAM_ESTIMATE_NOTICE — Artifact size is disk usage, not a measured VRAM requirement. Deployment requires peak-memory profiling.
- COMPRESSED_DERIVATIVE — Quantized precision ladder of ibm-granite/granite-4.2-30b under Apache 2.0 license.
Public release identity
- Release ID
- granite-4.2-30b-gguf-v1
- Release version
- 1
- Published
- 2026-10-13T00:51:00.000Z
- Model ID
- Omega-Factory/granite-4.2-30b-GGUF
- Base model ID
- ibm-granite/granite-4.2-30b
- Catalog family
- Not supplied
- Quantization rung
- Not supplied
- Supersedes
- Not supplied
- HF repository
- Omega-Factory/granite-4.2-30b-GGUF
- HF repository URL
- https://huggingface.co/Omega-Factory/granite-4.2-30b-GGUF
- HF revision
- a6100f66fafe84b052ed1efabf6345664516ea90
- HF revision URL
- https://huggingface.co/Omega-Factory/granite-4.2-30b-GGUF/tree/a6100f66fafe84b052ed1efabf6345664516ea90
- Release manifest SHA-256
- bdf94d6a1d5ad0127646f05b94c780479780ec497beca403c9c4c4583a706f1e
- Catalog SHA-256
- d853e89023b5b0378c96b28cf519639e2b0bb9b774d171f1fe1e0dbcc48b68f8
- Catalog generated
- 2026-10-14T08:00:00Z
- License
- Apache 2.0
- License ID
- apache-2.0
- Commercial use
- ALLOWED
- License URL
- https://www.apache.org/licenses/LICENSE-2.0