Best Granite 4.2 8B quant for a 24GB GPU
Short answer: Q8_0 from bartowski . Of the 8 Granite 4.2 8B files we tested that fit fully in 24GB with a 8192-token context, it has the smallest diff from the BF16 model (mean KLD 0.0082, same top token 97.8%).
- File size
- 8.70 GB
- KLD (mean)
- 0.0082
- Same top token
- 97.8%
- Tool calls
- 61.0%
- Generation
- 65 t/s full GPU
- File size
- 7.00 GB
- KLD (mean)
- 0.0136
- Same top token
- 96.2%
- Tool calls
- 60.5%
- Generation
- 78 t/s full GPU
- File size
- 3.33 GB
- KLD (mean)
- 0.4598
- Same top token
- 75.0%
- Tool calls
- 61.0%
- Generation
- 146 t/s full GPU
Other budgets: 8GB12GB16GB ยท all 8 Granite 4.2 8B quants
How we picked
We estimate VRAM need as file size + f16 KV cache for 8192 tokens + about 0.6GB of compute buffers, and keep 0.5GB free for your desktop. Among files that fit, we rank by mean KL divergence against the BF16 model's logits, then by tool-call score. If you need a longer context, step down one quant level or quantize the KV cache (-ctk q8_0 -ctv q8_0). Methodology.
GPU too small? Rent one.
Run a 32B model at Q6/Q8, or a 70B, on a cloud GPU for an hour or two instead of buying hardware.
Cloud GPU pods with 24-80GB cards (RTX 4090 / 5090 / A100 / H100) for big quants or BF16 reference runs.
Rent on RunPodGPU marketplace for renting a 24-48GB card for an evening of testing.
Rent on Vast.aiReferral links: QuantDiff may earn credit if you sign up. Referral partners have no say in scores or rankings.