All models › ibm-granite
granite-4.1-3b
ibm-granite · open license · on Arena since 6 Apr 2026
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Arena score—Not voted on Arena yet
Size3.4 Bparameters
4-bit download2.1 GBweights only, Q4_K_M
Max context128Ktokens, from the model config
Where to use it for free
- Download and run it at home: official weights · GGUF files for Ollama and LM Studio
No Arena votes yet
Arena has not voted on this model. It is listed because it is a popular small model on its lab's official Hugging Face page (648K downloads a month).
Speed on popular hardware
| Hardware | GPU memory | Fits? | Speed |
|---|---|---|---|
| RTX 3060 12 GB | 12 GB | Fits in GPU | 53 tok/s |
| RTX 4060 Ti 16 GB | 16 GB | Fits in GPU | 42 tok/s |
| RTX 3090 | 24 GB | Fits in GPU | 138 tok/s |
| RTX 4090 | 24 GB | Fits in GPU | 149 tok/s |
| RTX 5090 | 32 GB | Fits in GPU | 264 tok/s |
| RX 7900 XTX | 24 GB | Fits in GPU | 142 tok/s |
| 2 × RTX 3090 | 48 GB | Fits in GPU | 134 tok/s |
| 4 × RTX 3090 | 96 GB | Fits in GPU | 127 tok/s |
| Mac M4 Max 128 GB | 96 GB | Fits in GPU | 81 tok/s |
| Ryzen AI Max+ 395 128 GB | 96 GB | Fits in GPU | 38 tok/s |
| 8 × H100 SXM | 640 GB | Fits in GPU | 408 tok/s |
Estimate at 4-bit with 32K context, 64 GB of DDR5 for offload. Speed ≈ memory bandwidth × 70% ÷ data read per token; real numbers vary by about ±30%.
GPU memory needed
| Level | 8K context | 32K context | 128K context | Quality vs full |
|---|---|---|---|---|
| Q3_K_M | 3.0 GB | 5.0 GB | 13 GB | +10.5% noticeable loss |
| Q4_K_M | 3.3 GB | 5.3 GB | 13 GB | +2.8% small loss |
| Q5_K_M | 3.7 GB | 5.7 GB | 14 GB | +0.9% very small loss |
| Q6_K | 4.1 GB | 6.1 GB | 14 GB | +0.3% near-lossless |
| Q8_0 | 4.9 GB | 6.9 GB | 15 GB | ≈ 0% same as full |
| F16 | 8.1 GB | 10 GB | 18 GB | ≈ 0% same as full |
Weights + context cache (from the model's architecture) + runtime. Quality: increase in perplexity against the full model, from llama.cpp tests, scaled by model size. Simulate your own setup.