Granite · IBM
Fine-tuning Granite 3.0 8B
What does it take to fine-tune Granite 3.0 8B?
Granite 3.0 8B needs 18 GB for half-precision LoRA training and 14 GB in four-bit, and 16 GB to serve. It supports supervised fine-tuning, preference tuning (dpo), under the Apache 2.0 licence. The cheapest qualifying class is RTX 4000 Ada at $0.09 per GPU-hour.
The 8B in the Granite 3.0 line, with the same enterprise orientation as the 2B and enough capacity to hold real domain knowledge. Apache-2.0, documented training data, and behaviour tuned to decline rather than improvise when it does not know.
What to know before choosing it
The same enterprise orientation as the 2B with enough capacity to hold real domain knowledge. Apache-2.0, documented training data, and a disposition toward refusing rather than guessing — the combination is what makes it a common choice for internal question answering over proprietary documents.
Against Qwen3 8B or Llama 3.1 8B on general benchmarks it will usually lose. On a retrieval-grounded task with a refusal requirement it frequently wins, and that is the comparison worth running through the gate.
Specification
| Parameters | 8B |
|---|---|
| Architecture | Dense |
| Size tier | Mid |
| LoRA training memory | 18 GBHalf precision, frozen base |
| QLoRA training memory | 14 GBFour-bit base, higher-precision adapter |
| Serving memory | 16 GBHalf precision, before attention cache |
| Objectives | SFT, DPO |
| Licence | Apache 2.0 |
| Repository | ibm-granite/granite-3.0-8b-instruct |
What it costs to train
Every class with enough memory for four-bit training, cheapest first. Total cost is the rate multiplied by wall time, so the cheapest rate is not always the cheapest run — a faster class that finishes sooner frequently wins.
| GPU class | VRAM | Training / hr | Serving / hr | Fits |
|---|---|---|---|---|
| RTX 4000 Ada | 20 GB | $0.09 | $0.11 | LoRA and QLoRA |
| L4 | 24 GB | $0.17 | $0.20 | LoRA and QLoRA |
| RTX 3090 | 24 GB | $0.22 | $0.26 | LoRA and QLoRA |
| RTX 4090 | 24 GB | $0.38 | $0.43 | LoRA and QLoRA |
| A40 | 48 GB | $0.42 | $0.48 | LoRA and QLoRA |
| RTX 6000 Ada | 48 GB | $0.61 | $0.71 | LoRA and QLoRA |
| A6000 | 48 GB | $0.65 | $0.75 | LoRA and QLoRA |
| L40S | 48 GB | $0.78 | $0.90 | LoRA and QLoRA |
| A100 40 GB | 40 GB | $1.17 | $1.35 | LoRA and QLoRA |
| A100 80 GB | 80 GB | $1.68 | $1.94 | LoRA and QLoRA |
| H100 80 GB | 80 GB | $2.59 | $2.99 | LoRA and QLoRA |
| H200 | 141 GB | $4.55 | $5.25 | LoRA and QLoRA |
Serving fits on RTX 4000 Ada at $0.11 per hour — before the attention cache, which grows with context length and concurrency.
Good starting point for
- Enterprise question answering
- Regulated deployments
- Tool-calling agents needing predictable refusals
Preference tuning on this model
Preference tuning holds a frozen reference copy of the model alongside the one being trained, so budget roughly 36 GB rather than 18 GB. That is the thing that catches people out — supervised training on this model fits on a class that preference tuning will overflow.
Other sizes in this family
| Model | Params | QLoRA | Objectives |
|---|---|---|---|
| Granite 3.0 2B | 2B | 5 GB | SFT, DPO |
| Granite 4.1 8B | 8B | 14 GB | SFT, DPO |
Comparable sizes elsewhere
Frequently asked questions
How much VRAM does it take to fine-tune Granite 3.0 8B?
18 GB for half-precision LoRA and 14 GB for four-bit QLoRA. Serving needs 16 GB. Preference tuning roughly doubles the training figure, because a frozen reference model is held alongside the one being trained.
What is the cheapest way to fine-tune Granite 3.0 8B?
Four-bit QLoRA on RTX 4000 Ada at $0.09 per GPU-hour is the cheapest class that meets the 14 GB threshold. Note that quantised training is slower per step, so a faster class sometimes costs less over the whole run.
Can I download the weights after fine-tuning Granite 3.0 8B?
Yes. Every finished run exposes its trained weights for download, and publishing to a model hub is a single call with a generated model card recording the base model and version the adapter applies to.
What licence does Granite 3.0 8B carry?
Apache 2.0. The licence follows the fine-tune — a derivative inherits the base model’s terms, and those terms pass to anyone you give the model to.
Last verified 6 August 2026. Memory thresholds are the platform's own admission limits.
Fine-tune Granite 3.0 8B
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