What GPU do I need to run ibm-granite/granite-3.2-8b-instruct?
A 8.2B language model for chat and instruction-following. 8.2B parameters, published in BF16. View on Hugging Face
granite-3.2-8b-instruct is published by ibm-granite on Hugging Face, with 40,435 downloads and 92 likes to date. It's a GraniteForCausalLM model built for text-generation, published natively in BF16.
What granite-3.2-8b-instruct is
granite-3.2-8b-instruct is a 8.2B-parameter language model published by IBM on Hugging Face. It is released under Apache 2.0.
License note: fully permissive, no gating and no usage restrictions. Facts in this section are sourced from granite-3.2-8b-instruct's Hugging Face model card, not benchmarked by Aquanode.
What it's used for
- Chat assistants
- Instruction following
- Synthetic data generation
VRAM required & cheapest live GPU fit
Required VRAM = weight size at each precision times a flat 1.2 overhead for activations and fragmentation. The KV-cache grows with context and is not in that factor; it is listed per model below where the architecture is published. Full formula and assumptions: methodology.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 15.2 GB | 18.3 GB | RTX A5000 | 1 | $0.176/hr |
| FP8 (quantized) | 7.6 GB | 9.1 GB | RTX 4000 SFF Ada | 1 | $0.198/hr |
| INT4 (quantized) | 3.8 GB | 4.6 GB | RTX 3060 | 1 | $0.110/hr |
A GPU is only matched to a row if its hardware supports that precision, and the primary recommendation is always a single-GPU fit when one exists.
INT4 requires a quantized checkpoint actually published for this model, check its Hugging Face page before relying on this row.
Cheapest way to run granite-3.2-8b-instruct at its published (BF16) precision: 1× RTX A5000, at $0.176/hr per GPU ($0.176/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
granite-3.2-8b-instruct: KV cache by context length
The KV cache is the memory the attention layers hold for every token of context, on top of the weights. It is not part of the flat 1.2x overhead in the table above, grows with context length and with every concurrent request, and is why a long-context deployment needs more VRAM than the table shows.
Grouped-query attention: every layer caches keys and values for a few shared KV heads. Cached per token: 163,840 bytes at 16-bit.
| Context | KV cache, one sequence |
|---|---|
| 4K | 0.63 GB |
| 32K | 5.0 GB |
| 128K | 20.0 GB |
Computed from the layer, head and window counts in the model's own configuration (ibm-granite/granite-3.2-8b-instruct), at 16 bits per cached value. Each concurrent sequence needs its own cache, on top of the weights.
granite-3.2-8b-instruct: common questions
Does granite-3.2-8b-instruct fit on a 24 GB GPU?
Yes. At BF16 it needs 18.3 GB of VRAM, so a 24 GB card holds it with 5.7 GB to spare. A 16 GB card is not enough for it at BF16.
What is the least VRAM granite-3.2-8b-instruct can run in?
4.6 GB, at INT4 (quantized), which fits a 6 GB card, against 18.3 GB at BF16. That row assumes an INT4 checkpoint has actually been published for this model, so check its Hugging Face page before planning around it.
Does quantizing granite-3.2-8b-instruct lower the GPU bill?
Yes. At BF16 the cheapest live fit is one RTX A5000 at $0.176/hr. At INT4 (quantized) it drops to one RTX 3060 at $0.110/hr, provided a quantized checkpoint exists for it.
How to run granite-3.2-8b-instruct
Run granite-3.2-8b-instruct with vLLM
Generic example, not from the model's own docs: adjust flags (quantization, context length, parallelism) for your setup.
vllm serve ibm-granite/granite-3.2-8b-instruct --tensor-parallel-size 1Run granite-3.2-8b-instruct with Ollama
Verified against Ollama's own library listing.
ollama run granite3.2:8bRun granite-3.2-8b-instruct with GGUF quantizations
Prebuilt GGUF weights published at lmstudio-community/granite-3.2-8b-instruct-GGUF. Run with llama.cpp's llama-server or load the repo directly in LM Studio.
llama-server -hf lmstudio-community/granite-3.2-8b-instruct-GGUFSource: https://huggingface.co/lmstudio-community/granite-3.2-8b-instruct-GGUF
Deploy granite-3.2-8b-instruct on Aquanode
Aquanode has no one-click deploy template for granite-3.2-8b-instruct; you install the inference engine yourself with the commands below. Aquanode sells GPU pods billed per second, not a hosted inference API.
- Launch a bare GPU pod sized to the requirement above (1× RTX A5000 or larger).
- Open a terminal on the pod, or save one of the commands above as a startup script so it runs automatically the first time the pod boots.
- Run the command and connect to the resulting endpoint.
Launch a GPU podWeight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More Granite 3 models
- granite-3.0-8b-instruct (8.2B, BF16)
- granite-3.1-8b-instruct (8.2B, BF16)
- granite-guardian-3.3-8b (8.2B, BF16)
- granite-3.3-8b-instruct (8.2B, BF16)
- granite-3.3-2b-instruct (2.5B, BF16)
Lineage
Based on granite-3.1-8b-instruct.
Quantized and fine-tuned versions
Alternatives at this size
Other models for text-generation within about a third of granite-3.2-8b-instruct's 8.2B parameters, from other model lines.
- Qwen3-8B (8.2B, BF16)
- Llama-3.1-8B-Instruct (8.0B, BF16)
- Mistral-7B-Instruct-v0.2 (7.2B, BF16)
- DeepSeek-R1-0528-Qwen3-8B (8.2B, BF16)
- gemma-2-9b-it (9.2B, BF16)
More on granite-3.2-8b-instruct
Fits on a 24 GB GPU at BF16: every model that fits in 24 GB.
Fits on a 12 GB GPU at FP8: every model that fits in 12 GB.
Fits on an 8 GB GPU at INT4: every model that fits in 8 GB.
Best chat and assistants models: how granite-3.2-8b-instruct ranks against the rest.
Related reading: RTX A5000 pricing and specs, How much VRAM you need for LLMs, Serving LLMs with vLLM, vLLM vs TensorRT-LLM vs SGLang, and Best GPU for LLM inference.
