Ilama 3.2 1B — Hardware Requirements & GPU Compatibility
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Ilama 3.2 1B is a 1.2B-parameter open language model from hmellor. It supports a context window of up to 131,072 tokens. At BF16 it needs about 2.84 GB of VRAM — see which GPUs and Macs can run it below.
117.7K downloadsSpecifications
- Publisher
- hmellor
- Parameters
- 1.2B
- Architecture
- IlamaForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2025-07-22
Get Started
HuggingFace
How Much VRAM Does Ilama 3.2 1B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 2.8 GB | 7.1 GB | 2.47 GB | Brain floating point 16 — preferred for training |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Ilama 3.2 1B?
BF16 · 2.8 GBIlama 3.2 1B (BF16) requires 2.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 4+ GB is recommended. Using the full 131K context window can add up to 4.2 GB, bringing total usage to 7.1 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Ilama 3.2 1B?
BF16 · 2.8 GB59 devices with unified memory can run Ilama 3.2 1B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Ilama 3.2 1B need?
Ilama 3.2 1B requires 2.8 GB of VRAM at BF16. Full 131K context adds up to 4.2 GB (7.1 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 1.2B × 16 bits ÷ 8 = 2.5 GB
KV Cache + Overhead ≈ 0.3 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 4.6 GB (at full 131K context)
VRAM usage by quantization
BF162.8 GBBF16 + full context7.1 GB- Can I run Ilama 3.2 1B on a Mac?
Ilama 3.2 1B requires at least 2.8 GB at BF16, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Ilama 3.2 1B locally?
Yes — Ilama 3.2 1B can run locally on consumer hardware. At BF16 quantization it needs 2.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Ilama 3.2 1B?
At BF16, Ilama 3.2 1B can reach ~1690 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~231 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 2.8 × 0.65 = ~1831 tok/s
Estimated speed at BF16 (2.8 GB)
~1831 tok/s~231 tok/s~1831 tok/s~1690 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Ilama 3.2 1B?
At BF16, the download is about 2.47 GB.
- Which GPUs can run Ilama 3.2 1B?
52 consumer GPUs can run Ilama 3.2 1B at BF16 (2.8 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Ilama 3.2 1B?
59 devices with unified memory can run Ilama 3.2 1B at BF16 (2.8 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.