Fanar 1 9B Instruct — Hardware Requirements & GPU Compatibility
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Fanar 1 9B Instruct is a 8.8B-parameter open language model from QCRI. It supports a context window of up to 4,096 tokens. At BF16 it needs about 18.48 GB of VRAM — see which GPUs and Macs can run it below.
3.9K downloads
33 likesSpecifications
- Publisher
- QCRI
- Parameters
- 8.8B
- Architecture
- Gemma2ForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2025-07-15
- License
- Apache 2.0
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HuggingFace
How Much VRAM Does Fanar 1 9B Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 18.5 GB | 19.1 GB | 17.57 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Fanar 1 9B Instruct?
BF16 · 18.5 GBFanar 1 9B Instruct (BF16) requires 18.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 25+ GB is recommended. Using the full 4K context window can add up to 0.6 GB, bringing total usage to 19.1 GB. 6 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run Fanar 1 9B Instruct?
BF16 · 18.5 GB21 devices with unified memory can run Fanar 1 9B Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Fanar 1 9B Instruct need?
Fanar 1 9B Instruct requires 18.5 GB of VRAM at BF16. Full 4K context adds up to 0.6 GB (19.1 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 8.8B × 16 bits ÷ 8 = 17.6 GB
KV Cache + Overhead ≈ 0.9 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 1.5 GB (at full 4K context)
VRAM usage by quantization
BF1618.5 GBBF16 + full context19.1 GB- Can I run Fanar 1 9B Instruct on a Mac?
Fanar 1 9B Instruct requires at least 18.5 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 Fanar 1 9B Instruct locally?
Yes — Fanar 1 9B Instruct can run locally on consumer hardware. At BF16 quantization it needs 18.5 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Fanar 1 9B Instruct?
At BF16, Fanar 1 9B Instruct can reach ~158 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~36 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 18.5 × 0.55 = ~158 tok/s
Estimated speed at BF16 (18.5 GB)
~158 tok/s~36 tok/s~118 tok/s~98 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Fanar 1 9B Instruct?
At BF16, the download is about 17.57 GB.
- Which GPUs can run Fanar 1 9B Instruct?
6 consumer GPUs can run Fanar 1 9B Instruct at BF16 (18.5 GB). Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XT, AMD Radeon RX 7900 XTX. 1 GPU have plenty of headroom for comfortable inference.
- Which devices can run Fanar 1 9B Instruct?
21 devices with unified memory can run Fanar 1 9B Instruct at BF16 (18.5 GB), including Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.