Apertus 8B Instruct 2509 — Hardware Requirements & GPU Compatibility
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Apertus 8B Instruct is an open-source instruction-tuned model from Swiss AI, a collaborative research initiative. Built on an 8 billion parameter base, it emphasizes transparency, open data, and European AI sovereignty. For local users, it delivers solid general-purpose chat and instruction-following in a standard 8B footprint that runs well on consumer GPUs with 8 to 10 GB of VRAM, making it a practical choice for those who value open, community-driven model development.
117.9K downloads
439 likesSpecifications
- Publisher
- swiss-ai
- Parameters
- 8B
- Architecture
- ApertusForCausalLM
- Context Length
- 65,536 tokens
- Vocabulary Size
- 131,072
- Release Date
- 2025-11-14
- License
- Apache 2.0
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HuggingFace
How Much VRAM Does Apertus 8B Instruct 2509 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 16.6 GB | 24.9 GB | 16.00 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Apertus 8B Instruct 2509?
BF16 · 16.6 GBApertus 8B Instruct 2509 (BF16) requires 16.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 22+ GB is recommended. Using the full 66K context window can add up to 8.3 GB, bringing total usage to 24.9 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 Apertus 8B Instruct 2509?
BF16 · 16.6 GB21 devices with unified memory can run Apertus 8B Instruct 2509, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomRelated Models
Derivatives (4)
Frequently Asked Questions
- How much VRAM does Apertus 8B Instruct 2509 need?
Apertus 8B Instruct 2509 requires 16.6 GB of VRAM at BF16. Full 66K context adds up to 8.3 GB (24.9 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 8B × 16 bits ÷ 8 = 16 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 8.9 GB (at full 66K context)
VRAM usage by quantization
BF1616.6 GBBF16 + full context24.9 GB- Can I run Apertus 8B Instruct 2509 on a Mac?
Apertus 8B Instruct 2509 requires at least 16.6 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 Apertus 8B Instruct 2509 locally?
Yes — Apertus 8B Instruct 2509 can run locally on consumer hardware. At BF16 quantization it needs 16.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Apertus 8B Instruct 2509?
At BF16, Apertus 8B Instruct 2509 can reach ~176 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~40 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 ÷ 16.6 × 0.55 = ~176 tok/s
Estimated speed at BF16 (16.6 GB)
~176 tok/s~40 tok/s~132 tok/s~109 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Apertus 8B Instruct 2509?
At BF16, the download is about 16.00 GB.
- Which GPUs can run Apertus 8B Instruct 2509?
6 consumer GPUs can run Apertus 8B Instruct 2509 at BF16 (16.6 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 Apertus 8B Instruct 2509?
21 devices with unified memory can run Apertus 8B Instruct 2509 at BF16 (16.6 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.