Apertus V1.5 8B — Hardware Requirements & GPU Compatibility
Vision
Apertus V1.5 8B is an 8.9-billion-parameter multimodal model from the Swiss AI Initiative, a collaboration between ETH Zurich, EPFL, and the Swiss National Supercomputing Centre. Extended from the text-only Apertus 1 through continued pretraining, it accepts images alongside text and can process spoken audio experimentally, letting it reason about visual content in a chat setting. Its compact size suits local deployment on a single consumer GPU once quantized. It is released under the Apache 2.0 license, allowing unrestricted commercial and research use, alongside a public acceptable-use policy governing deployment. Published in July 2026, Apertus V1.5 stands out for being fully open in a way few large models are: not just the weights but the training data, code, and development process are published, with an optional reasoning mode for harder prompts.
299.6K downloads
106 likesSpecifications
- Publisher
- swiss-ai
- Family
- Apertus
- Parameters
- 8.9B
- Release Date
- 2026-07-24
- License
- Apache 2.0
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HuggingFace
How Much VRAM Does Apertus V1.5 8B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 4.2 GB | — | 3.78 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 4.8 GB | — | 4.34 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 5.9 GB | — | 5.34 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 7.0 GB | — | 6.34 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 8.1 GB | — | 7.35 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 9.8 GB | — | 8.90 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 19.6 GB | — | 17.81 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 Apertus V1.5 8B?
Q4_K_M · 5.9 GBApertus V1.5 8B (Q4_K_M) requires 5.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 8+ GB is recommended. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Apertus V1.5 8B?
Q4_K_M · 5.9 GB58 devices with unified memory can run Apertus V1.5 8B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Apertus V1.5 8B need?
Apertus V1.5 8B requires 5.9 GB of VRAM at Q4_K_M, or 19.6 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 8.9B × 4.8 bits ÷ 8 = 5.3 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M5.9 GB- What's the best quantization for Apertus V1.5 8B?
For Apertus V1.5 8B, Q4_K_M (5.9 GB) offers the best balance of quality and VRAM usage. Q5_K_M (7.0 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 4.2 GB.
VRAM requirement by quantization
Q2_K4.2 GBQ4_K_M ★5.9 GBQ5_K_M7.0 GBQ6_K8.1 GBQ8_09.8 GBBF1619.6 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Apertus V1.5 8B on a Mac?
Apertus V1.5 8B requires at least 4.2 GB at Q2_K, 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 V1.5 8B locally?
Yes — Apertus V1.5 8B can run locally on consumer hardware. At Q4_K_M quantization it needs 5.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Apertus V1.5 8B?
At Q4_K_M, Apertus V1.5 8B can reach ~816 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~111 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 ÷ 5.9 × 0.65 = ~884 tok/s
Estimated speed at Q4_K_M (5.9 GB)
~884 tok/s~111 tok/s~884 tok/s~816 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Apertus V1.5 8B?
At Q4_K_M, the download is about 5.34 GB. The full-precision BF16 version is 17.81 GB. The smallest option (Q2_K) is 3.78 GB.
- Which GPUs can run Apertus V1.5 8B?
52 consumer GPUs can run Apertus V1.5 8B at Q4_K_M (5.9 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 40 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Apertus V1.5 8B?
59 devices with unified memory can run Apertus V1.5 8B at Q4_K_M (5.9 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.