Evo2 1B Base — Hardware Requirements & GPU Compatibility
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Evo2 1B Base is a 1.1B-parameter open language model from Aquiles-ai. It supports a context window of up to 8,192 tokens. At BF16 it needs about 2.44 GB of VRAM — see which GPUs and Macs can run it below.
1.6K downloads
2 likesSpecifications
- Publisher
- Aquiles-ai
- Parameters
- 1.1B
- Architecture
- Evo2ForCausalLM
- Context Length
- 8,192 tokens
- Vocabulary Size
- 512
- Release Date
- 2026-09-14
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Evo2 1B Base Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 2.4 GB | — | 2.22 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 Evo2 1B Base?
BF16 · 2.4 GBEvo2 1B Base (BF16) requires 2.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 4+ GB is recommended. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Evo2 1B Base?
BF16 · 2.4 GB59 devices with unified memory can run Evo2 1B Base, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Evo2 1B Base need?
Evo2 1B Base requires 2.4 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 1.1B × 16 bits ÷ 8 = 2.2 GB
KV Cache + Overhead ≈ 0.2 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
BF162.4 GB- Can I run Evo2 1B Base on a Mac?
Evo2 1B Base requires at least 2.4 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 Evo2 1B Base locally?
Yes — Evo2 1B Base can run locally on consumer hardware. At BF16 quantization it needs 2.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Evo2 1B Base?
At BF16, Evo2 1B Base can reach ~1967 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~269 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 ÷ 2.4 × 0.65 = ~2131 tok/s
Estimated speed at BF16 (2.4 GB)
~2131 tok/s~269 tok/s~2131 tok/s~1967 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Evo2 1B Base?
At BF16, the download is about 2.22 GB.
- Which GPUs can run Evo2 1B Base?
52 consumer GPUs can run Evo2 1B Base at BF16 (2.4 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 Evo2 1B Base?
59 devices with unified memory can run Evo2 1B Base at BF16 (2.4 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.