Evo2 7B — Hardware Requirements & GPU Compatibility
Chat
Evo2 7B is a 6.6B-parameter open language model from Aquiles-ai. It supports a context window of up to 1,048,576 tokens. At BF16 it needs about 14.48 GB of VRAM — see which GPUs and Macs can run it below.
1.2K downloads
2 likesSpecifications
- Publisher
- Aquiles-ai
- Parameters
- 6.6B
- Architecture
- Evo2ForCausalLM
- Context Length
- 1,048,576 tokens
- Vocabulary Size
- 512
- Release Date
- 2026-09-14
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Evo2 7B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 14.5 GB | — | 13.16 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 7B?
BF16 · 14.5 GBEvo2 7B (BF16) requires 14.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 19+ GB is recommended. 26 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 5080.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Evo2 7B?
BF16 · 14.5 GB47 devices with unified memory can run Evo2 7B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 (16 GB).
Runs great
— Plenty of headroomFrequently Asked Questions
- How much VRAM does Evo2 7B need?
Evo2 7B requires 14.5 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 6.6B × 16 bits ÷ 8 = 13.2 GB
KV Cache + Overhead ≈ 1.3 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
BF1614.5 GB- Can I run Evo2 7B on a Mac?
Evo2 7B requires at least 14.5 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 Evo2 7B locally?
Yes — Evo2 7B can run locally on consumer hardware. At BF16 quantization it needs 14.5 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Evo2 7B?
At BF16, Evo2 7B can reach ~332 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~45 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 ÷ 14.5 × 0.65 = ~359 tok/s
Estimated speed at BF16 (14.5 GB)
~359 tok/s~45 tok/s~359 tok/s~332 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Evo2 7B?
At BF16, the download is about 13.16 GB.
- Which GPUs can run Evo2 7B?
26 consumer GPUs can run Evo2 7B at BF16 (14.5 GB). Top options include AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090, NVIDIA GeForce RTX 3090 Ti, AMD Radeon RX 6800. 7 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Evo2 7B?
49 devices with unified memory can run Evo2 7B at BF16 (14.5 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (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.