Atom2.7m — Hardware Requirements & GPU Compatibility
Chat
Atom2.7m is a 3M-parameter open language model from UniversalComputingResearch. It supports a context window of up to 512 tokens. At BF16 it needs about 0.31 GB of VRAM — see which GPUs and Macs can run it below.
1.9K downloads
16 likesSpecifications
- Publisher
- UniversalComputingResearch
- Parameters
- 3M
- Architecture
- GPTForCausalLM
- Context Length
- 512 tokens
- Vocabulary Size
- 4,096
- Release Date
- 2026-06-30
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Atom2.7m Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 0.3 GB | — | 0.01 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 Atom2.7m?
BF16 · 0.3 GBAtom2.7m (BF16) requires 0.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 1+ GB is recommended. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Atom2.7m?
BF16 · 0.3 GB59 devices with unified memory can run Atom2.7m, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomFrequently Asked Questions
- How much VRAM does Atom2.7m need?
Atom2.7m requires 0.3 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 3M × 16 bits ÷ 8 = 0 GB
KV Cache + Overhead ≈ 0.3 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
BF160.3 GB- Can I run Atom2.7m on a Mac?
Atom2.7m requires at least 0.3 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 Atom2.7m locally?
Yes — Atom2.7m can run locally on consumer hardware. At BF16 quantization it needs 0.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Atom2.7m?
At BF16, Atom2.7m can reach ~14194 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~2114 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 ÷ 0.3 × 0.65 = ~16774 tok/s
Estimated speed at BF16 (0.3 GB)
~16774 tok/s~2114 tok/s~16774 tok/s~14194 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Atom2.7m?
At BF16, the download is about 0.01 GB.
- Which GPUs can run Atom2.7m?
50 consumer GPUs can run Atom2.7m at BF16 (0.3 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 50 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Atom2.7m?
59 devices with unified memory can run Atom2.7m at BF16 (0.3 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.