GPT X2 125M — Hardware Requirements & GPU Compatibility
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GPT X2 125M is a 144M-parameter open language model from AxiomicLabs. It supports a context window of up to 1,024 tokens. At BF16 it needs about 0.64 GB of VRAM — see which GPUs and Macs can run it below.
979 downloads
23 likesSpecifications
- Publisher
- AxiomicLabs
- Parameters
- 144M
- Architecture
- GPTX2ForCausalLM
- Context Length
- 1,024 tokens
- Vocabulary Size
- 32,768
- Release Date
- 2026-03-25
- License
- Apache 2.0
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HuggingFace
How Much VRAM Does GPT X2 125M Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 0.6 GB | — | 0.29 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 GPT X2 125M?
BF16 · 0.6 GBGPT X2 125M (BF16) requires 0.6 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 GPT X2 125M?
BF16 · 0.6 GB59 devices with unified memory can run GPT X2 125M, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does GPT X2 125M need?
GPT X2 125M requires 0.6 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 144M × 16 bits ÷ 8 = 0.3 GB
KV Cache + Overhead ≈ 0.3 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
BF160.6 GB- Can I run GPT X2 125M on a Mac?
GPT X2 125M requires at least 0.6 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 GPT X2 125M locally?
Yes — GPT X2 125M can run locally on consumer hardware. At BF16 quantization it needs 0.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is GPT X2 125M?
At BF16, GPT X2 125M can reach ~6875 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~1024 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 ÷ 0.6 × 0.65 = ~8125 tok/s
Estimated speed at BF16 (0.6 GB)
~8125 tok/s~1024 tok/s~8125 tok/s~6875 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of GPT X2 125M?
At BF16, the download is about 0.29 GB.
- Which GPUs can run GPT X2 125M?
50 consumer GPUs can run GPT X2 125M at BF16 (0.6 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 GPT X2 125M?
59 devices with unified memory can run GPT X2 125M at BF16 (0.6 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.