GPT X2 125M CIx Long Context — Hardware Requirements & GPU Compatibility
Chat
GPT X2 125M CIx Long Context is a 126M-parameter open language model from reaperdoesntknow. It supports a context window of up to 32,768 tokens. At BF16 it needs about 0.60 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 8 GB (BF16 · 32K)Best on 24 GB (BF16 · ~1092 tok/s · 32K)Macs from 8 GB (BF16 · ~120 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run GPT X2 125M CIx Long Context
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve reaperdoesntknow/GPT-X2-125M-CIx-Long-Context --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (11 GB headroom) · BF16
- Generation speed
- ~390 tok/s
- generation speed
- Cost per 1M output tokens
- $0.059
- per 1M output tokens
How Much VRAM Does GPT X2 125M CIx Long Context Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 0.6 GB | 1.3 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 32K context · ~1092 tok/s |
Selected BF16
Brain floating point 160.9GBat 16K context
- Weights
- 0.3 GB
- KV cache
- 0.4 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 23.1 GB free · ~1092 tok/s
≥50 tok/s needs ≥46 GB/s · RTX 5090 ~1941, RTX 3090 Ti ~1092
Which Hardware Can Run GPT X2 125M CIx Long Context?
BF16 · 0.6 GBGPT X2 125M CIx Long Context (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. Using the full 33K context window can add up to 0.7 GB, bringing total usage to 1.3 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run GPT X2 125M CIx Long Context, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 7.1 GB free · up to 32K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~824Simulate output on RTX 3080RTX 2080 Ti~667Simulate output on RTX 2080 TiRTX 3070 Ti~659Simulate output on RTX 3070 TiM3*8 GB~120Simulate output on iMac 24" M3 (8 GB)Orin Nano~111Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~80Simulate output on iPhone 1720 GB
fits · 19.1 GB free · up to 32K ctx
1 GPU
RX 7900 XT~800Simulate output on RX 7900 XT32–40 GB
fits · 31.1 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~1941Simulate output on RTX 5090M5 Max36 GB~537Simulate output on Mac Studio M5 Max (36 GB)M532 GB~179Simulate output on MacBook Air 13" M5 (32 GB)Orin~222Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~146Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at BF16
Similar models
1| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| SmolLM 135M Instruct FP32modularai | — | 0.4 GB | ~1524 | same size |
Specifications
- Publisher
- reaperdoesntknow
- Parameters
- 126M
- Architecture
- GPTX2ForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 32,768
- Release Date
- 2026-05-24
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does GPT X2 125M CIx Long Context need?
GPT X2 125M CIx Long Context requires 0.6 GB of VRAM at BF16. Full 33K context adds up to 0.7 GB (1.3 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 126M × 16 bits ÷ 8 = 0.3 GB
KV Cache + Overhead ≈ 0.3 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 1 GB (at full 33K context)
VRAM usage by quantization
Can I run GPT X2 125M CIx Long Context on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run GPT X2 125M CIx Long Context. Apple Silicon uses unified memory, so the model shares RAM with the system. At BF16 you need at least 0.6 GB of usable unified memory (RAM minus macOS overhead).
Can I run GPT X2 125M CIx Long Context locally?
Yes — GPT X2 125M CIx Long Context can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is GPT X2 125M CIx Long Context?
At BF16, GPT X2 125M CIx Long Context can reach ~8000 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~1092 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 = ~8667 tok/s
Estimated speed at BF16 (0.6 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of GPT X2 125M CIx Long Context?
At BF16, the download is about 0.25 GB.
Which GPUs can run GPT X2 125M CIx Long Context?
52 consumer GPUs can run GPT X2 125M CIx Long Context at BF16. 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 GPT X2 125M CIx Long Context?
163 devices with unified memory can run GPT X2 125M CIx Long Context at BF16 (0.6 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (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.