GPT X3 150M — Hardware Requirements & GPU Compatibility
ChatMath
GPT X3 150M is a 149M-parameter open language model from AxiomicLabs. It supports a context window of up to 8,192 tokens. At BF16 it needs about 0.64 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 8 GB (BF16 · 8K)Best on 24 GB (BF16 · ~1024 tok/s · 8K)Macs from 32 GB (~131 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run GPT X3 150M
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve AxiomicLabs/GPT-X3-150M --max-model-len 8192vLLM 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
- ~366 tok/s
- generation speed
- Cost per 1M output tokens
- $0.052
- per 1M output tokens
How Much VRAM Does GPT X3 150M Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 0.6 GB | — | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 8K context · ~1024 tok/s |
Selected BF16
Brain floating point 160.8GBat 8K context
- Weights
- 0.3 GB
- KV cache
- 0.2 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 23.3 GB free · ~1024 tok/s
≥50 tok/s needs ≥49 GB/s · RTX 5090 ~1820, RTX 3090 Ti ~1024
Which Hardware Can Run GPT X3 150M?
BF16 · 0.6 GBGPT X3 150M (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 8K context window can add up to 0.1 GB, bringing total usage to 0.8 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 176 devices with unified memory can run GPT X3 150M, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
≤ 11 GB
≤ 11 GB tier →fits · 7.3 GB free · up to 8K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~772Simulate output on RTX 3080RTX 2080 Ti~626Simulate output on RTX 2080 TiRTX 3070 Ti~618Simulate output on RTX 3070 TiM3*8 GB~112Simulate output on iMac 24" M3 (8 GB)Orin Nano~104Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~75Simulate output on iPhone 1720 GB
fits · 19.3 GB free · up to 8K ctx
1 GPU
RX 7900 XT~750Simulate output on RX 7900 XT32–40 GB
fits · 31.3 GB free · up to 8K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~1820Simulate output on RTX 5090M5 Max36 GB~503Simulate output on Mac Studio M5 Max (36 GB)M532 GB~168Simulate output on MacBook Air 13" M5 (32 GB)Orin~208Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~137Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at BF16
Specifications
- Publisher
- AxiomicLabs
- Parameters
- 149M
- Architecture
- GPTX3ForCausalLM
- Context Length
- 8,192 tokens
- Vocabulary Size
- 65,536
- Release Date
- 2026-09-23
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does GPT X3 150M need?
GPT X3 150M requires 0.6 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 149M × 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 ≈ 0.5 GB (at full 8K context)
VRAM usage by quantization
Can I run GPT X3 150M on a Mac?
Yes — Mac Mini M1 (8 GB) and 155 other Macs can run GPT X3 150M. 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 X3 150M locally?
Yes — GPT X3 150M can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is GPT X3 150M?
At BF16, GPT X3 150M can reach ~7500 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 ÷ 0.64 × 0.65 = ~8125 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 X3 150M?
At BF16, the download is about 0.30 GB.
Which GPUs can run GPT X3 150M?
52 consumer GPUs can run GPT X3 150M 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 X3 150M?
176 devices with unified memory can run GPT X3 150M 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.