GPT S 1.4M — Hardware Requirements & GPU Compatibility
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GPT S 1.4M is a 1M-parameter open language model from AxiomicLabs. It supports a context window of up to 384 tokens. At BF16 it needs about 0.31 GB of VRAM — see which GPUs and Macs can run it below.
108 downloads
6 likesSpecifications
- Publisher
- AxiomicLabs
- Parameters
- 1M
- Architecture
- GPTS14MForCausalLM
- Context Length
- 384 tokens
- Vocabulary Size
- 4,096
- Release Date
- 2026-06-01
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does GPT S 1.4M Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 0.3 GB | — | 0.00 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run GPT S 1.4M?
BF16 · 0.3 GBGPT S 1.4M (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. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run GPT S 1.4M?
BF16 · 0.3 GB33 devices with unified memory can run GPT S 1.4M, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does GPT S 1.4M need?
GPT S 1.4M requires 0.3 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 1M × 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 GPT S 1.4M on a Mac?
GPT S 1.4M 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 GPT S 1.4M locally?
Yes — GPT S 1.4M 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 GPT S 1.4M?
At BF16, GPT S 1.4M can reach ~9403 tok/s on AMD Instinct MI300X. 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: AMD Instinct MI300X → 5300 ÷ 0.3 × 0.55 = ~9403 tok/s
Estimated speed at BF16 (0.3 GB)
~9403 tok/s~2114 tok/s~7028 tok/s~5814 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of GPT S 1.4M?
At BF16, the download is about 0.00 GB.
- Which GPUs can run GPT S 1.4M?
35 consumer GPUs can run GPT S 1.4M at BF16 (0.3 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 35 GPUs have plenty of headroom for comfortable inference.
- Which devices can run GPT S 1.4M?
33 devices with unified memory can run GPT S 1.4M at BF16 (0.3 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.