TinyStories 1M — Hardware Requirements & GPU Compatibility
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TinyStories 1M is a 1M-parameter open language model from roneneldan. It supports a context window of up to 2,048 tokens. At BF16 it needs about 0.01 GB of VRAM — see which GPUs and Macs can run it below.
117.8K downloads
68 likesSpecifications
- Publisher
- roneneldan
- Parameters
- 1M
- Architecture
- GPTNeoForCausalLM
- Context Length
- 2,048 tokens
- Vocabulary Size
- 50,257
- Release Date
- 2023-05-12
Get Started
HuggingFace
How Much VRAM Does TinyStories 1M Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 0.0 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 TinyStories 1M?
BF16 · 0.0 GBTinyStories 1M (BF16) requires 0.0 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 TinyStories 1M?
BF16 · 0.0 GB59 devices with unified memory can run TinyStories 1M, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomFrequently Asked Questions
- How much VRAM does TinyStories 1M need?
TinyStories 1M requires 0.0 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 1M × 16 bits ÷ 8 = 0 GB
VRAM usage by quantization
BF160.0 GB- Can I run TinyStories 1M on a Mac?
TinyStories 1M requires at least 0.0 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 TinyStories 1M locally?
Yes — TinyStories 1M can run locally on consumer hardware. At BF16 quantization it needs 0.0 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is TinyStories 1M?
At BF16, TinyStories 1M can reach ~440000 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~65520 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 ÷ 0.0 × 0.65 = ~520000 tok/s
Estimated speed at BF16 (0.0 GB)
~520000 tok/s~65520 tok/s~520000 tok/s~440000 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of TinyStories 1M?
At BF16, the download is about 0.01 GB.
- Which GPUs can run TinyStories 1M?
50 consumer GPUs can run TinyStories 1M at BF16 (0.0 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 TinyStories 1M?
59 devices with unified memory can run TinyStories 1M at BF16 (0.0 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.