Pythia 160M — Hardware Requirements & GPU Compatibility
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Pythia 160M is part of EleutherAI's Pythia training suite, a collection of models trained on the same data in the same order at multiple scales to enable rigorous scientific research into how language models learn. At 160 million parameters, it is the smallest model in the suite and runs on virtually any hardware. This model is primarily valuable for researchers studying scaling laws, training dynamics, and emergent capabilities across model sizes. EleutherAI released full training checkpoints, data, and code, making Pythia 160M one of the most transparent and reproducible models available for academic study.
2.6M downloads
39 likesSpecifications
- Publisher
- EleutherAI
- Parameters
- 160M
- Architecture
- GPTNeoXForCausalLM
- Context Length
- 2,048 tokens
- Vocabulary Size
- 50,304
- Release Date
- 2023-07-09
- License
- Apache 2.0
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HuggingFace
How Much VRAM Does Pythia 160M Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| FP16 | 16.00 | 0.3 GB | — | 0.32 GB | Full half-precision — baseline for inference |
Which GPUs Can Run Pythia 160M?
FP16 · 0.3 GBPythia 160M (FP16) 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 Pythia 160M?
FP16 · 0.3 GB33 devices with unified memory can run Pythia 160M, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Pythia 160M need?
Pythia 160M requires 0.3 GB of VRAM at FP16.
VRAM = Weights + KV Cache + Overhead
Weights = 160M × 16 bits ÷ 8 = 0.3 GB
VRAM usage by quantization
FP160.3 GB- Can I run Pythia 160M on a Mac?
Pythia 160M requires at least 0.3 GB at FP16, 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 Pythia 160M locally?
Yes — Pythia 160M can run locally on consumer hardware. At FP16 quantization it needs 0.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Pythia 160M?
At FP16, Pythia 160M can reach ~8329 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~1872 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 = ~8329 tok/s
Estimated speed at FP16 (0.3 GB)
~8329 tok/s~1872 tok/s~6225 tok/s~5149 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Pythia 160M?
At FP16, the download is about 0.32 GB.
- Which GPUs can run Pythia 160M?
35 consumer GPUs can run Pythia 160M at FP16 (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 Pythia 160M?
33 devices with unified memory can run Pythia 160M at FP16 (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.