Pythia 1B — Hardware Requirements & GPU Compatibility
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28.4K downloads
44 likesSpecifications
- Publisher
- EleutherAI
- Parameters
- 1.1B
- 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 1B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| FP16 | 16.00 | 2.4 GB | — | 2.16 GB | Full half-precision — baseline for inference |
Which GPUs Can Run Pythia 1B?
FP16 · 2.4 GBPythia 1B (FP16) requires 2.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 4+ 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 1B?
FP16 · 2.4 GB33 devices with unified memory can run Pythia 1B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Pythia 1B need?
Pythia 1B requires 2.4 GB of VRAM at FP16.
VRAM = Weights + KV Cache + Overhead
Weights = 1.1B × 16 bits ÷ 8 = 2.2 GB
KV Cache + Overhead ≈ 0.2 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
FP162.4 GB- Can I run Pythia 1B on a Mac?
Pythia 1B requires at least 2.4 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 1B locally?
Yes — Pythia 1B can run locally on consumer hardware. At FP16 quantization it needs 2.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Pythia 1B?
At FP16, Pythia 1B can reach ~1230 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~277 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 ÷ 2.4 × 0.55 = ~1230 tok/s
Estimated speed at FP16 (2.4 GB)
AMD Instinct MI300X~1230 tok/sNVIDIA GeForce RTX 4090~277 tok/sNVIDIA H100 SXM~919 tok/sAMD Instinct MI250X~760 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Pythia 1B?
At FP16, the download is about 2.16 GB.
- Which GPUs can run Pythia 1B?
35 consumer GPUs can run Pythia 1B at FP16 (2.4 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 1B?
33 devices with unified memory can run Pythia 1B at FP16 (2.4 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.