Pythia 1B — Hardware Requirements & GPU Compatibility
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Pythia 1B is a 1.1B-parameter open language model from EleutherAI. It supports a context window of up to 2,048 tokens. At Q4_K_M it needs about 0.71 GB of VRAM — see which GPUs and Macs can run it below.
28.4K downloads
44 likesSpecifications
- Publisher
- EleutherAI
- Parameters
- 1.1B
- Architecture
- GPTNeoXForCausalLM
- Context Length
- 2,048 tokens
- Vocabulary Size
- 50,304
- Release Date
- 2023-03-10
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Pythia 1B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 0.5 GB | — | 0.46 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 0.6 GB | — | 0.53 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 0.7 GB | — | 0.65 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 0.8 GB | — | 0.77 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 1.0 GB | — | 0.89 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 1.2 GB | — | 1.08 GB | 8-bit quantization, near-lossless |
| FP16est. | 16.00 | 2.4 GB | — | 2.16 GB | Full half-precision — baseline for inference |
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 Pythia 1B?
Q4_K_M · 0.7 GBPythia 1B (Q4_K_M) requires 0.7 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 Pythia 1B?
Q4_K_M · 0.7 GB59 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 0.7 GB of VRAM at Q4_K_M, or 2.4 GB at FP16.
VRAM = Weights + KV Cache + Overhead
Weights = 1.1B × 4.8 bits ÷ 8 = 0.6 GB
KV Cache + Overhead ≈ 0.1 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M0.7 GB- What's the best quantization for Pythia 1B?
For Pythia 1B, Q4_K_M (0.7 GB) offers the best balance of quality and VRAM usage. Q5_K_M (0.8 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 0.5 GB.
VRAM requirement by quantization
Q2_K0.5 GBQ4_K_M ★0.7 GBQ5_K_M0.8 GBQ6_K1.0 GBQ8_01.2 GBFP162.4 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Pythia 1B on a Mac?
Pythia 1B requires at least 0.5 GB at Q2_K, 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 Q4_K_M quantization it needs 0.7 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Pythia 1B?
At Q4_K_M, Pythia 1B can reach ~6197 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~923 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 ÷ 0.7 × 0.65 = ~7324 tok/s
Estimated speed at Q4_K_M (0.7 GB)
~7324 tok/s~923 tok/s~7324 tok/s~6197 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 Q4_K_M, the download is about 0.65 GB. The full-precision FP16 version is 2.16 GB. The smallest option (Q2_K) is 0.46 GB.
- Which GPUs can run Pythia 1B?
50 consumer GPUs can run Pythia 1B at Q4_K_M (0.7 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 Pythia 1B?
59 devices with unified memory can run Pythia 1B at Q4_K_M (0.7 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.