Pythia 2.8B — Hardware Requirements & GPU Compatibility
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Pythia 2.8B is a 2.9B-parameter open language model from EleutherAI. It supports a context window of up to 2,048 tokens. At Q4_K_M it needs about 1.92 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 2K)Best on 24 GB (Q4_K_M · ~341 tok/s · 2K)Macs from 8 GB (Q4_K_M · ~37 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Pythia 2.8B
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
vllm serve EleutherAI/pythia-2.8b --max-model-len 2048vLLM repo with its own quantisation
Download: Weights on Hugging Face
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 3080 Ti (12 GB) (10 GB headroom) · Q4_K_M
- Generation speed
- ~309 tok/s
- generation speed
- Cost per 1M output tokens
- $0.11
- per 1M output tokens
How Much VRAM Does Pythia 2.8B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 1.4 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 2K context · ~482 tok/s | |
| 1.6 GB | — | 3-bit medium — 3-bit medium quantization | Fits: 2K context · ~420 tok/s | |
| 1.9 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 2K context · ~341 tok/s | |
| 2.3 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 2K context · ~287 tok/s | |
| 2.6 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Fits: 2K context · ~248 tok/s | |
| 3.2 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 2K context · ~205 tok/s | |
| 6.4 GB | — | 16-bit · full — Full half-precision — baseline for inference | Fits: 2K context · ~102 tok/s |
Selected Q4_K_M
4-bit medium quantization1.9GBat 2K context
- Weights
- 1.3 GB
- KV cache
- 0.3 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 22.1 GB free · ~341 tok/s
≥50 tok/s needs ≥148 GB/s · RTX 5090 ~607, RTX 3090 Ti ~341
Which Hardware Can Run Pythia 2.8B?
Q4_K_M · 1.9 GBPythia 2.8B (Q4_K_M) requires 1.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 3+ GB is recommended. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run Pythia 2.8B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 6.1 GB free · up to 2K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~257Simulate output on RTX 3080RTX 2080 Ti~209Simulate output on RTX 2080 TiRTX 3070 Ti~206Simulate output on RTX 3070 TiM3*8 GB~37Simulate output on iMac 24" M3 (8 GB)Orin Nano~35Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~25Simulate output on iPhone 1720 GB
fits · 18.1 GB free · up to 2K ctx
1 GPU
RX 7900 XT~250Simulate output on RX 7900 XT32–40 GB
fits · 30.1 GB free · up to 2K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~607Simulate output on RTX 5090M5 Max36 GB~168Simulate output on Mac Studio M5 Max (36 GB)M532 GB~56Simulate output on MacBook Air 13" M5 (32 GB)Orin~69Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~46Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| LFM2.5 2.6BLiquid AI | — | 2.3 GB | ~321 | same size |
| SmolLM3 3BHugging Face | — | 3.4 GB | ~285 | same size |
| Phi 2Microsoft | — | 2.6 GB | ~248 | same size |
| Nemotron Flash 3BNVIDIA | — | 7.6 GB | ~109 | same size |
| Qwen2.5 3B InstructAlibaba | — | 2.8 GB | ~294 | same size |
| Stablelm 3B 4e1tStability AI | — | 3.3 GB | ~247 | same size |
Specifications
- Publisher
- EleutherAI
- Parameters
- 2.9B
- Architecture
- GPTNeoXForCausalLM
- Context Length
- 2,048 tokens
- Vocabulary Size
- 50,304
- Release Date
- 2023-02-13
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does Pythia 2.8B need?
Pythia 2.8B requires 1.9 GB of VRAM at Q4_K_M, or 6.4 GB at FP16.
VRAM = Weights + KV Cache + Overhead
Weights = 2.9B × 4.8 bits ÷ 8 = 1.7 GB
KV Cache + Overhead ≈ 0.2 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
VRAM usage by quantization
What's the best quantization for Pythia 2.8B?
For Pythia 2.8B, Q4_K_M (1.9 GB) offers the best balance of quality and VRAM usage. Q5_K_M (2.3 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 1.4 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Pythia 2.8B on a Mac?
Yes — Mac Mini M2 (8 GB) and 145 other Macs can run Pythia 2.8B. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 1.9 GB of usable unified memory (RAM minus macOS overhead).
Can I run Pythia 2.8B locally?
Yes — Pythia 2.8B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Pythia 2.8B?
At Q4_K_M, Pythia 2.8B can reach ~2500 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~341 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 ÷ 1.92 × 0.65 = ~2708 tok/s
Estimated speed at Q4_K_M (1.9 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Pythia 2.8B?
At Q4_K_M, the download is about 1.75 GB. The full-precision FP16 version is 5.82 GB. The smallest option (Q2_K) is 1.24 GB.
Which GPUs can run Pythia 2.8B?
52 consumer GPUs can run Pythia 2.8B at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
Which devices can run Pythia 2.8B?
163 devices with unified memory can run Pythia 2.8B at Q4_K_M (1.9 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (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.