Pythia 410M — Hardware Requirements & GPU Compatibility
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Pythia 410M is a 506M-parameter open language model from EleutherAI. It supports a context window of up to 2,048 tokens. At Q4_K_M it needs about 0.33 GB of VRAM — see which GPUs and Macs can run it below.
104.6K downloads
37 likesSpecifications
- Publisher
- EleutherAI
- Parameters
- 506M
- Architecture
- GPTNeoXForCausalLM
- Context Length
- 2,048 tokens
- Vocabulary Size
- 50,304
- Release Date
- 2023-02-13
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Pythia 410M Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q3_K_S | 3.50 | 0.2 GB | — | 0.22 GB | 3-bit small quantization |
| Q2_K | 3.40 | 0.2 GB | — | 0.22 GB | 2-bit quantization with K-quant improvements |
| Q3_K_M | 3.90 | 0.3 GB | — | 0.25 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 0.3 GB | — | 0.30 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 0.4 GB | — | 0.36 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 0.5 GB | — | 0.42 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 0.6 GB | — | 0.51 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Pythia 410M?
Q4_K_M · 0.3 GBPythia 410M (Q4_K_M) 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 410M?
Q4_K_M · 0.3 GB33 devices with unified memory can run Pythia 410M, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download Pythia 410M
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Frequently Asked Questions
- How much VRAM does Pythia 410M need?
Pythia 410M requires 0.3 GB of VRAM at Q4_K_M, or 1.1 GB at FP16.
VRAM = Weights + KV Cache + Overhead
Weights = 506M × 4.8 bits ÷ 8 = 0.3 GB
VRAM usage by quantization
Q4_K_M0.3 GB- What's the best quantization for Pythia 410M?
For Pythia 410M, Q4_K_M (0.3 GB) offers the best balance of quality and VRAM usage. Q5_K_S (0.4 GB) provides better quality if you have the VRAM. The smallest option is IQ3_XS at 0.2 GB.
VRAM requirement by quantization
IQ3_XS0.2 GBIQ3_M0.3 GBIQ4_XS0.3 GBQ4_K_M ★0.3 GBQ5_K_M0.4 GBFP161.1 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Pythia 410M on a Mac?
Pythia 410M requires at least 0.2 GB at IQ3_XS, 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 410M locally?
Yes — Pythia 410M can run locally on consumer hardware. At Q4_K_M quantization it needs 0.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Pythia 410M?
At Q4_K_M, Pythia 410M can reach ~8833 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~1986 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 = ~8833 tok/s
Estimated speed at Q4_K_M (0.3 GB)
~8833 tok/s~1986 tok/s~6602 tok/s~5461 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Pythia 410M?
At Q4_K_M, the download is about 0.30 GB. The full-precision FP16 version is 1.01 GB. The smallest option (IQ3_XS) is 0.21 GB.
- Which GPUs can run Pythia 410M?
35 consumer GPUs can run Pythia 410M at Q4_K_M (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 410M?
33 devices with unified memory can run Pythia 410M at Q4_K_M (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.