Pythia 410M Deduped — Hardware Requirements & GPU Compatibility
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Pythia 410M Deduped 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.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 2K)Best on 24 GB (Q4_K_M · ~1986 tok/s · 2K)Macs from 8 GB (Q4_K_M · ~217 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Pythia 410M Deduped
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
ollama run hf.co/mradermacher/pythia-410m-deduped-i1-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 23.7 GB free · 2K context
Download: pythia-410m-deduped.i1-Q4_K_M.gguf0.27 GBmradermacher
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (11 GB headroom) · Q4_K_M
- Generation speed
- ~709 tok/s
- generation speed
- Cost per 1M output tokens
- $0.032
- per 1M output tokens
How Much VRAM Does Pythia 410M Deduped Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 0.2 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 2K context · ~2730 tok/s | |
| 0.2 GB | — | 3-bit small — 3-bit small quantization | Fits: 2K context · ~2730 tok/s | |
| 0.3 GB | — | 3-bit medium — 3-bit medium quantization | Fits: 2K context · ~2427 tok/s | |
| 0.3 GB | — | 4-bit · legacy — 4-bit legacy quantization | Fits: 2K context · ~2340 tok/s | |
| 0.3 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 2K context · ~1986 tok/s | |
| 0.4 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 2K context · ~1638 tok/s | |
| 0.5 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Fits: 2K context · ~1424 tok/s | |
| 0.6 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 2K context · ~1170 tok/s |
Selected Q4_K_M
4-bit medium quantization0.3GBat 2K context
- Weights
- 0.1 GB
- KV cache
- 0.0 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 23.7 GB free · ~1986 tok/s
≥50 tok/s needs ≥25 GB/s · RTX 5090 ~3530, RTX 3090 Ti ~1986
Which Hardware Can Run Pythia 410M Deduped?
Q4_K_M · 0.3 GBPythia 410M Deduped (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. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run Pythia 410M Deduped, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 7.7 GB free · up to 2K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~1498Simulate output on RTX 3080RTX 2080 Ti~1213Simulate output on RTX 2080 TiRTX 3070 Ti~1198Simulate output on RTX 3070 TiM3*8 GB~217Simulate output on iMac 24" M3 (8 GB)Orin Nano~201Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~145Simulate output on iPhone 1720 GB
fits · 19.7 GB free · up to 2K ctx
1 GPU
RX 7900 XT~1455Simulate output on RX 7900 XT32–40 GB
fits · 31.7 GB free · up to 2K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~3530Simulate output on RTX 5090M5 Max36 GB~976Simulate output on Mac Studio M5 Max (36 GB)M532 GB~326Simulate output on MacBook Air 13" M5 (32 GB)Orin~403Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~266Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Similar models
4| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Falcon H1 0.5B BaseTII UAE | — | 1.2 GB | ~950 | same size |
| Qwen2.5 0.5B InstructAlibaba | — | 0.8 GB | ~1057 | same size |
| Qwen2 0.5B InstructAlibaba | — | 0.8 GB | ~1057 | same size |
| Qwen3 4B DFlash B16z-lab | — | 0.8 GB | ~1008 | same size |
Specifications
- Publisher
- EleutherAI
- Parameters
- 506M
- 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 410M Deduped need?
Pythia 410M Deduped 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
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 410M Deduped?
For Pythia 410M Deduped, 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 IQ2_XXS at 0.1 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Pythia 410M Deduped on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Pythia 410M Deduped. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 0.3 GB of usable unified memory (RAM minus macOS overhead).
Can I run Pythia 410M Deduped locally?
Yes — Pythia 410M Deduped can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Pythia 410M Deduped?
At Q4_K_M, Pythia 410M Deduped can reach ~14546 tok/s on AMD Instinct MI350X. 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: NVIDIA B200 → 8000 ÷ 0.33 × 0.65 = ~15758 tok/s
Estimated speed at Q4_K_M (0.3 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Pythia 410M Deduped?
At Q4_K_M, the download is about 0.30 GB. The full-precision FP16 version is 1.01 GB. The smallest option (IQ2_XXS) is 0.14 GB.
Which GPUs can run Pythia 410M Deduped?
52 consumer GPUs can run Pythia 410M Deduped 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 410M Deduped?
163 devices with unified memory can run Pythia 410M Deduped at Q4_K_M (0.3 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.