Pythia 160M Deduped — Hardware Requirements & GPU Compatibility
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Pythia 160M Deduped is a 213M-parameter open language model from EleutherAI. It supports a context window of up to 2,048 tokens. At Q4_K_M it needs about 0.14 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 · ~4680 tok/s · 2K)Macs from 8 GB (Q4_K_M · ~512 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Pythia 160M Deduped
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
ollama run hf.co/mradermacher/pythia-160m-deduped-i1-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 23.9 GB free · 2K context
Download: pythia-160m-deduped.i1-Q4_K_M.gguf0.11 GBmradermacher
Ways to runOther ways
each opens step-by-stepRun in cloud
Your own private copy · Q4_K_M · 24 GB GPU
- The easiest way to try this model
- Running in a few minutes, easy setup
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$5+ bonus for new users
Runs on RTX 3080 Ti (12 GB) (11 GB headroom) · Q4_K_M
- Generation speed
- ~4236 tok/s
- generation speed
- Cost per 1M output tokens
- $0.0080
- per 1M output tokens
How Much VRAM Does Pythia 160M Deduped Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 0.1 GB | — | 3-bit small — 3-bit small quantization | Fits: 2K context · ~6552 tok/s | |
| 0.1 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 2K context · ~6552 tok/s | |
| 0.1 GB | — | 3-bit medium — 3-bit medium quantization | Fits: 2K context · ~5956 tok/s | |
| 0.1 GB | — | 4-bit · legacy — 4-bit legacy quantization | Fits: 2K context · ~5460 tok/s | |
| 0.1 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 2K context · ~4680 tok/s | |
| 0.2 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 2K context · ~3854 tok/s | |
| 0.2 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Fits: 2K context · ~3448 tok/s | |
| 0.2 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 2K context · ~2849 tok/s |
Selected Q4_K_M
4-bit medium quantization0.1GBat 2K context
- Weights
- 0.1 GB
- KV cache
- 0.0 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 23.9 GB free · ~4680 tok/s
≥50 tok/s needs ≥11 GB/s · RTX 5090 ~8320, RTX 3090 Ti ~4680
Which Hardware Can Run Pythia 160M Deduped?
Q4_K_M · 0.1 GBPythia 160M Deduped (Q4_K_M) requires 0.1 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 160M Deduped, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 7.9 GB free · up to 2K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~3530Simulate output on RTX 3080RTX 2080 Ti~2860Simulate output on RTX 2080 TiRTX 3070 Ti~2824Simulate output on RTX 3070 TiM3*8 GB~512Simulate output on iMac 24" M3 (8 GB)Orin Nano~474Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~341Simulate output on iPhone 1720 GB
fits · 19.9 GB free · up to 2K ctx
1 GPU
RX 7900 XT~3429Simulate output on RX 7900 XT32–40 GB
fits · 31.9 GB free · up to 2K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~8320Simulate output on RTX 5090M5 Max36 GB~2300Simulate output on Mac Studio M5 Max (36 GB)M532 GB~768Simulate output on MacBook Air 13" M5 (32 GB)Orin~951Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~627Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Similar models
1| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| MiniCPM V 4.7 1B DSparkOpenBMB | — | 0.5 GB | ~1489 | same size |
Specifications
- Publisher
- EleutherAI
- Parameters
- 213M
- Architecture
- GPTNeoXForCausalLM
- Context Length
- 2,048 tokens
- Vocabulary Size
- 50,304
- Release Date
- 2023-02-08
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does Pythia 160M Deduped need?
Pythia 160M Deduped requires 0.1 GB of VRAM at Q4_K_M, or 0.5 GB at FP16.
VRAM = Weights + KV Cache + Overhead
Weights = 213M × 4.8 bits ÷ 8 = 0.1 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 160M Deduped?
For Pythia 160M Deduped, Q4_K_M (0.1 GB) offers the best balance of quality and VRAM usage. Q5_K_S (0.2 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 160M Deduped on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Pythia 160M Deduped. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 0.1 GB of usable unified memory (RAM minus macOS overhead).
Can I run Pythia 160M Deduped locally?
Yes — Pythia 160M Deduped can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Pythia 160M Deduped?
At Q4_K_M, Pythia 160M Deduped can reach ~34286 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~4680 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 ÷ 0.14 × 0.65 = ~37143 tok/s
Estimated speed at Q4_K_M (0.1 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Pythia 160M Deduped?
At Q4_K_M, the download is about 0.13 GB. The full-precision FP16 version is 0.43 GB. The smallest option (IQ2_XXS) is 0.06 GB.
Which GPUs can run Pythia 160M Deduped?
52 consumer GPUs can run Pythia 160M 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 160M Deduped?
163 devices with unified memory can run Pythia 160M Deduped at Q4_K_M (0.1 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.