Mythos Nano — Hardware Requirements & GPU Compatibility
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Mythos Nano is a 3.1B-parameter open language model from squ11z1. It supports a context window of up to 131,072 tokens. At Q4_K_M it needs about 2.23 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 · 128K)Best on 24 GB (Q4_K_M · ~294 tok/s · 128K)Macs from 8 GB (Q4_K_M · ~32 tok/s)
MIT
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Mythos Nano
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve squ11z1/Mythos-nano --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (9 GB headroom) · Q4_K_M
- Generation speed
- ~105 tok/s
- generation speed
- Cost per 1M output tokens
- $0.18
- per 1M output tokens
How Much VRAM Does Mythos Nano Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 1.7 GB | 2.8 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 128K context · ~388 tok/s | |
| 1.7 GB | 2.9 GB | 3-bit small — 3-bit small quantization | Fits: 128K context · ~379 tok/s | |
| 1.9 GB | 3 GB | 3-bit medium — 3-bit medium quantization | Fits: 128K context · ~349 tok/s | |
| 2.0 GB | 3.1 GB | 3-bit large — 3-bit large quantization | Fits: 128K context · ~334 tok/s | |
| 2.2 GB | 3.4 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 128K context · ~294 tok/s | |
| 2.6 GB | 3.7 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 128K context · ~255 tok/s | |
| 2.9 GB | 4.1 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 128K context · ~224 tok/s | |
| 3.5 GB | 4.6 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 128K context · ~189 tok/s |
Selected Q4_K_M
4-bit medium quantization2.8GBat 16K context
- Weights
- 1.9 GB
- KV cache
- 0.6 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 21.2 GB free · ~294 tok/s
≥50 tok/s needs ≥171 GB/s · RTX 5090 ~522, RTX 3090 Ti ~294
Which Hardware Can Run Mythos Nano?
Q4_K_M · 2.2 GBMythos Nano (Q4_K_M) requires 2.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 3+ GB is recommended. Using the full 131K context window can add up to 4.8 GB, bringing total usage to 7.0 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run Mythos Nano, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 5.2 GB free · up to 128K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~222Simulate output on RTX 3080RTX 2080 Ti~180Simulate output on RTX 2080 TiRTX 3070 Ti~177Simulate output on RTX 3070 TiM3*8 GB~32Simulate output on iMac 24" M3 (8 GB)Orin Nano~30Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~21Simulate output on iPhone 1720 GB
fits · 17.2 GB free · up to 128K ctx
1 GPU
RX 7900 XT~215Simulate output on RX 7900 XT32–40 GB
fits · 29.2 GB free · up to 128K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~522Simulate output on RTX 5090M5 Max36 GB~144Simulate output on Mac Studio M5 Max (36 GB)M532 GB~48Simulate output on MacBook Air 13" M5 (32 GB)Orin~60Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~39Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Similar models
5| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| SmolLM3 3BHugging Face | — | 3.4 GB | ~285 | same size |
| Llama 3.2 3B InstructMeta | — | 4.1 GB | ~309 | same size |
| AI21 Jamba Reasoning 3BAI21 Labs | — | 2.3 GB | ~291 | same size |
| Qwen2.5 3B InstructAlibaba | — | 2.8 GB | ~294 | same size |
| MiniMax M3 EAGLE3Inferact | — | 2.7 GB | ~284 | same size |
Specifications
- Publisher
- squ11z1
- Parameters
- 3.1B
- Architecture
- Qwen2ForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2026-06-14
- License
- MIT
Frequently Asked Questions
How much VRAM does Mythos Nano need?
Mythos Nano requires 2.2 GB of VRAM at Q4_K_M, or 6.5 GB at BF16. Full 131K context adds up to 4.8 GB (7.0 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 3.1B × 4.8 bits ÷ 8 = 1.9 GB
KV Cache + Overhead ≈ 0.3 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.
KV Cache + Overhead ≈ 5.1 GB (at full 131K context)
VRAM usage by quantization
What's the best quantization for Mythos Nano?
For Mythos Nano, Q4_K_M (2.2 GB) offers the best balance of quality and VRAM usage. Q5_K_S (2.5 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 1.7 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Mythos Nano on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Mythos Nano. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 2.2 GB of usable unified memory (RAM minus macOS overhead).
Can I run Mythos Nano locally?
Yes — Mythos Nano can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Mythos Nano?
At Q4_K_M, Mythos Nano can reach ~2153 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~294 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 ÷ 2.23 × 0.65 = ~2332 tok/s
Estimated speed at Q4_K_M (2.2 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Mythos Nano?
At Q4_K_M, the download is about 1.85 GB. The full-precision BF16 version is 6.17 GB. The smallest option (Q2_K) is 1.31 GB.
Which GPUs can run Mythos Nano?
52 consumer GPUs can run Mythos Nano 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 Mythos Nano?
163 devices with unified memory can run Mythos Nano at Q4_K_M (2.2 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.