Limite 1B Violetto — Hardware Requirements & GPU Compatibility
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Limite 1B Violetto is a 1.0B-parameter open language model from paradigma-inc. It supports a context window of up to 131,072 tokens. At BF16 it needs about 2.47 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 8 GB (BF16 · 64K)Best on 24 GB (BF16 · ~265 tok/s · 128K)Macs from 8 GB (BF16 · ~29 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Limite 1B Violetto
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 paradigma-inc/limite-1b-violetto --max-model-len 16384vLLM 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) (8 GB headroom) · BF16
- Generation speed
- ~240 tok/s
- generation speed
- Cost per 1M output tokens
- $0.14
- per 1M output tokens
How Much VRAM Does Limite 1B Violetto Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 2.5 GB | 4 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 128K context · ~265 tok/s |
Selected BF16
Brain floating point 163.2GBat 16K context
- Weights
- 2.1 GB
- KV cache
- 0.8 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 20.8 GB free · ~265 tok/s
≥50 tok/s needs ≥190 GB/s · RTX 5090 ~472, RTX 3090 Ti ~265
Which Hardware Can Run Limite 1B Violetto?
BF16 · 2.5 GBLimite 1B Violetto (BF16) requires 2.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 4+ GB is recommended. Using the full 131K context window can add up to 6.3 GB, bringing total usage to 8.8 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run Limite 1B Violetto, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 4.8 GB free · up to 64K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~200Simulate output on RTX 3080RTX 2080 Ti~162Simulate output on RTX 2080 TiRTX 3070 Ti~160Simulate output on RTX 3070 TiM3*8 GB~29Simulate output on iMac 24" M3 (8 GB)Orin Nano~27Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~19Simulate output on iPhone 1720 GB
fits · 16.8 GB free · up to 128K ctx
1 GPU
RX 7900 XT~194Simulate output on RX 7900 XT32–40 GB
fits · 28.8 GB free · up to 128K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~472Simulate output on RTX 5090M5 Max36 GB~130Simulate output on Mac Studio M5 Max (36 GB)M532 GB~44Simulate output on MacBook Air 13" M5 (32 GB)Orin~54Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~36Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at BF16
Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| MiniCPM5 1BOpenBMB | — | 1.3 GB | ~662 | same size |
| Gemma 3 1B ITGoogle | — | 2.1 GB | ~993 | same size |
| TinyLlama 1.1B Chat v1.0TinyLlama | — | 1 GB | ~649 | same size |
| LLaMA3.1 8B Instruct DFlash UltraChatz-lab | — | 2.7 GB | ~269 | same size |
| Vaultgemma 1BGoogle | — | 3.7 GB | ~286 | same size |
| Qwen3 8B Speculator.eagle3RedHatAI | — | 2.1 GB | ~978 | same size |
Specifications
- Publisher
- paradigma-inc
- Parameters
- 1.0B
- Architecture
- LimiteForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 151,680
- Release Date
- 2026-09-21
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does Limite 1B Violetto need?
Limite 1B Violetto requires 2.5 GB of VRAM at BF16. Full 131K context adds up to 6.3 GB (8.8 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 1.0B × 16 bits ÷ 8 = 2.1 GB
KV Cache + Overhead ≈ 0.4 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 ≈ 6.7 GB (at full 131K context)
VRAM usage by quantization
Can I run Limite 1B Violetto on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Limite 1B Violetto. Apple Silicon uses unified memory, so the model shares RAM with the system. At BF16 you need at least 2.5 GB of usable unified memory (RAM minus macOS overhead).
Can I run Limite 1B Violetto locally?
Yes — Limite 1B Violetto can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Limite 1B Violetto?
At BF16, Limite 1B Violetto can reach ~1943 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~265 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 ÷ 2.47 × 0.65 = ~2105 tok/s
Estimated speed at BF16 (2.5 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Limite 1B Violetto?
At BF16, the download is about 2.07 GB.
Which GPUs can run Limite 1B Violetto?
52 consumer GPUs can run Limite 1B Violetto at BF16. 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 Limite 1B Violetto?
163 devices with unified memory can run Limite 1B Violetto at BF16 (2.5 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.