OneRec 1.7B — Hardware Requirements & GPU Compatibility
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OneRec 1.7B is a 2.1B-parameter open language model from OpenOneRec. It supports a context window of up to 40,960 tokens. At Q4_K_M it needs about 1.81 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 · 40K)Best on 24 GB (Q4_K_M · ~362 tok/s · 40K)Macs from 8 GB (Q4_K_M · ~40 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run OneRec 1.7B
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/OneRec-1.7B-i1-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 20.5 GB free · 16K context
Download: OneRec-1.7B.i1-Q4_K_M.gguf1.35 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
- Chat right here or connect any app
$5+ bonus for new users
Runs on RTX 3080 Ti (12 GB) (8 GB headroom) · Q4_K_M
- Generation speed
- ~328 tok/s
- generation speed
- Cost per 1M output tokens
- $0.10
- per 1M output tokens
How Much VRAM Does OneRec 1.7B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 1.4 GB | 5 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 40K context · ~455 tok/s | |
| 1.5 GB | 5 GB | 3-bit small — 3-bit small quantization | Fits: 40K context · ~446 tok/s | |
| 1.6 GB | 5.1 GB | 3-bit medium — 3-bit medium quantization | Fits: 40K context · ~417 tok/s | |
| 1.6 GB | 5.1 GB | 4-bit · legacy — 4-bit legacy quantization | Fits: 40K context · ~410 tok/s | |
| 1.8 GB | 5.3 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 40K context · ~362 tok/s | |
| 2.0 GB | 5.6 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 40K context · ~320 tok/s | |
| 2.3 GB | 5.8 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 40K context · ~286 tok/s | |
| 2.7 GB | 6.2 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 40K context · ~245 tok/s |
Selected Q4_K_M
4-bit medium quantization3.5GBat 16K context
- Weights
- 1.3 GB
- KV cache
- 1.9 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 20.6 GB free · ~362 tok/s
≥50 tok/s needs ≥139 GB/s · RTX 5090 ~644, RTX 3090 Ti ~362
Which Hardware Can Run OneRec 1.7B?
Q4_K_M · 1.8 GBOneRec 1.7B (Q4_K_M) requires 1.8 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 41K context window can add up to 4.5 GB, bringing total usage to 6.3 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run OneRec 1.7B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 4.5 GB free · up to 40K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~273Simulate output on RTX 3080RTX 2080 Ti~221Simulate output on RTX 2080 TiRTX 3070 Ti~219Simulate output on RTX 3070 TiM3*8 GB~40Simulate output on iMac 24" M3 (8 GB)Orin Nano~37Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~26Simulate output on iPhone 1720 GB
fits · 16.6 GB free · up to 40K ctx
1 GPU
RX 7900 XT~265Simulate output on RX 7900 XT32–40 GB
fits · 28.6 GB free · up to 40K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~644Simulate output on RTX 5090M5 Max36 GB~178Simulate output on Mac Studio M5 Max (36 GB)M532 GB~59Simulate output on MacBook Air 13" M5 (32 GB)Orin~74Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~49Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| LFM3 Omni 2BLiquid AI | — | 2.4 GB | ~385 | same size |
| Qwen3.5 2BAlibaba | — | 1.9 GB | ~370 | same size |
| Qwen3 VL 2B InstructAlibaba | — | 3.5 GB | ~362 | same size |
| Qwen2 VL 2B InstructAlibaba | — | 2.1 GB | ~390 | same size |
| Kimi K3 DSparkRadixArk | — | 2.2 GB | ~381 | same size |
| Gemma 3n E2B IT Litert LmGoogle | — | 3.1 GB | ~496 | same size |
Specifications
- Publisher
- OpenOneRec
- Parameters
- 2.1B
- Architecture
- Qwen3ForCausalLM
- Context Length
- 40,960 tokens
- Vocabulary Size
- 176,384
- Release Date
- 2025-12-30
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does OneRec 1.7B need?
OneRec 1.7B requires 1.8 GB of VRAM at Q4_K_M, or 4.8 GB at BF16. Full 41K context adds up to 4.5 GB (6.3 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 2.1B × 4.8 bits ÷ 8 = 1.3 GB
KV Cache + Overhead ≈ 0.5 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 GB (at full 41K context)
VRAM usage by quantization
What's the best quantization for OneRec 1.7B?
For OneRec 1.7B, Q4_K_M (1.8 GB) offers the best balance of quality and VRAM usage. Q5_K_S (2 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 1.1 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run OneRec 1.7B on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run OneRec 1.7B. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 1.8 GB of usable unified memory (RAM minus macOS overhead).
Can I run OneRec 1.7B locally?
Yes — OneRec 1.7B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is OneRec 1.7B?
At Q4_K_M, OneRec 1.7B can reach ~2652 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~362 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 ÷ 1.81 × 0.65 = ~2873 tok/s
Estimated speed at Q4_K_M (1.8 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of OneRec 1.7B?
At Q4_K_M, the download is about 1.28 GB. The full-precision BF16 version is 4.26 GB. The smallest option (IQ2_XXS) is 0.59 GB.
Which GPUs can run OneRec 1.7B?
52 consumer GPUs can run OneRec 1.7B 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 OneRec 1.7B?
163 devices with unified memory can run OneRec 1.7B at Q4_K_M (1.8 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.