WhiteRabbitNeo 13B V1 — Hardware Requirements & GPU Compatibility
Chat
WhiteRabbitNeo 13B V1 is a 13B-parameter open language model from WhiteRabbitNeo. It supports a context window of up to 16,384 tokens. At Q4_K_M it needs about 9.78 GB of VRAM — see which GPUs and Macs can run it below.
Runs well on a 24 GB GPU
Runs from 12 GB (Q4_K_M · 4K)Best on 24 GB (Q4_K_M · ~67 tok/s · 16K)Macs from 16 GB (Q4_K_M · ~14 tok/s)
Llama 2 Community
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run WhiteRabbitNeo 13B V1
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
ollama run hf.co/MaziyarPanahi/WhiteRabbitNeo-13B-v1-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 2.5 GB free · 16K context
Download: WhiteRabbitNeo-13B-v1.Q4_K_M.gguf7.87 GBMaziyarPanahi
Other ways
each opens step-by-stepRun in cloud
Fits on RTX PRO 4500 (32 GB) (10 GB headroom) · Q4_K_M
- Generation speed
- ~60 tok/s
- generation speed
- Cost per 1M output tokens
- $1.58
- per 1M output tokens
How Much VRAM Does WhiteRabbitNeo 13B V1 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 7.5 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 16K context · ~87 tok/s | |
| 7.7 GB | — | 3-bit small — 3-bit small quantization | Fits: 16K context · ~85 tok/s | |
| 8.3 GB | — | 3-bit medium — 3-bit medium quantization | Fits: 16K context · ~79 tok/s | |
| 8.6 GB | — | 3-bit large — 3-bit large quantization | Fits: 16K context · ~76 tok/s | |
| 9.8 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 16K context · ~67 tok/s | |
| 11.2 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 16K context · ~58 tok/s | |
| 12.7 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Fits: 8K context · ~52 tok/s | |
| 15.0 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 8K context · ~44 tok/s |
Selected Q4_K_M
4-bit medium quantization21.5GBat 16K context
- Weights
- 7.8 GB
- KV cache
- 13.4 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 2.5 GB free · ~67 tok/s
≥50 tok/s needs ≥752 GB/s · RTX 5090 ~119, RTX 3090 Ti ~67
Which Hardware Can Run WhiteRabbitNeo 13B V1?
Q4_K_M · 9.8 GBWhiteRabbitNeo 13B V1 (Q4_K_M) requires 9.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 13+ GB is recommended. Using the full 16K context window can add up to 11.7 GB, bringing total usage to 21.5 GB. 40 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080 Ti. 150 devices with unified memory can run WhiteRabbitNeo 13B V1, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Mini M2 Pro (16 GB).
20 GB
tight · up to 8K ctx
1 GPU
RX 7900 XT~49Simulate output on RX 7900 XT32–40 GB
fits · 10.5 GB free · up to 16K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~119Simulate output on RTX 5090M5 Max36 GB~33Simulate output on Mac Studio M5 Max (36 GB)M532 GB~11Simulate output on MacBook Air 13" M5 (32 GB)Orin~14Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~9Simulate 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 |
|---|---|---|---|---|
| Gemma 3 12B ITGoogle | 74.2 | 10.9 GB | ~82 | same size |
| Mistral Nemo Base 2407Mistral AI | — | 11 GB | ~81 | same size |
| Phi 3 Medium 128k InstructMicrosoft | — | 12 GB | ~72 | same size |
| Baichuan2 13B Basebaichuan-inc | — | 11.5 GB | ~76 | same size |
| Llama 2 13B Chat HFMeta | — | 11.5 GB | ~76 | same size |
| Vicuna 13B V1.3LMSYS | — | 8.6 GB | ~76 | same size |
Specifications
- Publisher
- WhiteRabbitNeo
- Parameters
- 13B
- Architecture
- LlamaForCausalLM
- Context Length
- 16,384 tokens
- Vocabulary Size
- 32,016
- Release Date
- 2023-12-17
- License
- Llama 2 Community
Frequently Asked Questions
How much VRAM does WhiteRabbitNeo 13B V1 need?
WhiteRabbitNeo 13B V1 requires 9.8 GB of VRAM at Q4_K_M, or 28.0 GB at BF16. Full 16K context adds up to 11.7 GB (21.5 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 13B × 4.8 bits ÷ 8 = 7.8 GB
KV Cache + Overhead ≈ 2 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 ≈ 13.7 GB (at full 16K context)
VRAM usage by quantization
Can NVIDIA GeForce RTX 4090 run WhiteRabbitNeo 13B V1?
Yes, at Q8_0 (15.0 GB) or lower. Higher quantizations like BF16 (28.0 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
What's the best quantization for WhiteRabbitNeo 13B V1?
For WhiteRabbitNeo 13B V1, Q4_K_M (9.8 GB) offers the best balance of quality and VRAM usage. Q5_K_S (10.9 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 7.5 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run WhiteRabbitNeo 13B V1 on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run WhiteRabbitNeo 13B V1. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 9.8 GB of usable unified memory (RAM minus macOS overhead).
Can I run WhiteRabbitNeo 13B V1 locally?
Yes — WhiteRabbitNeo 13B V1 can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is WhiteRabbitNeo 13B V1?
At Q4_K_M, WhiteRabbitNeo 13B V1 can reach ~491 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~67 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 ÷ 9.78 × 0.65 = ~532 tok/s
Estimated speed at Q4_K_M (9.8 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of WhiteRabbitNeo 13B V1?
At Q4_K_M, the download is about 7.80 GB. The full-precision BF16 version is 26.00 GB. The smallest option (Q2_K) is 5.53 GB.
Which GPUs can run WhiteRabbitNeo 13B V1?
40 consumer GPUs can run WhiteRabbitNeo 13B V1 at Q4_K_M. Top options include AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 6900 XT, AMD Radeon RX 6700 XT. 26 GPUs have plenty of headroom for comfortable inference.
Which devices can run WhiteRabbitNeo 13B V1?
150 devices with unified memory can run WhiteRabbitNeo 13B V1 at Q4_K_M (9.8 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.