WhiteRabbitNeo v3 7B — Hardware Requirements & GPU Compatibility
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WhiteRabbitNeo v3 7B is a 7.6B-parameter open language model from WhiteRabbitNeo. It supports a context window of up to 32,768 tokens. At Q4_K_M it needs about 4.99 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 · 32K)Best on 24 GB (Q4_K_M · ~131 tok/s · 32K)Macs from 8 GB (Q2_K · ~20 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run WhiteRabbitNeo v3 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/bartowski/WhiteRabbitNeo_WhiteRabbitNeo-V3-7B-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 18.2 GB free · 16K context
Download: WhiteRabbitNeo_WhiteRabbitNeo-V3-7B-Q4_K_M.gguf4.68 GBbartowski
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) (6 GB headroom) · Q4_K_M
- Generation speed
- ~119 tok/s
- generation speed
- Cost per 1M output tokens
- $0.29
- per 1M output tokens
How Much VRAM Does WhiteRabbitNeo v3 7B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 3.6 GB | 5.4 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~180 tok/s | |
| 3.8 GB | 5.5 GB | 3-bit small — 3-bit small quantization | Fits: 32K context · ~175 tok/s | |
| 4.1 GB | 5.9 GB | 3-bit medium — 3-bit medium quantization | Fits: 32K context · ~159 tok/s | |
| 4.2 GB | 6 GB | 4-bit · legacy — 4-bit legacy quantization | Fits: 32K context · ~155 tok/s | |
| 5.0 GB | 6.8 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 32K context · ~131 tok/s | |
| 5.8 GB | 7.6 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 32K context · ~112 tok/s | |
| 6.7 GB | 8.5 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 32K context · ~98 tok/s | |
| 8.0 GB | 9.8 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 32K context · ~82 tok/s |
Selected Q4_K_M
4-bit medium quantization5.8GBat 16K context
- Weights
- 4.6 GB
- KV cache
- 0.9 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 18.2 GB free · ~131 tok/s
≥50 tok/s needs ≥383 GB/s · RTX 5090 ~233, RTX 3090 Ti ~131
Which Hardware Can Run WhiteRabbitNeo v3 7B?
Q4_K_M · 5.0 GBWhiteRabbitNeo v3 7B (Q4_K_M) requires 5.0 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 7+ GB is recommended. Using the full 33K context window can add up to 1.8 GB, bringing total usage to 6.8 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 151 devices with unified memory can run WhiteRabbitNeo v3 7B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 2.2 GB free · up to 32K ctx
16 GPUs · 2 device families
RTX 3080~99Simulate output on RTX 3080RTX 2080 Ti~80Simulate output on RTX 2080 TiRTX 3070 Ti~79Simulate output on RTX 3070 TiOrin Nano~13Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~10Simulate output on iPhone 1720 GB
fits · 14.2 GB free · up to 32K ctx
1 GPU
RX 7900 XT~96Simulate output on RX 7900 XT32–40 GB
fits · 26.2 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~233Simulate output on RTX 5090M5 Max36 GB~65Simulate output on Mac Studio M5 Max (36 GB)M532 GB~22Simulate output on MacBook Air 13" M5 (32 GB)Orin~27Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~18Simulate 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 |
|---|---|---|---|---|
| Ling 3.0 TinyInclusion AI | — | 5.5 GB | ~364 | same size |
| BitCPM CANN 8BOpenBMB | — | 5.6 GB | ~127 | same size |
| Gemma 4 E4BGoogle | — | 5.6 GB | ~123 | same size |
| Olmo 3 7B InstructAllen AI | — | 8.4 GB | ~114 | same size |
| Phi Mini MoE InstructMicrosoft | — | 5.4 GB | ~239 | same size |
| Gemma 3n E4B ITGoogle | — | 7.7 GB | ~127 | same size |
Specifications
- Publisher
- WhiteRabbitNeo
- Parameters
- 7.6B
- Architecture
- Qwen2ForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 152,064
- Release Date
- 2025-04-25
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does WhiteRabbitNeo v3 7B need?
WhiteRabbitNeo v3 7B requires 5.0 GB of VRAM at Q4_K_M, or 15.7 GB at BF16. Full 33K context adds up to 1.8 GB (6.8 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 7.6B × 4.8 bits ÷ 8 = 4.6 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 ≈ 2.2 GB (at full 33K context)
VRAM usage by quantization
What's the best quantization for WhiteRabbitNeo v3 7B?
For WhiteRabbitNeo v3 7B, Q4_K_M (5.0 GB) offers the best balance of quality and VRAM usage. Q4_K_L (5.1 GB) provides better quality if you have the VRAM. The smallest option is IQ2_M at 3.0 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run WhiteRabbitNeo v3 7B on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run WhiteRabbitNeo v3 7B, the smallest at Q2_K. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 5.0 GB of usable unified memory (RAM minus macOS overhead), from Mac Mini M2 Pro (16 GB).
Can I run WhiteRabbitNeo v3 7B locally?
Yes — WhiteRabbitNeo v3 7B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is WhiteRabbitNeo v3 7B?
At Q4_K_M, WhiteRabbitNeo v3 7B can reach ~962 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~131 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 ÷ 4.99 × 0.65 = ~1042 tok/s
Estimated speed at Q4_K_M (5.0 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of WhiteRabbitNeo v3 7B?
At Q4_K_M, the download is about 4.57 GB. The full-precision BF16 version is 15.23 GB. The smallest option (IQ2_M) is 2.57 GB.
Which GPUs can run WhiteRabbitNeo v3 7B?
52 consumer GPUs can run WhiteRabbitNeo v3 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 WhiteRabbitNeo v3 7B?
151 devices with unified memory can run WhiteRabbitNeo v3 7B at Q4_K_M (5.0 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.