VibeThinker 3B — Hardware Requirements & GPU Compatibility
ChatReasoningMath
VibeThinker 3B is a 3B-parameter open language model from litert-community. At Q4_K_M it needs about 1.98 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 · ~331 tok/s · 32K)Macs from 8 GB (Q4_K_M · ~36 tok/s)
MIT
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run VibeThinker 3B
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve litert-community/VibeThinker-3B --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 (8 GB headroom) · Q4_K_M
- Generation speed
- ~118 tok/s
- generation speed
- Cost per 1M output tokens
- $0.18
- per 1M output tokens
How Much VRAM Does VibeThinker 3B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 1.4 GB | 5.6 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~468 tok/s | |
| 1.6 GB | 5.8 GB | 3-bit medium — 3-bit medium quantization | Fits: 32K context · ~407 tok/s | |
| 2.0 GB | 6.2 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 32K context · ~331 tok/s | |
| 2.4 GB | 6.5 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 32K context · ~279 tok/s | |
| 2.7 GB | 6.9 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 32K context · ~241 tok/s | |
| 3.3 GB | 7.5 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 32K context · ~199 tok/s | |
| 6.6 GB | 10.8 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 32K context · ~99 tok/s |
Selected Q4_K_M
4-bit medium quantization3.9GBat 16K context
- Weights
- 1.4 GB
- KV cache
- 2.2 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 20.1 GB free · ~331 tok/s
≥50 tok/s needs ≥152 GB/s · RTX 5090 ~588, RTX 3090 Ti ~331
Which Hardware Can Run VibeThinker 3B?
Q4_K_M · 2.0 GBVibeThinker 3B (Q4_K_M) requires 2.0 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 3+ GB is recommended. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run VibeThinker 3B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 4.1 GB free · up to 32K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~250Simulate output on RTX 3080RTX 2080 Ti~202Simulate output on RTX 2080 TiRTX 3070 Ti~200Simulate output on RTX 3070 TiM3*8 GB~36Simulate output on iMac 24" M3 (8 GB)Orin Nano~34Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~24Simulate output on iPhone 1720 GB
fits · 16.1 GB free · up to 32K ctx
1 GPU
RX 7900 XT~242Simulate output on RX 7900 XT32–40 GB
fits · 28.1 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~588Simulate output on RTX 5090M5 Max36 GB~163Simulate output on Mac Studio M5 Max (36 GB)M532 GB~54Simulate output on MacBook Air 13" M5 (32 GB)Orin~67Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~44Simulate 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 |
|---|---|---|---|---|
| SmolLM3 3BHugging Face | — | 3.4 GB | ~285 | same size |
| Llama 3.2 3B InstructMeta | — | 4.1 GB | ~309 | same size |
| Phi 2Microsoft | — | 2.6 GB | ~248 | same size |
| AI21 Jamba Reasoning 3BAI21 Labs | — | 2.3 GB | ~291 | same size |
| Qwen2.5 3B InstructAlibaba | — | 2.8 GB | ~294 | same size |
| Stablelm 3B 4e1tStability AI | — | 3.3 GB | ~247 | same size |
Frequently Asked Questions
How much VRAM does VibeThinker 3B need?
VibeThinker 3B requires 2.0 GB of VRAM at Q4_K_M, or 6.6 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 3B × 4.8 bits ÷ 8 = 1.8 GB
KV Cache + Overhead ≈ 0.2 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.
VRAM usage by quantization
What's the best quantization for VibeThinker 3B?
For VibeThinker 3B, Q4_K_M (2.0 GB) offers the best balance of quality and VRAM usage. Q5_K_M (2.4 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 1.4 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run VibeThinker 3B on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run VibeThinker 3B. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 2.0 GB of usable unified memory (RAM minus macOS overhead).
Can I run VibeThinker 3B locally?
Yes — VibeThinker 3B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is VibeThinker 3B?
At Q4_K_M, VibeThinker 3B can reach ~2424 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~331 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 ÷ 1.98 × 0.65 = ~2626 tok/s
Estimated speed at Q4_K_M (2.0 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of VibeThinker 3B?
At Q4_K_M, the download is about 1.80 GB. The full-precision BF16 version is 6.00 GB. The smallest option (Q2_K) is 1.27 GB.
Which GPUs can run VibeThinker 3B?
52 consumer GPUs can run VibeThinker 3B 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 VibeThinker 3B?
163 devices with unified memory can run VibeThinker 3B at Q4_K_M (2.0 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.