Orca Mini 3B — Hardware Requirements & GPU Compatibility
ChatMath
Orca Mini 3B is a 3.4B-parameter open language model from pankajmathur in the Orca family. It supports a context window of up to 2,048 tokens. At Q4_K_M it needs about 2.26 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 · 2K)Best on 24 GB (Q4_K_M · ~290 tok/s · 2K)Macs from 8 GB (Q4_K_M · ~32 tok/s)
CC BY-NC-SA 4.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Orca Mini 3B
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve pankajmathur/orca_mini_3b --max-model-len 2048vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (9 GB headroom) · Q4_K_M
- Generation speed
- ~104 tok/s
- generation speed
- Cost per 1M output tokens
- $0.22
- per 1M output tokens
How Much VRAM Does Orca Mini 3B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 1.6 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 2K context · ~410 tok/s | |
| 1.8 GB | — | 3-bit medium — 3-bit medium quantization | Fits: 2K context · ~356 tok/s | |
| 2.3 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 2K context · ~290 tok/s | |
| 2.7 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 2K context · ~244 tok/s | |
| 3.1 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Fits: 2K context · ~211 tok/s | |
| 3.8 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 2K context · ~174 tok/s | |
| 7.5 GB | — | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 2K context · ~87 tok/s |
Selected Q4_K_M
4-bit medium quantization2.3GBat 2K context
- Weights
- 1.7 GB
- KV cache
- 0.3 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 21.7 GB free · ~290 tok/s
≥50 tok/s needs ≥174 GB/s · RTX 5090 ~515, RTX 3090 Ti ~290
Which Hardware Can Run Orca Mini 3B?
Q4_K_M · 2.3 GBOrca Mini 3B (Q4_K_M) requires 2.3 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 Orca Mini 3B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 5.7 GB free · up to 2K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~219Simulate output on RTX 3080RTX 2080 Ti~177Simulate output on RTX 2080 TiRTX 3070 Ti~175Simulate output on RTX 3070 TiM3*8 GB~32Simulate output on iMac 24" M3 (8 GB)Orin Nano~29Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~21Simulate output on iPhone 1720 GB
fits · 17.7 GB free · up to 2K ctx
1 GPU
RX 7900 XT~212Simulate output on RX 7900 XT32–40 GB
fits · 29.7 GB free · up to 2K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~515Simulate output on RTX 5090M5 Max36 GB~143Simulate output on Mac Studio M5 Max (36 GB)M532 GB~48Simulate output on MacBook Air 13" M5 (32 GB)Orin~59Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~39Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Orca Mini 3B
Orca family · 9 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Granite 4.0 MicroIBM | — | 3.7 GB | ~261 | same size |
| Deepseek Vl2 TinyDeepSeek | — | 9.4 GB | ~152 | same size |
| Llama 3.2 3B InstructMeta | — | 4.1 GB | ~309 | same size |
| AI21 Jamba Reasoning 3BAI21 Labs | — | 2.3 GB | ~291 | same size |
| Sarashina2.2 3B Instruct v0.1sbintuitions | — | 3.7 GB | ~247 | same size |
| PowerMoE 3Bibm-research | — | 2.6 GB | ~354 | same size |
Specifications
- Publisher
- pankajmathur
- Family
- Orca
- Parameters
- 3.4B
- Architecture
- LlamaForCausalLM
- Context Length
- 2,048 tokens
- Vocabulary Size
- 32,000
- Release Date
- 2023-06-22
- License
- CC BY-NC-SA 4.0
Frequently Asked Questions
How much VRAM does Orca Mini 3B need?
Orca Mini 3B requires 2.3 GB of VRAM at Q4_K_M, or 7.5 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 3.4B × 4.8 bits ÷ 8 = 2.1 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 Orca Mini 3B?
For Orca Mini 3B, Q4_K_M (2.3 GB) offers the best balance of quality and VRAM usage. Q5_K_M (2.7 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 1.6 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Orca Mini 3B on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Orca Mini 3B. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 2.3 GB of usable unified memory (RAM minus macOS overhead).
Can I run Orca Mini 3B locally?
Yes — Orca Mini 3B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Orca Mini 3B?
At Q4_K_M, Orca Mini 3B can reach ~2124 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~290 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 ÷ 2.26 × 0.65 = ~2301 tok/s
Estimated speed at Q4_K_M (2.3 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Orca Mini 3B?
At Q4_K_M, the download is about 2.06 GB. The full-precision BF16 version is 6.85 GB. The smallest option (Q2_K) is 1.46 GB.
Which GPUs can run Orca Mini 3B?
52 consumer GPUs can run Orca Mini 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 Orca Mini 3B?
163 devices with unified memory can run Orca Mini 3B at Q4_K_M (2.3 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.