OrcaSAQ 2 27B — Hardware Requirements & GPU Compatibility
ChatReasoningFunctions
OrcaSAQ 2 27B is a 6.8B-parameter open language model from orcarouter in the Orca family. It supports a context window of up to 262,144 tokens. At BF16 it needs about 14.29 GB of VRAM — see which GPUs and Macs can run it below.
3.7K downloads
254 likesSpecifications
- Publisher
- orcarouter
- Family
- Orca
- Parameters
- 6.8B
- Architecture
- Qwen3_5ForCausalLM
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-09-24
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does OrcaSAQ 2 27B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 14.3 GB | 71.1 GB | 13.54 GB | Brain floating point 16 — preferred for training |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run OrcaSAQ 2 27B?
BF16 · 14.3 GBOrcaSAQ 2 27B (BF16) requires 14.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 19+ GB is recommended. Using the full 262K context window can add up to 56.8 GB, bringing total usage to 71.1 GB. 26 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 5080.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run OrcaSAQ 2 27B?
BF16 · 14.3 GB47 devices with unified memory can run OrcaSAQ 2 27B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 (16 GB).
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does OrcaSAQ 2 27B need?
OrcaSAQ 2 27B requires 14.3 GB of VRAM at BF16. Full 262K context adds up to 56.8 GB (71.1 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 6.8B × 16 bits ÷ 8 = 13.5 GB
KV Cache + Overhead ≈ 0.8 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 57.6 GB (at full 262K context)
VRAM usage by quantization
BF1614.3 GBBF16 + full context71.1 GB- Can I run OrcaSAQ 2 27B on a Mac?
OrcaSAQ 2 27B requires at least 14.3 GB at BF16, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run OrcaSAQ 2 27B locally?
Yes — OrcaSAQ 2 27B can run locally on consumer hardware. At BF16 quantization it needs 14.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is OrcaSAQ 2 27B?
At BF16, OrcaSAQ 2 27B can reach ~336 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~46 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 ÷ 14.3 × 0.65 = ~364 tok/s
Estimated speed at BF16 (14.3 GB)
~364 tok/s~46 tok/s~364 tok/s~336 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of OrcaSAQ 2 27B?
At BF16, the download is about 13.54 GB.
- Which GPUs can run OrcaSAQ 2 27B?
26 consumer GPUs can run OrcaSAQ 2 27B at BF16 (14.3 GB). Top options include AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090, NVIDIA GeForce RTX 3090 Ti, AMD Radeon RX 6800. 7 GPUs have plenty of headroom for comfortable inference.
- Which devices can run OrcaSAQ 2 27B?
49 devices with unified memory can run OrcaSAQ 2 27B at BF16 (14.3 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (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.