North Micro Vision Instruct — Hardware Requirements & GPU Compatibility
VisionChat
North Micro Vision Instruct is Cohere's 2.5-billion-parameter vision-language model, built to handle native-resolution images alongside text in multi-turn conversations. It pairs a custom vision encoder with a small in-house language model, tuned for document understanding, chart and table reading, spatial reasoning, and visual grounding across a dozen-plus languages. Its small size suits local deployment on modest consumer GPUs once quantized. The model supports a very large 500,000 token context window, well beyond most models its size, useful for many-page documents or long image-heavy conversations. It is released under the Apache 2.0 license, allowing unrestricted commercial and research use. Published in August 2026 as part of Cohere's North family, it accepts inputs at resolutions up to roughly an A4 page at 200 dpi, aimed at document-heavy enterprise use.
168.2K downloads
146 likesSpecifications
- Publisher
- Cohere
- Family
- North
- Parameters
- 2.5B
- Architecture
- CohereCompassForConditionalGeneration
- Context Length
- 500,000 tokens
- Vocabulary Size
- 262,144
- Release Date
- 2026-08-10
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does North Micro Vision Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 5.5 GB | 62.6 GB | 4.97 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 North Micro Vision Instruct?
BF16 · 5.5 GBNorth Micro Vision Instruct (BF16) requires 5.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 8+ GB is recommended. Using the full 500K context window can add up to 57.1 GB, bringing total usage to 62.6 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run North Micro Vision Instruct?
BF16 · 5.5 GB58 devices with unified memory can run North Micro Vision Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does North Micro Vision Instruct need?
North Micro Vision Instruct requires 5.5 GB of VRAM at BF16. Full 500K context adds up to 57.1 GB (62.6 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 2.5B × 16 bits ÷ 8 = 5 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 57.6 GB (at full 500K context)
VRAM usage by quantization
BF165.5 GBBF16 + full context62.6 GB- Can I run North Micro Vision Instruct on a Mac?
North Micro Vision Instruct requires at least 5.5 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 North Micro Vision Instruct locally?
Yes — North Micro Vision Instruct can run locally on consumer hardware. At BF16 quantization it needs 5.5 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is North Micro Vision Instruct?
At BF16, North Micro Vision Instruct can reach ~873 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~119 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 ÷ 5.5 × 0.65 = ~946 tok/s
Estimated speed at BF16 (5.5 GB)
~946 tok/s~119 tok/s~946 tok/s~873 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of North Micro Vision Instruct?
At BF16, the download is about 4.97 GB.
- Which GPUs can run North Micro Vision Instruct?
52 consumer GPUs can run North Micro Vision Instruct at BF16 (5.5 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 40 GPUs have plenty of headroom for comfortable inference.
- Which devices can run North Micro Vision Instruct?
59 devices with unified memory can run North Micro Vision Instruct at BF16 (5.5 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, 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.