North Mini Code 1.0 — Hardware Requirements & GPU Compatibility
ChatCodeFunctions
North Mini Code 1.0 is Cohere's first open-weight coding model, a mixture-of-experts design with about 30.5 billion total parameters and roughly 3.3 billion active per token. It is tuned for agentic coding: tool calling, terminal workflows, and software-engineering tasks rather than general chat. Only the active parameters compute per token, so it decodes quickly for its size, though all weights must fit in memory; it runs on a single high-end consumer GPU once quantized. The model supports a 500,000 token context window, useful for large codebases or long agent transcripts. It is released under the Apache 2.0 license, allowing unrestricted commercial and research use. Published in June 2026 as the debut release in Cohere's North family, it is designed to run on a single H100-class GPU in FP8.
10.8K downloads
568 likesSpecifications
- Publisher
- Cohere
- Family
- North
- Parameters
- 30.5B
- Architecture
- Cohere2MoeForCausalLM
- Context Length
- 500,000 tokens
- Vocabulary Size
- 262,144
- Release Date
- 2026-06-05
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does North Mini Code 1.0 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 13.4 GB | 38.3 GB | 12.96 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 13.7 GB | 38.7 GB | 13.34 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 15.3 GB | 40.3 GB | 14.86 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 15.6 GB | 40.6 GB | 15.24 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 18.7 GB | 43.7 GB | 18.29 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 22.1 GB | 47.1 GB | 21.72 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 25.6 GB | 50.5 GB | 25.15 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 30.9 GB | 55.9 GB | 30.48 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run North Mini Code 1.0?
Q4_K_M · 18.7 GBNorth Mini Code 1.0 (Q4_K_M) requires 18.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 25+ GB is recommended. Using the full 500K context window can add up to 25.0 GB, bringing total usage to 43.7 GB. 8 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run North Mini Code 1.0?
Q4_K_M · 18.7 GB41 devices with unified memory can run North Mini Code 1.0, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightWhere to Download North Mini Code 1.0
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
Frequently Asked Questions
- How much VRAM does North Mini Code 1.0 need?
North Mini Code 1.0 requires 18.7 GB of VRAM at Q4_K_M, or 61.4 GB at BF16. Full 500K context adds up to 25.0 GB (43.7 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 30.5B × 4.8 bits ÷ 8 = 18.3 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 25.4 GB (at full 500K context)
VRAM usage by quantization
Q4_K_M18.7 GBQ4_K_M + full context43.7 GB- Can NVIDIA GeForce RTX 4090 run North Mini Code 1.0?
Yes, at Q5_K_L (22.5 GB) or lower. Higher quantizations like Q6_K (25.6 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for North Mini Code 1.0?
For North Mini Code 1.0, Q4_K_M (18.7 GB) offers the best balance of quality and VRAM usage. Q4_K_L (19.1 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 8.8 GB.
VRAM requirement by quantization
IQ2_XXS8.8 GBIQ3_S13.4 GBQ3_K_L16.0 GBQ4_K_M ★18.7 GBQ4_K_L19.1 GBBF1661.4 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run North Mini Code 1.0 on a Mac?
North Mini Code 1.0 requires at least 8.8 GB at IQ2_XXS, 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 Mini Code 1.0 locally?
Yes — North Mini Code 1.0 can run locally on consumer hardware. At Q4_K_M quantization it needs 18.7 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is North Mini Code 1.0?
At Q4_K_M, North Mini Code 1.0 can reach ~98 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~173 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 ÷ 18.7 × 0.65 = ~324 tok/s
Estimated speed at Q4_K_M (18.7 GB)
~324 tok/s~173 tok/s~324 tok/s~300 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of North Mini Code 1.0?
At Q4_K_M, the download is about 18.29 GB. The full-precision BF16 version is 60.97 GB. The smallest option (IQ2_XXS) is 8.38 GB.
- Which GPUs can run North Mini Code 1.0?
8 consumer GPUs can run North Mini Code 1.0 at Q4_K_M (18.7 GB). Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XT, AMD Radeon RX 7900 XTX. 1 GPU have plenty of headroom for comfortable inference.
- Which devices can run North Mini Code 1.0?
41 devices with unified memory can run North Mini Code 1.0 at Q4_K_M (18.7 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.