NousCoder 14B — Hardware Requirements & GPU Compatibility
ChatCode
NousCoder 14B is a 14.8B-parameter open language model from Nous Research. It supports a context window of up to 81,920 tokens. At BF16 it needs about 30.17 GB of VRAM — see which GPUs and Macs can run it below.
169 downloads
209 likesSpecifications
- Publisher
- Nous Research
- Parameters
- 14.8B
- Architecture
- Qwen3ForCausalLM
- Context Length
- 81,920 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2026-01-05
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does NousCoder 14B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 30.2 GB | 43.3 GB | 29.54 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run NousCoder 14B?
BF16 · 30.2 GBNousCoder 14B (BF16) requires 30.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 40+ GB is recommended. Using the full 82K context window can add up to 13.1 GB, bringing total usage to 43.3 GB. 1 GPU can run it, including NVIDIA GeForce RTX 5090.
All compatible consumer-level GPUs are running near their VRAM limit. You may also want to consider professional GPUs (e.g., NVIDIA A100, H100) which offer significantly more VRAM. For more headroom and better throughput, consider a multi-GPU configuration with tensor parallelism (supported by tools like vLLM, llama.cpp, or text-generation-inference).
Decent
— Enough VRAM, may be tightWhich Devices Can Run NousCoder 14B?
BF16 · 30.2 GB15 devices with unified memory can run NousCoder 14B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M4 Max (36 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does NousCoder 14B need?
NousCoder 14B requires 30.2 GB of VRAM at BF16. Full 82K context adds up to 13.1 GB (43.3 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 14.8B × 16 bits ÷ 8 = 29.5 GB
KV Cache + Overhead ≈ 0.7 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 13.8 GB (at full 82K context)
VRAM usage by quantization
BF1630.2 GBBF16 + full context43.3 GB- Can I run NousCoder 14B on a Mac?
NousCoder 14B requires at least 30.2 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 NousCoder 14B locally?
Yes — NousCoder 14B can run locally on consumer hardware. At BF16 quantization it needs 30.2 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is NousCoder 14B?
At BF16, NousCoder 14B can reach ~97 tok/s on AMD Instinct MI300X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 30.2 × 0.55 = ~97 tok/s
Estimated speed at BF16 (30.2 GB)
~97 tok/s~72 tok/s~60 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of NousCoder 14B?
At BF16, the download is about 29.54 GB.
- Which GPUs can run NousCoder 14B?
1 consumer GPU can run NousCoder 14B at BF16 (30.2 GB). Top options include NVIDIA GeForce RTX 5090.
- Which devices can run NousCoder 14B?
15 devices with unified memory can run NousCoder 14B at BF16 (30.2 GB), including Mac Mini M4 (32 GB), Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB), Mac Studio M2 Ultra (192 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.