Amber — Hardware Requirements & GPU Compatibility
Chat
Amber is a 6.7 billion parameter model from LLM360, an initiative dedicated to full transparency in large language model training. Every aspect of Amber's creation has been publicly documented and released, including the complete training data, all intermediate checkpoints, training code, and evaluation results. This level of openness makes Amber uniquely valuable for researchers studying training dynamics, data influence, and model behavior at scale. For local deployment, it offers solid general-purpose text generation at a size that fits comfortably on mid-range consumer GPUs, though users primarily seeking chat performance may prefer models specifically tuned for instruction following.
53.0K downloads
72 likesSpecifications
- Publisher
- LLM360
- Parameters
- 6.7B
- Architecture
- LlamaForCausalLM
- Context Length
- 2,048 tokens
- Vocabulary Size
- 32,000
- Release Date
- 2025-10-10
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Amber Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 14.8 GB | — | 13.48 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Amber?
BF16 · 14.8 GBAmber (BF16) requires 14.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 20+ GB is recommended. 17 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 Amber?
BF16 · 14.8 GB27 devices with unified memory can run Amber, 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 Amber need?
Amber requires 14.8 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 6.7B × 16 bits ÷ 8 = 13.5 GB
KV Cache + Overhead ≈ 1.3 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
BF1614.8 GB- Can I run Amber on a Mac?
Amber requires at least 14.8 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 Amber locally?
Yes — Amber can run locally on consumer hardware. At BF16 quantization it needs 14.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Amber?
At BF16, Amber can reach ~196 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~44 tok/s. 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 ÷ 14.8 × 0.55 = ~196 tok/s
Estimated speed at BF16 (14.8 GB)
AMD Instinct MI300X~196 tok/sNVIDIA GeForce RTX 4090~44 tok/sNVIDIA H100 SXM~147 tok/sAMD Instinct MI250X~121 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Amber?
At BF16, the download is about 13.48 GB.