Amber — Hardware Requirements & GPU Compatibility
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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
- 2023-12-07
- 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 |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 4.2 GB | — | 2.86 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 4.3 GB | — | 2.95 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 4.7 GB | — | 3.28 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 4.7 GB | — | 3.37 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 5.4 GB | — | 4.04 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 6.2 GB | — | 4.80 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 6.9 GB | — | 5.56 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 8.1 GB | — | 6.74 GB | 8-bit quantization, near-lossless |
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 Amber?
Q4_K_M · 5.4 GBAmber (Q4_K_M) requires 5.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 8+ GB is recommended. 50 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 Amber?
Q4_K_M · 5.4 GB58 devices with unified memory can run Amber, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroomWhere to Download Amber
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
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Frequently Asked Questions
- How much VRAM does Amber need?
Amber requires 5.4 GB of VRAM at Q4_K_M, or 14.8 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 6.7B × 4.8 bits ÷ 8 = 4 GB
KV Cache + Overhead ≈ 1.4 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M5.4 GB- What's the best quantization for Amber?
For Amber, Q4_K_M (5.4 GB) offers the best balance of quality and VRAM usage. Q5_0 (5.6 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 3.2 GB.
VRAM requirement by quantization
IQ2_XXS3.2 GBIQ3_XS4.2 GBQ4_04.7 GBQ4_K_M ★5.4 GBQ5_05.6 GBBF1614.8 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Amber on a Mac?
Amber requires at least 3.2 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 Amber locally?
Yes — Amber can run locally on consumer hardware. At Q4_K_M quantization it needs 5.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Amber?
At Q4_K_M, Amber can reach ~812 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~121 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 ÷ 5.4 × 0.65 = ~959 tok/s
Estimated speed at Q4_K_M (5.4 GB)
~959 tok/s~121 tok/s~959 tok/s~812 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 Q4_K_M, the download is about 4.04 GB. The full-precision BF16 version is 13.48 GB. The smallest option (IQ2_XXS) is 1.85 GB.
- Which GPUs can run Amber?
50 consumer GPUs can run Amber at Q4_K_M (5.4 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 39 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Amber?
59 devices with unified memory can run Amber at Q4_K_M (5.4 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.