AliceAI Foundation 80B A3B Base — Hardware Requirements & GPU Compatibility
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AliceAI-Foundation-80B-A3B-Base is a base language model from Yandex, built on a hybrid attention and Mixture-of-Experts architecture and trained entirely from scratch. It has 80 billion total parameters with about 3 billion active per token, routed across 512 experts. As a pretrained checkpoint rather than a chat model, it is intended for fine-tuning or research use; its publisher reports results on par with larger open models in math, coding, and reasoning, with particular strength in Russian-language factual knowledge. It supports a 262,144-token context window and is released under the Apache 2.0 license. At roughly 80 billion total parameters, 4-bit quantization needs around 46GB of memory, putting it beyond a single consumer GPU and into multi-GPU or workstation territory.
2.3K downloads
296 likesSpecifications
- Publisher
- yandex
- Parameters
- 81.3B
- Architecture
- AliceAIForCausalLM
- Context Length
- 262,144 tokens
- Vocabulary Size
- 129,024
- Release Date
- 2026-09-12
- License
- Apache 2.0
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HuggingFace
How Much VRAM Does AliceAI Foundation 80B A3B Base Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 35.0 GB | 47.7 GB | 34.55 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 40.0 GB | 52.8 GB | 39.63 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 49.2 GB | 62.0 GB | 48.77 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 58.3 GB | 71.1 GB | 57.92 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 67.5 GB | 80.3 GB | 67.06 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 81.7 GB | 94.5 GB | 81.29 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 163.0 GB | 175.8 GB | 162.57 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 AliceAI Foundation 80B A3B Base?
Q4_K_M · 49.2 GBAliceAI Foundation 80B A3B Base (Q4_K_M) requires 49.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 64+ GB is recommended. Using the full 262K context window can add up to 12.8 GB, bringing total usage to 62.0 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run AliceAI Foundation 80B A3B Base?
Q4_K_M · 49.2 GB22 devices with unified memory can run AliceAI Foundation 80B A3B Base, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio (M3 Ultra, 96GB).
Runs great
— Plenty of headroom
Where to Download AliceAI Foundation 80B A3B Base
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 AliceAI Foundation 80B A3B Base need?
AliceAI Foundation 80B A3B Base requires 49.2 GB of VRAM at Q4_K_M, or 163.0 GB at BF16. Full 262K context adds up to 12.8 GB (62.0 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 81.3B × 4.8 bits ÷ 8 = 48.8 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 13.2 GB (at full 262K context)
VRAM usage by quantization
Q4_K_M49.2 GBQ4_K_M + full context62.0 GB- Can NVIDIA GeForce RTX 5090 run AliceAI Foundation 80B A3B Base?
No — AliceAI Foundation 80B A3B Base requires at least 35.0 GB at Q2_K, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- What's the best quantization for AliceAI Foundation 80B A3B Base?
For AliceAI Foundation 80B A3B Base, Q4_K_M (49.2 GB) offers the best balance of quality and VRAM usage. Q5_K_M (58.3 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 35.0 GB.
VRAM requirement by quantization
Q2_K35.0 GBQ4_K_M ★49.2 GBQ5_K_M58.3 GBQ6_K67.5 GBQ8_081.7 GBBF16163.0 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run AliceAI Foundation 80B A3B Base on a Mac?
AliceAI Foundation 80B A3B Base requires at least 35.0 GB at Q2_K, 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 AliceAI Foundation 80B A3B Base locally?
Yes — AliceAI Foundation 80B A3B Base can run locally on consumer hardware. At Q4_K_M quantization it needs 49.2 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is AliceAI Foundation 80B A3B Base?
At Q4_K_M, AliceAI Foundation 80B A3B Base can reach ~97 tok/s on AMD Instinct MI350X. 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 ÷ 49.2 × 0.65 = ~305 tok/s
Estimated speed at Q4_K_M (49.2 GB)
~305 tok/s~305 tok/s~272 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of AliceAI Foundation 80B A3B Base?
At Q4_K_M, the download is about 48.77 GB. The full-precision BF16 version is 162.57 GB. The smallest option (Q2_K) is 34.55 GB.
- Which GPUs can run AliceAI Foundation 80B A3B Base?
No single consumer GPU has enough VRAM to run AliceAI Foundation 80B A3B Base at Q4_K_M (49.2 GB). Multi-GPU or professional hardware is required.
- Which devices can run AliceAI Foundation 80B A3B Base?
23 devices with unified memory can run AliceAI Foundation 80B A3B Base at Q4_K_M (49.2 GB), including ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (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.