Can you run AliceAI Foundation 80B A3B Base locally?
AliceAI family · 2026 · 81.3B parameters (3B activated per token) · Hugging Face model card
AliceAI Foundation 80B A3B Base is a mixture-of-experts model with 3B activated parameters per token and 80B-class total open weights (official card: 80B total / 3B active; HF safetensors.total=81,286,433,408). Native context is 262,144 tokens, apache-2.0. Local VRAM fit uses the 3B active count, not the 80B dense-equivalent: roughly 8 GB at Q4-class (calculated). Weights still occupy ~80B-class disk. Measured speeds: not yet published here.
Minimum: 8 GB+ memory (Q4-class on 3B active, calculated) · Recommended: 8 GB+ for comfortable context headroom (Q4-class on 3B active, calculated)
How much VRAM does AliceAI Foundation 80B A3B Base need at each quantization?
AliceAI Foundation 80B A3B Base needs 2.4 GB of VRAM at Q4_K_M. The table below lists weights-only size and total VRAM including overhead for each common quantization level with 4k and 32k token contexts.
| Quantization | Bits / weight | Weights only | Total + KV @4k ctx | Total + KV @32k ctx |
|---|---|---|---|---|
| Q4_K_M | 4.8 | 1.8 GB | 2.4 GB | 5.2 GB |
| Q5_K_M | 5.7 | 2.1 GB | 2.8 GB | 5.6 GB |
| Q6_K | 6.6 | 2.5 GB | 3.1 GB | 6.0 GB |
| Q8_0 | 8.5 | 3.2 GB | 3.9 GB | 6.7 GB |
| FP16 | 16 | 6.0 GB | 7.0 GB | 9.8 GB |
hybrid KDA + gated attention; MoE 10/512 + 1 shared
Method: weights = parameters × bits-per-weight (Q4_K_M ≈ 4.8, Q5_K_M ≈ 5.7, Q6_K ≈ 6.6, Q8_0 ≈ 8.5, FP16 = 16), plus 10% loading overhead, plus KV cache from the verified architecture config (48 layers, 2 KV heads, 256 head dim). Source: HF-verified 2026-09-21 via api (gated=false params=81.29B safetensors.total=81286433408 ctx=262144 license=apache-2.0 repo=yandex/AliceAI-Foundation-80B-A3B-Base@641d8820dd13db8201b1d88e635c5b21ee247568 layers=48 kv_heads=2). MoE: num_experts=512 num_experts_per_tok=10 active_params_b=3.0 (official README (80B total, 3B active)). VRAM figures are documented calculations, not measurements. No tok/s invented.
Which GPUs can run AliceAI Foundation 80B A3B Base locally?
At Q4_K_M with a 4k context, AliceAI Foundation 80B A3B Base needs 2.4 GB of VRAM. The lists below are computed live from our GPU database and grouped by how much headroom the card has. Disclosure: CompareAIHardware.com participates in the Amazon Associates program and earns from qualifying purchases through links on this page. Affiliate relationships do not influence our recommendations.
Runs AliceAI Foundation 80B A3B Base comfortably (25%+ VRAM headroom)
| GPU | VRAM | Bandwidth | Type | |
|---|---|---|---|---|
| Radeon RX 9050 (4GB OEM) | 4 GB | — | Consumer GPU | |
| Arc A580 | 8 GB | 512 GB/s | Consumer GPU | Check price |
| Arc A750 | 8 GB | 512 GB/s | Consumer GPU | Check price |
| GeForce RTX 3070EOL | 8 GB | 448 GB/s | Consumer GPU | Check price |
| GeForce RTX 4060 | 8 GB | 272 GB/s | Consumer GPU | Check price |
| GeForce RTX 4060 Ti 8GB | 8 GB | 288 GB/s | Consumer GPU | Check price |
| GeForce RTX 5050 | 8 GB | 320 GB/s | Consumer GPU | Check price |
| GeForce RTX 5060 | 8 GB | 448 GB/s | Consumer GPU | Check price |
| Radeon RX 7600 | 8 GB | 288 GB/s | Consumer GPU | Check price |
| Radeon RX 9050 | 8 GB | 288 GB/s | Consumer GPU | |
| Radeon RX 9060 XT 8GB | 8 GB | 320 GB/s | Consumer GPU | |
| Arc B570 | 10 GB | 380 GB/s | Consumer GPU | Check price |
| GeForce RTX 3080 10GBEOL | 10 GB | 760 GB/s | Consumer GPU | Check price |
| GeForce RTX 2080 TiEOL | 11 GB | 616 GB/s | Consumer GPU | Check price |
| Arc B580 | 12 GB | 456 GB/s | Consumer GPU | Check price |
| GeForce RTX 3060 12GB | 12 GB | 360 GB/s | Consumer GPU | Check price |
| GeForce RTX 3080 TiEOL | 12 GB | 912 GB/s | Consumer GPU | Check price |
| GeForce RTX 4070EOL | 12 GB | 504 GB/s | Consumer GPU | Check price |
| GeForce RTX 4070 SUPEREOL | 12 GB | 504 GB/s | Consumer GPU | Check price |
| GeForce RTX 5070 | 12 GB | 672 GB/s | Consumer GPU | Check price |
| Radeon RX 7700 XT | 12 GB | 432 GB/s | Consumer GPU | Check price |
| Radeon RX 9070 GRE | 12 GB | 432 GB/s | Consumer GPU | |
| Arc A770 16GB | 16 GB | 560 GB/s | Consumer GPU | Check price |
| GeForce RTX 4060 Ti 16GB | 16 GB | 288 GB/s | Consumer GPU | Check price |
| GeForce RTX 4070 Ti SUPEREOL | 16 GB | 672 GB/s | Consumer GPU | Check price |
| GeForce RTX 4080EOL | 16 GB | 716 GB/s | Consumer GPU | Check price |
| GeForce RTX 4080 SUPER | 16 GB | 736 GB/s | Consumer GPU | Check price |
| GeForce RTX 5060 Ti 16GB | 16 GB | 448 GB/s | Consumer GPU | Check price |
| GeForce RTX 5070 Ti | 16 GB | 896 GB/s | Consumer GPU | Check price |
| GeForce RTX 5080 | 16 GB | 960 GB/s | Consumer GPU | Check price |
| Intel Arc Pro B50 | 16 GB | 224 GB/s | Pro GPU | |
| NVIDIA RTX PRO 2000 Blackwell | 16 GB | 288 GB/s | Pro GPU | Check price |
| Radeon RX 6800 XTEOL | 16 GB | 512 GB/s | Consumer GPU | Check price |
| Radeon RX 7600 XT | 16 GB | 288 GB/s | Consumer GPU | Check price |
| Radeon RX 7800 XT | 16 GB | 624 GB/s | Consumer GPU | Check price |
| Radeon RX 9060 XT (16GB) | 16 GB | 320 GB/s | Consumer GPU | |
| Radeon RX 9060 XT 16GB | 16 GB | 320 GB/s | Consumer GPU | |
| Radeon RX 9060 XT LP (16GB) | 16 GB | 320 GB/s | Consumer GPU | |
| Radeon RX 9070 | 16 GB | 640 GB/s | Consumer GPU | Check price |
| Radeon RX 9070 XT | 16 GB | 640 GB/s | Consumer GPU | Check price |
| Radeon RX 7900 XT | 20 GB | 800 GB/s | Consumer GPU | Check price |
| GeForce RTX 3090EOL | 24 GB | 936 GB/s | Consumer GPU | Check price |
| GeForce RTX 3090 TiEOL | 24 GB | 1008 GB/s | Consumer GPU | Check price |
| GeForce RTX 4090 | 24 GB | 1008 GB/s | Consumer GPU | Check price |
| Intel Arc Pro B60 | 24 GB | 456 GB/s | Pro GPU | |
| NVIDIA RTX PRO 4000 Blackwell | 24 GB | 672 GB/s | Pro GPU | Check price |
| Radeon RX 7900 XTX | 24 GB | 960 GB/s | Consumer GPU | Check price |
| AMD Radeon PRO W6800 | 32 GB | 512 GB/s | Pro GPU | |
| AMD Radeon Pro W7800 | 32 GB | 576 GB/s | Pro GPU | Check price |
| GeForce RTX 5090 | 32 GB | 1792 GB/s | Consumer GPU | Check price |
| Intel Arc Pro B65 | 32 GB | 608 GB/s | Pro GPU | |
| Intel Arc Pro B70 | 32 GB | 608 GB/s | Pro GPU | |
| NVIDIA RTX 5000 Ada | 32 GB | 576 GB/s | Pro GPU | Check price |
| NVIDIA RTX PRO 4500 Blackwell | 32 GB | 896 GB/s | Pro GPU | Check price |
| NVIDIA A100 40GB SXMEOL | 40 GB | 1555 GB/s | Data Center GPU | |
| AMD Radeon PRO W7900 Dual Slot | 48 GB | 864 GB/s | Pro GPU | |
| AMD Radeon Pro W7900 | 48 GB | 864 GB/s | Pro GPU | Check price |
| NVIDIA RTX PRO 5000 Blackwell | 48 GB | 1344 GB/s | Pro GPU | |
| RTX 6000 Ada | 48 GB | 960 GB/s | Pro GPU | Check price |
| RTX A6000 | 48 GB | 768 GB/s | Pro GPU | Check price |
| H100 SXM | 80 GB | 3350 GB/s | Data Center GPU | |
| NVIDIA A100 80GB SXM | 80 GB | 2039 GB/s | Data Center GPU | |
| NVIDIA H100 PCIe 80GB | 80 GB | 2039 GB/s | Data Center GPU | |
| NVIDIA RTX PRO 6000 Blackwell | 96 GB | 1792 GB/s | Pro GPU | Check price |
| NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition | 96 GB | — | Pro GPU | |
| NVIDIA RTX PRO 6000 Blackwell Server Edition | 96 GB | — | Pro GPU | |
| H200 SXM | 141 GB | 4800 GB/s | Data Center GPU | |
| AMD Instinct MI300X | 192 GB | 5300 GB/s | Data Center GPU | |
| NVIDIA B200 | 192 GB | 8000 GB/s | Data Center GPU | |
| AMD Instinct MI325X | 256 GB | 6000 GB/s | Data Center GPU | |
| AMD Instinct MI350X | 288 GB | 8000 GB/s | Data Center GPU | |
| AMD Instinct MI355X | 288 GB | 8000 GB/s | Data Center GPU | |
| NVIDIA B300 (Blackwell Ultra) | 288 GB | 8000 GB/s | Data Center GPU | |
| AMD Instinct MI430X | 432 GB | 23300 GB/s | Data Center GPU | |
| AMD Instinct MI455X | 432 GB | 23300 GB/s | Data Center GPU |
Can a Mac run AliceAI Foundation 80B A3B Base?
Yes — these Apple Silicon machines fit AliceAI Foundation 80B A3B Base at Q4_K_M, since macOS lets the GPU use about 75% of unified memory. Generation speed is bound by memory bandwidth, so the GB/s column matters as much as capacity.
| Apple Silicon | Unified memory | Usable by GPU (~75%) | Bandwidth |
|---|---|---|---|
| Apple M3 | 24 GB | 18 GB | 150 GB/s |
| Apple M4 | 32 GB | 24 GB | 120 GB/s |
| M6 (Mac mini) | 32 GB | 24 GB | 170 GB/s |
| Apple M3 Pro | 36 GB | 27 GB | 300 GB/s |
| Apple M4 Pro | 64 GB | 48 GB | 273 GB/s |
| Apple M5 Pro | 64 GB | 48 GB | 307 GB/s |
| M5 Pro (Mac mini) | 64 GB | 48 GB | 307 GB/s |
| Apple M3 Max | 128 GB | 96 GB | 400 GB/s |
| Apple M5 Max | 128 GB | 96 GB | 614 GB/s |
| M4 Max (MacBook Pro) | 128 GB | 96 GB | 546 GB/s |
| M5 Max (Mac Studio) | 128 GB | 96 GB | — |
| M5 Ultra (Mac Studio) | 512 GB | 384 GB | 1200 GB/s |
Can AliceAI Foundation 80B A3B Base run on mini PCs, Jetson, or NPU devices?
These edge and NPU devices from our database have enough memory for AliceAI Foundation 80B A3B Base at Q4_K_M. Their memory bandwidth is far below discrete GPUs, so expect a fraction of desktop generation speed.
| Device | Memory | Bandwidth | Type |
|---|---|---|---|
| Jetson Orin Nano 8GB Super (Dev Kit) | 8 GB | 102 GB/s | Edge Compute Device |
| NVIDIA Jetson Orin Nano 2 | 8 GB | — | Edge Compute Device |
| Jetson Orin NX 16GB Super | 16 GB | 102 GB/s | Edge Compute Device |
| Intel Core Ultra 9 288V (Lunar Lake) | 32 GB | 136 GB/s | NPU Chip |
| Jetson AGX Orin 32GB | 32 GB | 204 GB/s | Edge Compute Device |
| Jetson AGX Orin 64GB | 64 GB | 204 GB/s | Edge Compute Device |
| Snapdragon X Elite (X1E-84-100) | 64 GB | 135 GB/s | NPU Chip |
| AMD Ryzen AI 9 HX 370 (Strix Point) | 96 GB | 120 GB/s | NPU Chip |
| NVIDIA Jetson AGX Thor Developer Kit | 128 GB | — | Edge Compute Device |
| NVIDIA RTX Spark (superchip) | 128 GB | — | NPU Chip |
| NVIDIA RTX Spark N1X (desktop superchip) | 128 GB | — | NPU Chip |
Which pre-built systems can run AliceAI Foundation 80B A3B Base?
These mini PCs and workstations from our database fit AliceAI Foundation 80B A3B Base at Q4_K_M. Usable-memory figures are conservative: Windows shares about half of system RAM with the GPU by default (Linux can expose more), macOS lets Apple Silicon GPUs use about 75% of unified memory, and Linux unified-memory systems such as GB10 expose roughly 90%. Generation speed is bound by memory bandwidth, so compare the GB/s column before buying.
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