GigaChat3.1 Audio 10B A1.8B — Hardware Requirements & GPU Compatibility
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GigaChat3.1 Audio 10B A1.8B is a 10B-parameter open language model from ai-sage. It supports a context window of up to 262,144 tokens. At BF16 it needs about 20.63 GB of VRAM — see which GPUs and Macs can run it below.
53.4K downloads
39 likesSpecifications
- Publisher
- ai-sage
- Parameters
- 10B
- Architecture
- GigaChatAudioForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2026-07-13
- License
- MIT
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HuggingFace
How Much VRAM Does GigaChat3.1 Audio 10B A1.8B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 20.6 GB | 62.2 GB | 20.00 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 GigaChat3.1 Audio 10B A1.8B?
BF16 · 20.6 GBGigaChat3.1 Audio 10B A1.8B (BF16) requires 20.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 27+ GB is recommended. Using the full 262K context window can add up to 41.6 GB, bringing total usage to 62.2 GB. 7 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run GigaChat3.1 Audio 10B A1.8B?
BF16 · 20.6 GB41 devices with unified memory can run GigaChat3.1 Audio 10B A1.8B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightFrequently Asked Questions
- How much VRAM does GigaChat3.1 Audio 10B A1.8B need?
GigaChat3.1 Audio 10B A1.8B requires 20.6 GB of VRAM at BF16. Full 262K context adds up to 41.5 GB (62.2 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 10B × 16 bits ÷ 8 = 20 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 42.2 GB (at full 262K context)
VRAM usage by quantization
BF1620.6 GBBF16 + full context62.2 GB- Can I run GigaChat3.1 Audio 10B A1.8B on a Mac?
GigaChat3.1 Audio 10B A1.8B requires at least 20.6 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 GigaChat3.1 Audio 10B A1.8B locally?
Yes — GigaChat3.1 Audio 10B A1.8B can run locally on consumer hardware. At BF16 quantization it needs 20.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is GigaChat3.1 Audio 10B A1.8B?
At BF16, GigaChat3.1 Audio 10B A1.8B can reach ~213 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~32 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 ÷ 20.6 × 0.65 = ~252 tok/s
Estimated speed at BF16 (20.6 GB)
~252 tok/s~32 tok/s~252 tok/s~213 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of GigaChat3.1 Audio 10B A1.8B?
At BF16, the download is about 20.00 GB.
- Which GPUs can run GigaChat3.1 Audio 10B A1.8B?
7 consumer GPUs can run GigaChat3.1 Audio 10B A1.8B at BF16 (20.6 GB). Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090. 1 GPU have plenty of headroom for comfortable inference.
- Which devices can run GigaChat3.1 Audio 10B A1.8B?
41 devices with unified memory can run GigaChat3.1 Audio 10B A1.8B at BF16 (20.6 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (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.