GigaChat3 10B A1.8B Base — Hardware Requirements & GPU Compatibility
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GigaChat3 10B A1.8B Base is a 11.5B-parameter open language model from ai-sage. It supports a context window of up to 262,144 tokens. At BF16 it needs about 23.59 GB of VRAM — see which GPUs and Macs can run it below.
762 downloads
10 likesSpecifications
- Publisher
- ai-sage
- Parameters
- 11.5B
- Architecture
- DeepseekV3ForCausalLM
- Context Length
- 262,144 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2025-11-22
- License
- MIT
Get Started
HuggingFace
How Much VRAM Does GigaChat3 10B A1.8B Base Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 23.6 GB | 65.1 GB | 22.96 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run GigaChat3 10B A1.8B Base?
BF16 · 23.6 GBGigaChat3 10B A1.8B Base (BF16) requires 23.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 31+ GB is recommended. Using the full 262K context window can add up to 41.6 GB, bringing total usage to 65.1 GB. 5 GPUs can run it, including NVIDIA GeForce RTX 5090.
All compatible consumer-level GPUs are running near their VRAM limit. You may also want to consider professional GPUs (e.g., NVIDIA A100, H100) which offer significantly more VRAM. For more headroom and better throughput, consider a multi-GPU configuration with tensor parallelism (supported by tools like vLLM, llama.cpp, or text-generation-inference).
Which Devices Can Run GigaChat3 10B A1.8B Base?
BF16 · 23.6 GB21 devices with unified memory can run GigaChat3 10B A1.8B Base, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does GigaChat3 10B A1.8B Base need?
GigaChat3 10B A1.8B Base requires 23.6 GB of VRAM at BF16. Full 262K context adds up to 41.5 GB (65.1 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 11.5B × 16 bits ÷ 8 = 23 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 42.1 GB (at full 262K context)
VRAM usage by quantization
BF1623.6 GBBF16 + full context65.1 GB- Can I run GigaChat3 10B A1.8B Base on a Mac?
GigaChat3 10B A1.8B Base requires at least 23.6 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 10B A1.8B Base locally?
Yes — GigaChat3 10B A1.8B Base can run locally on consumer hardware. At BF16 quantization it needs 23.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is GigaChat3 10B A1.8B Base?
At BF16, GigaChat3 10B A1.8B Base can reach ~124 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~28 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 ÷ 23.6 × 0.55 = ~124 tok/s
Estimated speed at BF16 (23.6 GB)
~124 tok/s~28 tok/s~92 tok/s~76 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of GigaChat3 10B A1.8B Base?
At BF16, the download is about 22.96 GB.
- Which GPUs can run GigaChat3 10B A1.8B Base?
5 consumer GPUs can run GigaChat3 10B A1.8B Base at BF16 (23.6 GB). Top options include AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090.
- Which devices can run GigaChat3 10B A1.8B Base?
21 devices with unified memory can run GigaChat3 10B A1.8B Base at BF16 (23.6 GB), including Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.