GigaChat 20B A3B Base — Hardware Requirements & GPU Compatibility
Chat
GigaChat 20B A3B Base is a 20B-parameter open language model from ai-sage. It supports a context window of up to 131,072 tokens. At Q4_K_M it needs about 12.53 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 12 GB (Q3_K_M · 16K)Best on 24 GB (Q4_K_M · ~239 tok/s · 64K)Macs from 16 GB (Q3_K_M · ~64 tok/s)
MIT
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run GigaChat 20B A3B Base
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
vllm serve ai-sage/GigaChat-20B-A3B-base --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 3090 (24 GB) (9 GB headroom) · Q4_K_M
- Generation speed
- ~228 tok/s
- generation speed
- Cost per 1M output tokens
- $0.24
- per 1M output tokens
How Much VRAM Does GigaChat 20B A3B Base Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 9.0 GB | 12.6 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 128K context · ~291 tok/s | |
| 10.3 GB | 13.8 GB | 3-bit medium — 3-bit medium quantization | Fits: 64K context · ~270 tok/s | |
| 12.5 GB | 16.1 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 64K context · ~239 tok/s | |
| 14.8 GB | 18.3 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 64K context · ~215 tok/s | |
| 17.0 GB | 20.6 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 32K context · ~195 tok/s | |
| 20.5 GB | 24.1 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 16K context · ~171 tok/s | |
| 40.5 GB | 44.1 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Does not fit: needs 42.2 GB |
Selected Q4_K_M
4-bit medium quantization14.2GBat 16K context
- Weights
- 12.0 GB
- KV cache
- 1.9 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 9.8 GB free · ~239 tok/s
≥50 tok/s needs ≥144 GB/s · RTX 5090 ~331, RTX 3090 Ti ~239
Which Hardware Can Run GigaChat 20B A3B Base?
Q4_K_M · 12.5 GBGigaChat 20B A3B Base (Q4_K_M) requires 12.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 17+ GB is recommended. Using the full 131K context window can add up to 14.8 GB, bringing total usage to 27.3 GB. 26 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 5080. 125 devices with unified memory can run GigaChat 20B A3B Base, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), MacBook Pro 14" M3 Pro (18 GB).
16–18 GB
16–18 GB tier →fits · 1.8 GB free · up to 16K ctx
18 GPUs · 1 Mac class · 1 device family
RTX 5080~232Simulate output on RTX 5080RTX 5070 Ti~222Simulate output on RTX 5070 TiRTX 4080 SUPER~194Simulate output on RTX 4080 SUPERM3 Pro*18 GB~45Simulate output on MacBook Pro 14" M3 Pro (18 GB)Orin NX~36Simulate output on NVIDIA Jetson Orin NX 16GB20 GB
fits · 5.8 GB free · up to 64K ctx
1 GPU
RX 7900 XT~109Simulate output on RX 7900 XT32–40 GB
fits · 17.8 GB free · up to 128K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~331Simulate output on RTX 5090M5 Max36 GB~91Simulate output on Mac Studio M5 Max (36 GB)M532 GB~46Simulate output on MacBook Air 13" M5 (32 GB)Orin~69Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~45Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Similar models
3| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| InternVL3 5 GPT OSS 20B A4B Preview HFOpenGVLab | — | 13.6 GB | ~180 | same size |
| Internlm 20BInternLM | — | 45.3 GB | —needs > 24 GB | same size |
| ERNIE 4.5 21B A3B PTBaidu | — | 13.8 GB | ~254 | same size |
Specifications
- Publisher
- ai-sage
- Parameters
- 20B
- Active parameters
- 2.8B per token
- Architecture
- DeepseekForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2024-12-13
- License
- MIT
Frequently Asked Questions
How much VRAM does GigaChat 20B A3B Base need?
GigaChat 20B A3B Base requires 12.5 GB of VRAM at Q4_K_M, or 40.5 GB at BF16. Full 131K context adds up to 14.8 GB (27.3 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 20B × 4.8 bits ÷ 8 = 12 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 15.3 GB (at full 131K context)
VRAM usage by quantization
Can NVIDIA GeForce RTX 4090 run GigaChat 20B A3B Base?
Yes, at Q8_0 (20.5 GB) or lower. Higher quantizations like BF16 (40.5 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
What's the best quantization for GigaChat 20B A3B Base?
For GigaChat 20B A3B Base, Q4_K_M (12.5 GB) offers the best balance of quality and VRAM usage. Q5_K_M (14.8 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 9.0 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run GigaChat 20B A3B Base on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run GigaChat 20B A3B Base, the smallest at Q3_K_M. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 12.5 GB of usable unified memory (RAM minus macOS overhead), from MacBook Pro 14" M3 Pro (18 GB).
Can I run GigaChat 20B A3B Base locally?
Yes — GigaChat 20B A3B Base can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is GigaChat 20B A3B Base?
At Q4_K_M, GigaChat 20B A3B Base can reach ~168 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~239 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = 1000 ÷ (active GB ÷ (bandwidth GB/s × efficiency) × 1000 + layers × routing ms)
Mixture-of-Experts: just the active experts get read for each token, along with a fixed routing cost per layer.
Example: NVIDIA B200 → 1.7 GB active ÷ (8000 × 0.65) = 0.332 ms, plus 28 layers × 0.055 ms = 1.540 ms, so 1000 ÷ 1.872 ms = ~534 tok/s
Estimated speed at Q4_K_M (12.5 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of GigaChat 20B A3B Base?
At Q4_K_M, the download is about 12.00 GB. The full-precision BF16 version is 40.00 GB. The smallest option (Q2_K) is 8.50 GB.
Which GPUs can run GigaChat 20B A3B Base?
26 consumer GPUs can run GigaChat 20B A3B Base at Q4_K_M. Top options include AMD Radeon RX 7900 XT, AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090, AMD Radeon RX 6800. 8 GPUs have plenty of headroom for comfortable inference.
Which devices can run GigaChat 20B A3B Base?
125 devices with unified memory can run GigaChat 20B A3B Base at Q4_K_M (12.5 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, 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.