Baichuan2 13B Chat — Hardware Requirements & GPU Compatibility
Chat
Baichuan2-13B-Chat is Baichuan Intelligence's 13-billion-parameter bilingual (Chinese/English) chat model, instruction-aligned from the Baichuan2-13B-Base model that was pretrained from scratch on 2.6 trillion tokens. It was evaluated across general, legal, medical, math, code, and multilingual-translation benchmarks against contemporaries like LLaMA2-13B-Chat and Vicuna-13B, and a 4-bit quantized version is also distributed for lower-memory deployment. At 13B parameters it needs a capable consumer GPU at full precision, considerably less once quantized to 4 bits. License is a custom Baichuan2 Community License: free for academic research, and free for commercial use after obtaining a license via email request to the developers. It was published in August 2023, with a v2 revision issued in December 2023 that improved math, logical reasoning, and instruction-following.
8.0K downloads
434 likesSpecifications
- Publisher
- baichuan-inc
- Family
- Baichuan
- Parameters
- 13B
- Architecture
- BaichuanForCausalLM
- Vocabulary Size
- 125,696
- Release Date
- 2023-08-29
- License
- Other
Get Started
HuggingFace
How Much VRAM Does Baichuan2 13B Chat Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 6.1 GB | — | 5.53 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 6.3 GB | — | 5.69 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 7.0 GB | — | 6.34 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 7.2 GB | — | 6.50 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 8.6 GB | — | 7.80 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 10.2 GB | — | 9.26 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 11.8 GB | — | 10.72 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 14.3 GB | — | 13.00 GB | 8-bit quantization, near-lossless |
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 Baichuan2 13B Chat?
Q4_K_M · 8.6 GBBaichuan2 13B Chat (Q4_K_M) requires 8.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 12+ GB is recommended. 40 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Baichuan2 13B Chat?
Q4_K_M · 8.6 GB49 devices with unified memory can run Baichuan2 13B Chat, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, iPad Pro M5 13" (16 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightWhere to Download Baichuan2 13B Chat
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Benchmarks
Benchmark details →Related Models
Frequently Asked Questions
- How much VRAM does Baichuan2 13B Chat need?
Baichuan2 13B Chat requires 8.6 GB of VRAM at Q4_K_M, or 28.6 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 13B × 4.8 bits ÷ 8 = 7.8 GB
KV Cache + Overhead ≈ 0.8 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M8.6 GB- Can NVIDIA GeForce RTX 4090 run Baichuan2 13B Chat?
Yes, at Q8_0 (14.3 GB) or lower. Higher quantizations like BF16 (28.6 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Baichuan2 13B Chat?
For Baichuan2 13B Chat, Q4_K_M (8.6 GB) offers the best balance of quality and VRAM usage. Q5_K_S (9.8 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 3.9 GB.
VRAM requirement by quantization
IQ2_XXS3.9 GBIQ3_XS5.9 GBQ3_K_M7.0 GBQ4_K_M ★8.6 GBQ5_K_S9.8 GBBF1628.6 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Baichuan2 13B Chat on a Mac?
Baichuan2 13B Chat requires at least 3.9 GB at IQ2_XXS, 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 Baichuan2 13B Chat locally?
Yes — Baichuan2 13B Chat can run locally on consumer hardware. At Q4_K_M quantization it needs 8.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Baichuan2 13B Chat?
At Q4_K_M, Baichuan2 13B Chat can reach ~559 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~76 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 ÷ 8.6 × 0.65 = ~606 tok/s
Estimated speed at Q4_K_M (8.6 GB)
~606 tok/s~76 tok/s~606 tok/s~559 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Baichuan2 13B Chat?
At Q4_K_M, the download is about 7.80 GB. The full-precision BF16 version is 26.00 GB. The smallest option (IQ2_XXS) is 3.58 GB.
- Which GPUs can run Baichuan2 13B Chat?
40 consumer GPUs can run Baichuan2 13B Chat at Q4_K_M (8.6 GB). Top options include AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 6900 XT, AMD Radeon RX 6700 XT. 26 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Baichuan2 13B Chat?
52 devices with unified memory can run Baichuan2 13B Chat at Q4_K_M (8.6 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, 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.