Baichuan2 7B Chat — Hardware Requirements & GPU Compatibility
Chat
Baichuan2 7B Chat is Baichuan Intelligence's 7-billion-parameter chat model, the instruction-tuned version of Baichuan 2 for English and Chinese conversation. The card says the Baichuan 2 series was trained on 2.6 trillion tokens of high-quality data and reports the best results among models of similar size on Chinese and English benchmarks. It is an older general-purpose model, mainly of interest for Chinese-language use and comparison. At this size it runs on a single consumer GPU once quantized, and a 4-bit build fits modest cards. The context length is 4,096 tokens, short by current standards. Weights are released under the Baichuan 2 Community License together with Apache 2.0 for the code. Research use is open, but commercial use requires an emailed application and approval, and the card limits it to entities with under 1 million daily active users that are not software or cloud service providers. It was published in August 2023, alongside 13B and 4-bit quantized versions.
42.4K downloads
172 likesSpecifications
- Publisher
- baichuan-inc
- Family
- Baichuan
- Parameters
- 7B
- Architecture
- BaichuanForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 125,696
- Release Date
- 2023-08-29
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HuggingFace
How Much VRAM Does Baichuan2 7B Chat Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 3.3 GB | — | 2.98 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 3.4 GB | — | 3.06 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 3.8 GB | — | 3.41 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 3.9 GB | — | 3.50 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 4.6 GB | — | 4.20 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 5.5 GB | — | 4.99 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 6.3 GB | — | 5.78 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 7.7 GB | — | 7.00 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 7B Chat?
Q4_K_M · 4.6 GBBaichuan2 7B Chat (Q4_K_M) requires 4.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 7+ GB is recommended. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Baichuan2 7B Chat?
Q4_K_M · 4.6 GB59 devices with unified memory can run Baichuan2 7B Chat, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, iPhone 17.
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightWhere to Download Baichuan2 7B Chat
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
Frequently Asked Questions
- How much VRAM does Baichuan2 7B Chat need?
Baichuan2 7B Chat requires 4.6 GB of VRAM at Q4_K_M, or 15.4 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 7B × 4.8 bits ÷ 8 = 4.2 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M4.6 GB- What's the best quantization for Baichuan2 7B Chat?
For Baichuan2 7B Chat, Q4_K_M (4.6 GB) offers the best balance of quality and VRAM usage. Q5_K_S (5.3 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 3.3 GB.
VRAM requirement by quantization
Q2_K3.3 GBQ4_03.9 GBQ4_K_S4.3 GBQ4_K_M ★4.6 GBQ5_K_M5.5 GBBF1615.4 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Baichuan2 7B Chat on a Mac?
Baichuan2 7B Chat requires at least 3.3 GB at Q2_K, 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 7B Chat locally?
Yes — Baichuan2 7B Chat can run locally on consumer hardware. At Q4_K_M quantization it needs 4.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Baichuan2 7B Chat?
At Q4_K_M, Baichuan2 7B Chat can reach ~1039 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~142 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 ÷ 4.6 × 0.65 = ~1126 tok/s
Estimated speed at Q4_K_M (4.6 GB)
~1126 tok/s~142 tok/s~1126 tok/s~1039 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Baichuan2 7B Chat?
At Q4_K_M, the download is about 4.20 GB. The full-precision BF16 version is 14.00 GB. The smallest option (Q2_K) is 2.98 GB.
- Which GPUs can run Baichuan2 7B Chat?
52 consumer GPUs can run Baichuan2 7B Chat at Q4_K_M (4.6 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Baichuan2 7B Chat?
59 devices with unified memory can run Baichuan2 7B Chat at Q4_K_M (4.6 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.