Baichuan2 7B Base — Hardware Requirements & GPU Compatibility
Chat
Baichuan2-7B-Base is Baichuan Intelligence's second-generation 7-billion-parameter pretrained base model, a bilingual English/Chinese language model, not instruction-tuned, trained on 2.6 trillion tokens of high-quality data, up from 1.2 trillion for the original Baichuan-7B. It reports the best results among same-size open models on Chinese and English benchmarks including C-Eval, MMLU, CMMLU, and BBH. A separately released Baichuan2-7B-Chat provides the aligned, conversational counterpart to this base checkpoint, along with a 4-bit quantized chat variant. At 7 billion parameters it runs easily on a single consumer GPU. Context length is 4,096 tokens. It is released under a custom Baichuan2 Community License plus Apache 2.0 for the code: free for research, and free for commercial use only for organizations with under 1 million daily active users that are not themselves software or cloud service providers, subject to a written authorization request. It was published in August 2023.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 4K)Best on 24 GB (Q4_K_M · ~142 tok/s · 4K)Macs from 8 GB (Q3_K_M · ~19 tok/s)
Other
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Baichuan2 7B 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 baichuan-inc/Baichuan2-7B-Base --max-model-len 4096vLLM repo with its own quantisation
Download: Weights on Hugging Face
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 3080 Ti (12 GB) (7 GB headroom) · Q4_K_M
- Generation speed
- ~128 tok/s
- generation speed
- Cost per 1M output tokens
- $0.26
- per 1M output tokens
How Much VRAM Does Baichuan2 7B Base Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 3.3 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 4K context · ~200 tok/s | |
| 3.8 GB | — | 3-bit medium — 3-bit medium quantization | Fits: 4K context · ~175 tok/s | |
| 4.6 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 4K context · ~142 tok/s | |
| 5.5 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 4K context · ~119 tok/s | |
| 6.3 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Fits: 4K context · ~103 tok/s | |
| 7.7 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 4K context · ~85 tok/s | |
| 15.4 GB | — | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 4K context · ~43 tok/s |
Selected Q4_K_M
4-bit medium quantization5.0GBat 4K context
- Weights
- 4.0 GB
- KV cache
- 0.7 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 19.0 GB free · ~142 tok/s
≥50 tok/s needs ≥355 GB/s · RTX 5090 ~252, RTX 3090 Ti ~142
Which Hardware Can Run Baichuan2 7B Base?
Q4_K_M · 4.6 GBBaichuan2 7B Base (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. 151 devices with unified memory can run Baichuan2 7B Base, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 3.0 GB free · up to 4K ctx
16 GPUs · 2 device families
RTX 3080~107Simulate output on RTX 3080RTX 2080 Ti~87Simulate output on RTX 2080 TiRTX 3070 Ti~86Simulate output on RTX 3070 TiOrin Nano~14Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~10Simulate output on iPhone 1720 GB
fits · 15.0 GB free · up to 4K ctx
1 GPU
RX 7900 XT~104Simulate output on RX 7900 XT32–40 GB
fits · 27.0 GB free · up to 4K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~252Simulate output on RTX 5090M5 Max36 GB~70Simulate output on Mac Studio M5 Max (36 GB)M532 GB~23Simulate output on MacBook Air 13" M5 (32 GB)Orin~29Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~19Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
How good is Baichuan2 7B Base?
llmrun Score blends public benchmark boards into one number; 100 = the average open model.
Not enough data yet for an llmrun Score.
A Score needs results on enough independent boards. How the Composite Score is built
Benchmarks
Here are all 6 boards that have results for this model, sorted by their weight in the llmrun Score; badges show who ran the test.
- MMLU
Epoch AI · Sep 2023Lab-reported
Lab-reported54.2%
#58/85 open
- BIG-Bench Hard
Epoch AI · Sep 2023Lab-reported
Lab-reported41.6%
#28/38 open
- GSM8K
Epoch AI · Sep 2023Lab-reported
Lab-reported24.6%
#50/62 open
- HellaSwag
Epoch AI · Sep 2023Lab-reported
Lab-reported68.0%
#37/47 open
- ARC Challenge
Epoch AI · Sep 2023Lab-reported
Lab-reported32.5%
#52/56 open
- BoolQ
Epoch AI · Sep 2023Lab-reported
Lab-reported63.2%
#34/38 open
Alternatives to Baichuan2 7B Base
Baichuan family · 6 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Olmo 3 7B InstructAllen AI | — | 8.4 GB | ~114 | same size |
| Mathstral 7B v0.1Mistral AI | — | 6.8 GB | ~133 | same size |
| Mistral 7B Instruct v0.3Mistral AI | — | 6.8 GB | ~133 | same size |
| Phi 3 Small 8k InstructMicrosoft | — | 16.2 GB | ~43 | same size |
| CodeQwen1.5 7BAlibaba | — | 5.7 GB | ~137 | same size |
| Starling LM 7B BetaNexusflow | — | 5.7 GB | ~133 | same size |
Specifications
- Publisher
- baichuan-inc
- Family
- Baichuan
- Parameters
- 7B
- Architecture
- BaichuanForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 125,696
- Release Date
- 2023-08-30
- License
- Other
Frequently Asked Questions
How much VRAM does Baichuan2 7B Base need?
Baichuan2 7B Base 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)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
VRAM usage by quantization
What's the best quantization for Baichuan2 7B Base?
For Baichuan2 7B Base, Q4_K_M (4.6 GB) offers the best balance of quality and VRAM usage. Q5_K_M (5.5 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 3.3 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Baichuan2 7B Base on a Mac?
Yes — Mac Mini M2 (8 GB) and 145 other Macs can run Baichuan2 7B 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 4.6 GB of usable unified memory (RAM minus macOS overhead), from Mac Mini M2 Pro (16 GB).
Can I run Baichuan2 7B Base locally?
Yes — Baichuan2 7B Base can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Baichuan2 7B Base?
At Q4_K_M, Baichuan2 7B Base 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 ÷ 4.62 × 0.65 = ~1126 tok/s
Estimated speed at Q4_K_M (4.6 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Baichuan2 7B Base?
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 Base?
52 consumer GPUs can run Baichuan2 7B Base at Q4_K_M. 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 Base?
151 devices with unified memory can run Baichuan2 7B Base at Q4_K_M (4.6 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.