Baichuan 13B Base — Hardware Requirements & GPU Compatibility
Chat
Baichuan-13B-Base is Baichuan Intelligence's 13-billion-parameter pretrained base model, the follow-up to Baichuan-7B, trained on 1.4 trillion tokens of bilingual Chinese/English data, about 40% more than LLaMA-13B at the time. It replaces the usual rotary position embeddings with ALiBi linear position biasing, which the authors report gives roughly 31.6% faster token generation than a comparable LLaMA-13B. Official int8 and int4 quantized versions are provided so the model deploys on consumer GPUs such as the RTX 3090 with little accuracy loss. A separately released Baichuan-13B-Chat provides the aligned, conversational counterpart to this base checkpoint. Context length is 4,096 tokens. It is released under a custom Community License for the Baichuan-13B Model: free for academic research, with commercial use requiring written authorization from Baichuan via email request. It was published in July 2023, an early bilingual open model that predates the Baichuan2 series.
Runs great on a 24 GB GPU
Runs from 12 GB (Q4_K_M · 16K)Best on 24 GB (Q4_K_M · ~76 tok/s · 32K)Macs from 16 GB (Q4_K_M · ~16 tok/s)
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Baichuan 13B 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
ollama run hf.co/tensorblock/baichuan-inc_Baichuan-13B-Base-GGUFQ4_K_M fits fully on the RTX 4090 · 12.5 GB free · 16K context
Download: baichuan-inc_Baichuan-13B-Base-Q4_K_M.gguf7.80 GBtensorblock
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 5070 Ti (16 GB) (4 GB headroom) · Q4_K_M
- Generation speed
- ~68 tok/s
- generation speed
- Cost per 1M output tokens
- $0.69
- per 1M output tokens
How Much VRAM Does Baichuan 13B Base Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 6.1 GB | 12.4 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~108 tok/s | |
| 7.0 GB | 13.3 GB | 3-bit medium — 3-bit medium quantization | Fits: 32K context · ~94 tok/s | |
| 8.6 GB | 14.9 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 32K context · ~76 tok/s | |
| 10.2 GB | 16.5 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 32K context · ~64 tok/s | |
| 11.8 GB | 18.1 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 32K context · ~56 tok/s | |
| 14.3 GB | 20.6 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 32K context · ~46 tok/s | |
| 28.6 GB | 34.9 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Does not fit: needs 31.5 GB |
Selected Q4_K_M
4-bit medium quantization11.5GBat 16K context
- Weights
- 7.9 GB
- KV cache
- 3.4 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 12.5 GB free · ~76 tok/s
≥50 tok/s needs ≥659 GB/s · RTX 5090 ~136, RTX 3090 Ti ~76
Which Hardware Can Run Baichuan 13B Base?
Q4_K_M · 8.6 GBBaichuan 13B Base (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. 150 devices with unified memory can run Baichuan 13B Base, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Mini M2 Pro (16 GB).
12 GB
12 GB tier →fits · 0.5 GB free · up to 16K ctx
10 GPUs
RTX 3080 Ti~69Simulate output on RTX 3080 TiRTX 5070~51Simulate output on RTX 5070RTX 4070~38Simulate output on RTX 407020 GB
fits · 8.5 GB free · up to 32K ctx
1 GPU
RX 7900 XT~56Simulate output on RX 7900 XT32–40 GB
fits · 20.5 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~136Simulate output on RTX 5090M5 Max36 GB~38Simulate output on Mac Studio M5 Max (36 GB)M532 GB~13Simulate output on MacBook Air 13" M5 (32 GB)Orin~16Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~10Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
How good is Baichuan 13B 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 3 boards that have results for this model, sorted by their weight in the llmrun Score; badges show who ran the test.
- MMLU
Epoch AI · Jul 2023Lab-reported
Lab-reported51.6%
#60/85 open
- BIG-Bench Hard
Epoch AI · Jul 2023Lab-reported
Lab-reported43.0%
#25/38 open
- GSM8K
Epoch AI · Jul 2023Lab-reported
Lab-reported26.8%
#48/62 open
Alternatives to Baichuan 13B Base
Baichuan family · 6 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Gemma 3 12B ITGoogle | 74.2 | 10.9 GB | ~82 | same size |
| Mistral Nemo Base 2407Mistral AI | — | 11 GB | ~81 | same size |
| Phi 3 Medium 128k InstructMicrosoft | — | 12 GB | ~72 | same size |
| Llama 2 13B Chat HFMeta | — | 11.5 GB | ~76 | same size |
| Vicuna 13B V1.3LMSYS | — | 8.6 GB | ~76 | same size |
| Vllm Translategemma 12B ITchbae624 | — | 9.8 GB | ~73 | same size |
Specifications
- Publisher
- baichuan-inc
- Family
- Baichuan
- Parameters
- 13B
- Architecture
- BaichuanForCausalLM
- Vocabulary Size
- 64,000
- Release Date
- 2023-07-08
Frequently Asked Questions
How much VRAM does Baichuan 13B Base need?
Baichuan 13B Base 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)
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
Can NVIDIA GeForce RTX 4090 run Baichuan 13B Base?
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 Baichuan 13B Base?
For Baichuan 13B Base, Q4_K_M (8.6 GB) offers the best balance of quality and VRAM usage. Q5_K_M (10.2 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 6.1 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Baichuan 13B Base on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run Baichuan 13B Base. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 8.6 GB of usable unified memory (RAM minus macOS overhead).
Can I run Baichuan 13B Base locally?
Yes — Baichuan 13B Base can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Baichuan 13B Base?
At Q4_K_M, Baichuan 13B Base 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 ÷ 8.58 × 0.65 = ~606 tok/s
Estimated speed at Q4_K_M (8.6 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Baichuan 13B Base?
At Q4_K_M, the download is about 7.80 GB. The full-precision BF16 version is 26.00 GB. The smallest option (Q2_K) is 5.53 GB.
Which GPUs can run Baichuan 13B Base?
40 consumer GPUs can run Baichuan 13B Base at Q4_K_M. 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 Baichuan 13B Base?
150 devices with unified memory can run Baichuan 13B Base at Q4_K_M (8.6 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.