Yi 6B — Hardware Requirements & GPU Compatibility
Chat
Yi-6B is 01.AI's 6.1-billion-parameter base language model — pretrained, not instruction-tuned — from the first Yi series, one of the company's earliest large bilingual (English/Chinese) open models trained from scratch. It was trained on roughly 3 trillion multilingual tokens and is positioned as a general-purpose foundation for fine-tuning rather than direct chat use; a separate Yi-6B-Chat release and a long-context Yi-6B-200K variant followed later. As a 6-billion-parameter dense model, it fits easily on a single consumer GPU. Context length is 4,096 tokens. It is released under the Apache 2.0 license, permitting unrestricted commercial and research use, and was published in November 2023, making it one of the older entries in the now much-expanded Yi model family.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 4K)Best on 24 GB (Q4_K_M · ~161 tok/s · 4K)Macs from 8 GB (Q4_K_M · ~18 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Yi 6B
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/mradermacher/Yi-6B-i1-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 19.8 GB free · 4K context
Download: Yi-6B.i1-Q4_K_M.gguf3.67 GBmradermacher
Ways to runOther ways
each opens step-by-stepRun in cloud
Your own private copy · Q4_K_M · 24 GB GPU
- The easiest way to try this model
- Running in a few minutes, easy setup
- Chat right here or connect any app
$5+ bonus for new users
Runs on RTX 3080 Ti (12 GB) (7 GB headroom) · Q4_K_M
- Generation speed
- ~146 tok/s
- generation speed
- Cost per 1M output tokens
- $0.23
- per 1M output tokens
How Much VRAM Does Yi 6B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 3.0 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 4K context · ~218 tok/s | |
| 3.1 GB | — | 3-bit small — 3-bit small quantization | Fits: 4K context · ~212 tok/s | |
| 3.4 GB | — | 3-bit medium — 3-bit medium quantization | Fits: 4K context · ~193 tok/s | |
| 3.5 GB | — | 4-bit · legacy — 4-bit legacy quantization | Fits: 4K context · ~189 tok/s | |
| 4.1 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 4K context · ~161 tok/s | |
| 4.8 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 4K context · ~138 tok/s | |
| 5.4 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Fits: 4K context · ~121 tok/s | |
| 6.5 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 4K context · ~101 tok/s |
Selected Q4_K_M
4-bit medium quantization4.2GBat 4K context
- Weights
- 3.6 GB
- KV cache
- 0.3 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 19.8 GB free · ~161 tok/s
≥50 tok/s needs ≥313 GB/s · RTX 5090 ~286, RTX 3090 Ti ~161
Which Hardware Can Run Yi 6B?
Q4_K_M · 4.1 GBYi 6B (Q4_K_M) requires 4.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 6+ GB is recommended. Using the full 4K context window can add up to 0.1 GB, bringing total usage to 4.2 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run Yi 6B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), MacBook Air 13" M3 (8 GB).
≤ 11 GB
≤ 11 GB tier →fits · 3.8 GB free · up to 4K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~121Simulate output on RTX 3080RTX 2080 Ti~98Simulate output on RTX 2080 TiRTX 3070 Ti~97Simulate output on RTX 3070 TiM3*8 GB~18Simulate output on iMac 24" M3 (8 GB)Orin Nano~16Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~12Simulate output on iPhone 1720 GB
fits · 15.8 GB free · up to 4K ctx
1 GPU
RX 7900 XT~118Simulate output on RX 7900 XT32–40 GB
fits · 27.8 GB free · up to 4K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~286Simulate output on RTX 5090M5 Max36 GB~79Simulate output on Mac Studio M5 Max (36 GB)M532 GB~26Simulate output on MacBook Air 13" M5 (32 GB)Orin~33Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~22Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
How good is Yi 6B?
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
The 6 boards that weigh most in the llmrun Score, of 7 with a result for this model; badges show who ran the test.
- MMLU-Pro
TIGER-LabIndependent
Independent26.5%
#124/146 open
- MMLU
Epoch AI · Nov 2023Lab-reported
Lab-reported64.0%
#41/85 open
- BIG-Bench Hard
Epoch AI · Nov 2023Lab-reported
Lab-reported42.8%
#26/38 open
- GSM8K
Epoch AI · Nov 2023Lab-reported
Lab-reported32.5%
#45/62 open
- HellaSwag
Epoch AI · Nov 2023Lab-reported
Lab-reported74.4%
#25/47 open
- ARC Challenge
Epoch AI · Nov 2023Lab-reported
Lab-reported50.3%
#31/56 open
Alternatives to Yi 6B
Yi family · 6 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Chatglm3 6BZ.ai | — | 5.2 GB | ~159 | same size |
| TLive Omni 4BTaoLiveAIGC | — | 13.3 GB | ~54 | same size |
| Z Image TurboTongyi-MAI | — | 15.9 GB | ~48 | same size |
| GPT J 6BEleutherAI | — | 4 GB | ~166 | same size |
| Ko PlatYi 6Bkyujinpy | — | 4 GB | ~163 | same size |
| Yi 1.5 6B Chat01.AI | — | 4.2 GB | ~161 | same size |
Specifications
- Publisher
- 01.AI
- Family
- Yi
- Parameters
- 6.1B
- Architecture
- LlamaForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 64,000
- Release Date
- 2023-11-01
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does Yi 6B need?
Yi 6B requires 4.1 GB of VRAM at Q4_K_M, or 12.6 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 6.1B × 4.8 bits ÷ 8 = 3.6 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 ≈ 0.6 GB (at full 4K context)
VRAM usage by quantization
What's the best quantization for Yi 6B?
For Yi 6B, Q4_K_M (4.1 GB) offers the best balance of quality and VRAM usage. Q5_0 (4.2 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 2.1 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Yi 6B on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Yi 6B. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 4.1 GB of usable unified memory (RAM minus macOS overhead).
Can I run Yi 6B locally?
Yes — Yi 6B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Yi 6B?
At Q4_K_M, Yi 6B can reach ~1179 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~161 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 ÷ 4.07 × 0.65 = ~1278 tok/s
Estimated speed at Q4_K_M (4.1 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Yi 6B?
At Q4_K_M, the download is about 3.64 GB. The full-precision BF16 version is 12.12 GB. The smallest option (IQ2_XXS) is 1.67 GB.
Which GPUs can run Yi 6B?
52 consumer GPUs can run Yi 6B 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 Yi 6B?
163 devices with unified memory can run Yi 6B at Q4_K_M (4.1 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.