Yi 9B — Hardware Requirements & GPU Compatibility
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Yi-9B is 01.AI's 8.8-billion-parameter base language model, continuously pretrained from Yi-6B on an additional 0.8 trillion tokens as part of the bilingual English/Chinese Yi series (trained on 3 trillion tokens overall). It is a pretrained model, not instruction-tuned; a separate long-context Yi-9B-200K variant exists for extended-context use. The card reports it as the strongest model in its size class among Mistral-7B, SOLAR-10.7B, Gemma-7B, and DeepSeek-Coder-7B-Base, particularly in code, math, common-sense reasoning, and reading comprehension. It fits 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 March 2024.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 4K)Best on 24 GB (Q4_K_M · ~113 tok/s · 4K)Macs from 8 GB (IQ3_S · ~17 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Yi 9B
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-9B-i1-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 18.0 GB free · 4K context
Download: Yi-9B.i1-Q4_K_M.gguf5.33 GBmradermacher
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 3080 Ti (12 GB) (6 GB headroom) · Q4_K_M
- Generation speed
- ~102 tok/s
- generation speed
- Cost per 1M output tokens
- $0.33
- per 1M output tokens
How Much VRAM Does Yi 9B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 4.3 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 4K context · ~154 tok/s | |
| 4.4 GB | — | 3-bit small — 3-bit small quantization | Fits: 4K context · ~150 tok/s | |
| 4.8 GB | — | 3-bit medium — 3-bit medium quantization | Fits: 4K context · ~136 tok/s | |
| 4.9 GB | — | 4-bit · legacy — 4-bit legacy quantization | Fits: 4K context · ~133 tok/s | |
| 5.8 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 4K context · ~113 tok/s | |
| 6.8 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 4K context · ~97 tok/s | |
| 7.8 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Fits: 4K context · ~84 tok/s | |
| 9.3 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 4K context · ~70 tok/s |
Selected Q4_K_M
4-bit medium quantization6.0GBat 4K context
- Weights
- 5.3 GB
- KV cache
- 0.4 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 18.0 GB free · ~113 tok/s
≥50 tok/s needs ≥446 GB/s · RTX 5090 ~201, RTX 3090 Ti ~113
Which Hardware Can Run Yi 9B?
Q4_K_M · 5.8 GBYi 9B (Q4_K_M) requires 5.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 8+ GB is recommended. Using the full 4K context window can add up to 0.2 GB, bringing total usage to 6 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti. 151 devices with unified memory can run Yi 9B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), NVIDIA Jetson Orin Nano 8GB (Super).
≤ 11 GB
≤ 11 GB tier →fits · 2.0 GB free · up to 4K ctx
16 GPUs · 1 device family
RTX 3080~85Simulate output on RTX 3080RTX 2080 Ti~69Simulate output on RTX 2080 TiRTX 3070 Ti~68Simulate output on RTX 3070 TiOrin Nano~11Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)20 GB
fits · 14.0 GB free · up to 4K ctx
1 GPU
RX 7900 XT~83Simulate output on RX 7900 XT32–40 GB
fits · 26.0 GB free · up to 4K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~201Simulate output on RTX 5090M5 Max36 GB~56Simulate output on Mac Studio M5 Max (36 GB)M532 GB~19Simulate output on MacBook Air 13" M5 (32 GB)Orin~23Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~15Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
How good is Yi 9B?
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 4 boards that have results for this model, sorted by their weight in the llmrun Score; badges show who ran the test.
- MMLU
Epoch AI · Mar 2024Lab-reported
Lab-reported68.4%
#34/85 open
- HellaSwag
Epoch AI · Mar 2024Lab-reported
Lab-reported76.4%
#23/47 open
- ARC Challenge
Epoch AI · Mar 2024Lab-reported
Lab-reported55.6%
#27/56 open
- WinoGrande
Epoch AI · Mar 2024Lab-reported
Lab-reported73.0%
#20/51 open
Alternatives to Yi 9B
Yi family · 6 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Granite Switch 4.1 8BIBM | — | 8.4 GB | ~111 | same size |
| ALoDLM 8BAmazon | — | 7.6 GB | ~119 | same size |
| Llm Jp 4.1 8B Thinkingllm-jp | — | 7.6 GB | ~115 | same size |
| Apertus V1.5 8Bswiss-ai | — | 8.5 GB | ~111 | same size |
| Ornith 1.0 9BDeep Reinforce | — | 6.5 GB | ~106 | same size |
| LFM2.5 8B A1BLiquid AI | — | 5.6 GB | ~335 | same size |
Specifications
- Publisher
- 01.AI
- Family
- Yi
- Parameters
- 8.8B
- Architecture
- LlamaForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 64,000
- Release Date
- 2024-03-01
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does Yi 9B need?
Yi 9B requires 5.8 GB of VRAM at Q4_K_M, or 18.2 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 8.8B × 4.8 bits ÷ 8 = 5.3 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 ≈ 0.7 GB (at full 4K context)
VRAM usage by quantization
What's the best quantization for Yi 9B?
For Yi 9B, Q4_K_M (5.8 GB) offers the best balance of quality and VRAM usage. Q5_K_S (6.6 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 2.9 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Yi 9B on a Mac?
Yes — Mac Mini M2 (8 GB) and 145 other Macs can run Yi 9B, the smallest at IQ3_S. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 5.8 GB of usable unified memory (RAM minus macOS overhead), from Mac Mini M2 Pro (16 GB).
Can I run Yi 9B locally?
Yes — Yi 9B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Yi 9B?
At Q4_K_M, Yi 9B can reach ~828 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~113 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 ÷ 5.8 × 0.65 = ~897 tok/s
Estimated speed at Q4_K_M (5.8 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Yi 9B?
At Q4_K_M, the download is about 5.30 GB. The full-precision BF16 version is 17.66 GB. The smallest option (IQ2_XXS) is 2.43 GB.
Which GPUs can run Yi 9B?
52 consumer GPUs can run Yi 9B at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 40 GPUs have plenty of headroom for comfortable inference.
Which devices can run Yi 9B?
151 devices with unified memory can run Yi 9B at Q4_K_M (5.8 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.