Yi 1.5 9B Chat — Hardware Requirements & GPU Compatibility
Chat
Yi-1.5-9B-Chat is 01.AI's second-generation 9-billion-parameter bilingual (English/Chinese) chat model, continually pretrained from the original Yi series on a further 500 billion high-quality tokens and then fine-tuned on 3 million diverse instruction samples. Compared with the original Yi, Yi-1.5 delivers stronger coding, math, and reasoning while keeping the same language understanding and commonsense reasoning, and 01.AI reports it as the top performer among similarly sized open models on its benchmark suite. At 9B parameters it fits comfortably on a single consumer GPU, or a much smaller card once quantized. Context length is 4,096 tokens; separate 16K- and 32K-context variants of the same model are also available for longer documents. It is released under the Apache 2.0 license, permitting unrestricted commercial and research use, and was published in May 2024.
20.0K downloads
149 likesSpecifications
- Publisher
- 01.AI
- Family
- Yi 1.5
- Parameters
- 8.8B
- Architecture
- LlamaForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 64,000
- Release Date
- 2024-05-10
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Yi 1.5 9B Chat Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 4.3 GB | 4.5 GB | 3.75 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 4.4 GB | 4.6 GB | 3.86 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 4.8 GB | 5.0 GB | 4.30 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 4.9 GB | 5.1 GB | 4.41 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 5.8 GB | 6 GB | 5.30 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 6.8 GB | 7.0 GB | 6.29 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 7.8 GB | 8.0 GB | 7.28 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 9.3 GB | 9.5 GB | 8.83 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 Yi 1.5 9B Chat?
Q4_K_M · 5.8 GBYi 1.5 9B Chat (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.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Yi 1.5 9B Chat?
Q4_K_M · 5.8 GB58 devices with unified memory can run Yi 1.5 9B Chat, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroomWhere to Download Yi 1.5 9B Chat
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Benchmarks
Benchmark details →Related Models
Frequently Asked Questions
- How much VRAM does Yi 1.5 9B Chat need?
Yi 1.5 9B Chat 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)
KV Cache + Overhead ≈ 0.7 GB (at full 4K context)
VRAM usage by quantization
Q4_K_M5.8 GBQ4_K_M + full context6.0 GB- What's the best quantization for Yi 1.5 9B Chat?
For Yi 1.5 9B Chat, Q4_K_M (5.8 GB) offers the best balance of quality and VRAM usage. Q5_0 (6.0 GB) provides better quality if you have the VRAM. The smallest option is IQ3_XS at 4.1 GB.
VRAM requirement by quantization
IQ3_XS4.1 GBIQ3_M4.5 GBQ4_K_S5.5 GBQ4_K_M ★5.8 GBQ5_K_S6.6 GBBF1618.2 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Yi 1.5 9B Chat on a Mac?
Yi 1.5 9B Chat requires at least 4.1 GB at IQ3_XS, 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 Yi 1.5 9B Chat locally?
Yes — Yi 1.5 9B Chat can run locally on consumer hardware. At Q4_K_M quantization it needs 5.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Yi 1.5 9B Chat?
At Q4_K_M, Yi 1.5 9B Chat 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)
~897 tok/s~113 tok/s~897 tok/s~828 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Yi 1.5 9B Chat?
At Q4_K_M, the download is about 5.30 GB. The full-precision BF16 version is 17.66 GB. The smallest option (IQ3_XS) is 3.64 GB.
- Which GPUs can run Yi 1.5 9B Chat?
52 consumer GPUs can run Yi 1.5 9B Chat at Q4_K_M (5.8 GB). 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 1.5 9B Chat?
59 devices with unified memory can run Yi 1.5 9B Chat at Q4_K_M (5.8 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.