Yi 6B — Hardware Requirements & GPU Compatibility
Chat
Yi 6B is a 6.1B-parameter open language model from 01.AI in the Yi family. It supports a context window of up to 4,096 tokens. At Q4_K_M it needs about 4.07 GB of VRAM — see which GPUs and Macs can run it below.
11.2K downloads
375 likesSpecifications
- Publisher
- 01.AI
- Family
- Yi
- Parameters
- 6.1B
- Architecture
- LlamaForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 64,000
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Yi 6B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 3.0 GB | 3.1 GB | 2.58 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 3.1 GB | 3.2 GB | 2.65 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 3.4 GB | 3.5 GB | 2.95 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 3.5 GB | 3.6 GB | 3.03 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 4.1 GB | 4.2 GB | 3.64 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 4.8 GB | 4.9 GB | 4.32 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 5.4 GB | 5.6 GB | 5.00 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 6.5 GB | 6.6 GB | 6.06 GB | 8-bit quantization, near-lossless |
Which GPUs 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. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Yi 6B?
Q4_K_M · 4.1 GB33 devices with unified memory can run Yi 6B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomBenchmarks
View all 4 →Related Models
Derivatives (3)
Frequently Asked Questions
- How much VRAM does Yi 6B need?
Yi 6B requires 4.1 GB of VRAM at Q4_K_M, or 6.5 GB at Q8_0.
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)
KV Cache + Overhead ≈ 0.6 GB (at full 4K context)
VRAM usage by quantization
Q4_K_M4.1 GBQ4_K_M + full context4.2 GB- 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 IQ3_XS at 2.9 GB.
VRAM requirement by quantization
IQ3_XS2.9 GBIQ3_M3.2 GBIQ4_XS3.7 GBQ4_K_M ★4.1 GBQ5_04.2 GBQ8_06.5 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Yi 6B on a Mac?
Yi 6B requires at least 2.9 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 6B locally?
Yes — Yi 6B can run locally on consumer hardware. At Q4_K_M quantization it needs 4.1 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Yi 6B?
At Q4_K_M, Yi 6B can reach ~716 tok/s on AMD Instinct MI300X. 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: AMD Instinct MI300X → 5300 ÷ 4.1 × 0.55 = ~716 tok/s
Estimated speed at Q4_K_M (4.1 GB)
~716 tok/s~161 tok/s~535 tok/s~443 tok/sReal-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 Q8_0 version is 6.06 GB. The smallest option (IQ3_XS) is 2.50 GB.
- Which GPUs can run Yi 6B?
35 consumer GPUs can run Yi 6B at Q4_K_M (4.1 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 35 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Yi 6B?
33 devices with unified memory can run Yi 6B at Q4_K_M (4.1 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.