Yi 1.5 6B Chat — Hardware Requirements & GPU Compatibility
Chat
Yi 1.5 6B Chat is a 6.1B-parameter open language model from 01.AI in the Yi 1.5 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.
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 1.5 6B Chat
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
ollama run hf.co/MaziyarPanahi/Yi-1.5-6B-Chat-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 19.8 GB free · 4K context
Download: Yi-1.5-6B-Chat.Q4_K_M.gguf3.67 GBMaziyarPanahi
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (7 GB headroom) · Q4_K_M
- Generation speed
- ~58 tok/s
- generation speed
- Cost per 1M output tokens
- $0.40
- per 1M output tokens
How Much VRAM Does Yi 1.5 6B Chat 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 | — | 3-bit large — 3-bit large quantization | Fits: 4K context · ~185 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 1.5 6B Chat?
Q4_K_M · 4.1 GBYi 1.5 6B Chat (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 1.5 6B Chat, 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 1.5 6B Chat?
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
This is the single board showing results for this model; badges show who ran the test.
- MMLU-Pro
TIGER-LabIndependent
Independent38.2%
#105/145 open
Alternatives to Yi 1.5 6B Chat
Yi 1.5 family · 3 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 6B Chat01.AI | — | 4.2 GB | ~161 | same size |
Specifications
- Publisher
- 01.AI
- Family
- Yi 1.5
- Parameters
- 6.1B
- Architecture
- LlamaForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 64,000
- Release Date
- 2024-05-11
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does Yi 1.5 6B Chat need?
Yi 1.5 6B Chat 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 1.5 6B Chat?
For Yi 1.5 6B Chat, Q4_K_M (4.1 GB) offers the best balance of quality and VRAM usage. Q5_K_S (4.6 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 3.0 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Yi 1.5 6B Chat on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Yi 1.5 6B Chat. 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 1.5 6B Chat locally?
Yes — Yi 1.5 6B Chat can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Yi 1.5 6B Chat?
At Q4_K_M, Yi 1.5 6B Chat 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 1.5 6B Chat?
At Q4_K_M, the download is about 3.64 GB. The full-precision BF16 version is 12.12 GB. The smallest option (Q2_K) is 2.58 GB.
Which GPUs can run Yi 1.5 6B Chat?
52 consumer GPUs can run Yi 1.5 6B Chat 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 1.5 6B Chat?
163 devices with unified memory can run Yi 1.5 6B Chat 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.