Yi Coder 1.5B Chat on Apple M5 Max (64 GB)
Can the Apple M5 Max (64 GB) run Yi Coder 1.5B Chat?
Yes, it runs well
Runs well. At F16 it needs about 9.0 GB of 48.0 GB usable, with an estimated speed of 103 tok/s.
- Best quant
F16- Generation speed
- 103 tok/s
- Prompt processing
- 7,918 tok/s
- First token (1K prompt)
- 0.3 s
- Largest safe context
- 128K
Download Yi Coder 1.5B Chat
Recommended for the Apple M5 Max (64 GB): F16. Every other quantization has its own download link in the table below.
Quickest · Ollama
One command downloads a ready-to-run version and starts a chat.
ollama run yi-coder:1.5b-chat-fp16Quantized files · GGUF
Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.
Download the F16 file (opens in a new tab)
huggingface.co/bartowski/Yi-Coder-1.5B-Chat-GGUF (opens in a new tab)
ollama run hf.co/bartowski/Yi-Coder-1.5B-Chat-GGUF:F16Original weights · Hugging Face
Full-precision files from 01.AI, for fine-tuning, vLLM, Transformers.
huggingface.co/01-ai/Yi-Coder-1.5B-Chat (opens in a new tab)
How the C grade is scored
Every result gets up to 100 points from four parts. The points decide the letter grade.
| Model quality | 7.2 / 45 | |
|---|---|---|
| Memory fit | 25.0 / 25 | |
| Speed | 15.0 / 15 | |
| Memory use | 4.3 / 15 | |
| Total | 51.5 / 100 | 51 ptsUsable (C) · 51 of 100 points: Model quality 7.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 4.3/15 |
Every quantization on the Apple M5 Max (64 GB)
| Quant | Memory needed | Available | Speed | Fit | Grade · points | Download |
|---|---|---|---|---|---|---|
Q2_K | 6.6 GB | 48.0 GB | 720 tok/s | Runs well | 48 ptsUsable (C) · 48 of 100 points: Model quality 5.0/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 3.2/15 | GGUF (opens in a new tab) |
Q3_K_M | 6.8 GB | 48.0 GB | 567 tok/s | Runs well | 49 ptsUsable (C) · 49 of 100 points: Model quality 6.0/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 3.3/15 | GGUF (opens in a new tab) |
Q4_K_M | 7.0 GB | 48.0 GB | 439 tok/s | Runs well | 50 ptsUsable (C) · 50 of 100 points: Model quality 6.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 3.3/15 | GGUF (opens in a new tab) |
Q5_K_M | 7.1 GB | 48.0 GB | 362 tok/s | Runs well | 50 ptsUsable (C) · 50 of 100 points: Model quality 7.0/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 3.4/15 | GGUF (opens in a new tab) |
Q6_K | 7.3 GB | 48.0 GB | 304 tok/s | Runs well | 50 ptsUsable (C) · 50 of 100 points: Model quality 7.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 3.5/15 | GGUF (opens in a new tab) |
Q8_0 | 7.6 GB | 48.0 GB | 223 tok/s | Runs well | 50 ptsUsable (C) · 50 of 100 points: Model quality 7.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 3.7/15 | GGUF (opens in a new tab) |
F16 | 9.0 GB | 48.0 GB | 103 tok/s | Runs well | 51 ptsUsable (C) · 51 of 100 points: Model quality 7.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 4.3/15 | GGUF (opens in a new tab) Original weights (opens in a new tab) |
Memory figures assume 8K of context. Longer conversations or documents grow the KV cache.
Other hardware that runs it
- 62 ptsGood (B) · 62 of 100 points: Model quality 7.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 4060
- 58 ptsGood (B) · 58 of 100 points: Model quality 7.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 11.1/15 Intel Arc B580
- 58 ptsGood (B) · 58 of 100 points: Model quality 7.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 11.1/15 NVIDIA GeForce RTX 3060 12GB
- 58 ptsGood (B) · 58 of 100 points: Model quality 7.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 11.1/15 NVIDIA GeForce RTX 4070 SUPER
- 57 ptsUsable (C) · 57 of 100 points: Model quality 7.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 10.0/15 Apple M4 (16 GB)
- 56 ptsUsable (C) · 56 of 100 points: Model quality 7.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 9.3/15 Apple M4 Pro (24 GB)
Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.