Qwen2.5 Coder 14B Instruct MLX 4bit — Hardware Requirements & GPU Compatibility
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An MLX 4-bit quantized version of Alibaba's Qwen2.5 Coder 14B Instruct, converted by LM Studio Community for Apple Silicon Macs. Qwen2.5 Coder 14B is a capable mid-size coding model that handles code generation, completion, and explanation across many popular programming languages. The 4-bit quantization makes this model very accessible on Apple Silicon, fitting comfortably on Macs with 16GB or more of unified memory. It offers a strong balance of coding ability and resource efficiency, making it a practical everyday coding assistant for developers running local models on macOS.
104.3K downloads
3 likesSpecifications
- Publisher
- LM Studio Community
- Family
- Qwen 2.5
- Parameters
- 2.3B
- Architecture
- Qwen2ForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 152,064
- Release Date
- 2024-11-13
- License
- Apache 2.0
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How Much VRAM Does Qwen2.5 Coder 14B Instruct MLX 4bit Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 1.7 GB | 7.7 GB | 0.98 GB | 2-bit quantization with K-quant improvements |
| Q3_K_M | 3.90 | 1.8 GB | 7.9 GB | 1.13 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 1.9 GB | 7.9 GB | 1.15 GB | 4-bit legacy quantization |
| Q3_K_L | 4.10 | 1.9 GB | 7.9 GB | 1.18 GB | 3-bit large quantization |
| Q4_K_M | 4.80 | 2.1 GB | 8.1 GB | 1.39 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_0 | 5.00 | 2.1 GB | 8.2 GB | 1.44 GB | 5-bit legacy quantization |
| Q5_K_M | 5.70 | 2.4 GB | 8.4 GB | 1.64 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 2.6 GB | 8.7 GB | 1.90 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 3.0 GB | 9.1 GB | 2.31 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Qwen2.5 Coder 14B Instruct MLX 4bit?
Q4_K_M · 2.1 GBQwen2.5 Coder 14B Instruct MLX 4bit (Q4_K_M) requires 2.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 3+ GB is recommended. Using the full 33K context window can add up to 6.0 GB, bringing total usage to 8.1 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Qwen2.5 Coder 14B Instruct MLX 4bit?
Q4_K_M · 2.1 GB33 devices with unified memory can run Qwen2.5 Coder 14B Instruct MLX 4bit, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Qwen2.5 Coder 14B Instruct MLX 4bit need?
Qwen2.5 Coder 14B Instruct MLX 4bit requires 2.1 GB of VRAM at Q4_K_M, or 3.0 GB at Q8_0. Full 33K context adds up to 6.0 GB (8.1 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 2.3B × 4.8 bits ÷ 8 = 1.4 GB
KV Cache + Overhead ≈ 0.7 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 6.7 GB (at full 33K context)
VRAM usage by quantization
Q4_K_M2.1 GBQ4_K_M + full context8.1 GB- What's the best quantization for Qwen2.5 Coder 14B Instruct MLX 4bit?
For Qwen2.5 Coder 14B Instruct MLX 4bit, Q4_K_M (2.1 GB) offers the best balance of quality and VRAM usage. Q5_0 (2.1 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 1.7 GB.
VRAM requirement by quantization
Q2_K1.7 GB~75%Q4_01.9 GB~85%Q4_K_M ★2.1 GB~89%Q5_02.1 GB~90%Q5_K_M2.4 GB~92%Q8_03.0 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen2.5 Coder 14B Instruct MLX 4bit on a Mac?
Qwen2.5 Coder 14B Instruct MLX 4bit requires at least 1.7 GB at Q2_K, 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 Qwen2.5 Coder 14B Instruct MLX 4bit locally?
Yes — Qwen2.5 Coder 14B Instruct MLX 4bit can run locally on consumer hardware. At Q4_K_M quantization it needs 2.1 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen2.5 Coder 14B Instruct MLX 4bit?
At Q4_K_M, Qwen2.5 Coder 14B Instruct MLX 4bit can reach ~1395 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~314 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 ÷ 2.1 × 0.55 = ~1395 tok/s
Estimated speed at Q4_K_M (2.1 GB)
AMD Instinct MI300X~1395 tok/sNVIDIA GeForce RTX 4090~314 tok/sNVIDIA H100 SXM~1043 tok/sAMD Instinct MI250X~862 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Qwen2.5 Coder 14B Instruct MLX 4bit?
At Q4_K_M, the download is about 1.39 GB. The full-precision Q8_0 version is 2.31 GB. The smallest option (Q2_K) is 0.98 GB.