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| Model Name | Model Size | Best For / Use Case | Full Precision (FP16/BF16) Memory | 4-bit Quantized Memory | Hardware Recommendations | Context Window | Special Notes |
|---|---|---|---|---|---|---|---|
| Qwen 2.5 0.5B | 0.5B | IoT devices, mobile on-device, extremely resource-constrained edge deployment | ~1 GB | ~500 MB | Smartphones, Raspberry Pi, edge devices | 32K tokens | INT8, INT4 quantization available |
| qwen2.5-coder:1.5b (YOUR MODEL) | 1.5B | Code completion, code generation, debugging, Python/JavaScript/Java/C++ tasks, lightweight programming assistant, runs on laptops | ~3 GB VRAM | ~1.2-1.6 GB (4-bit) | MacBook Air/Pro, any laptop with 4GB+ RAM, Raspberry Pi 5 (slow), NVIDIA GTX 1050 4GB | 32K tokens | Specialized for coding tasks, outperforms base 1.5B on programming benchmarks, great for local development |
| Qwen 2.5 1.5B | 1.5B | Light customer chat, simple conversational AI, text generation | ~3 GB | ~1.6 GB | Samsung S24 Ultra, 4GB GPU minimum | 32K tokens | GPTQ, AWQ, GGUF, A8W4 |
| Qwen 2.5 3B | 3B | Document RAG, edge servers, balanced performance | ~6 GB | 2-3 GB | Mid-range GPU 4-6GB VRAM, Apple M1/M2 | 32K tokens | GPTQ, AWQ, GGUF |
| Qwen 2.5 Coder 7B | 7B | Professional code generation, multi-file editing, complex programming tasks | ~15 GB | 4-6 GB | RTX 3060 12GB, RTX 4090 for development | 128K tokens | Specialized coding version of 7B |
| Qwen 2.5 7B | 7B | Multilingual applications, general purpose AI | ~15 GB | 4-6 GB | RTX 4090, A100 for production | 128K tokens | Base 7B model |
| Qwen 2.5 14B | 14B | Enterprise chat, advanced analytics, complex reasoning | ~28 GB | 8-10 GB | A100 40GB, RTX 4090 (quantized only) | 128K tokens | GPTQ, AWQ, GGUF |
| Qwen 2.5 Coder 32B | 32B | State-of-the-art open source code model, expert programmer | ~65 GB | 16-20 GB | RTX 4090 24GB (quantized), Mac 48GB RAM | 128K tokens | Top-tier coding performance |
| Qwen 2.5 32B | 32B | Research, complex reasoning, near-frontier performance | ~65 GB | 16-20 GB | RTX 3090/4090 (quantized), A100 | 128K tokens | Base 32B model |
| Qwen 2.5 72B | 72B | Frontier open-source, highest accuracy tasks | ~145 GB | 40-48 GB | 2x A100 40GB or 4x RTX 3090 | 128K tokens | AWQ, GPTQ, multi-GPU required |
Quantization Performance Impact (1.5B vs 7B Example)
| Model | Quantization Type | Memory Usage | Accuracy Loss | Inference Speed (tok/s) | Hardware Example |
|---|---|---|---|---|---|
| qwen2.5-coder:1.5b | FP16 (full) | ~3 GB | 0% | 50-70 tok/s | MacBook Air M1 |
| qwen2.5-coder:1.5b | Q4_K_M (4-bit) | ~1.2 GB | 1-2% | 80-100 tok/s | Raspberry Pi 5, 4GB GPU |
| Qwen 2.5 Coder 7B | FP16 | ~15 GB | 0% | 20-30 tok/s | RTX 4090 |
| Qwen 2.5 Coder 7B | Q4_K_M | ~4.5 GB | ~2% | 40-60 tok/s | RTX 3060 12GB |
How to Run Your qwen2.5-coder:1.5b
| Command | Description | Memory Needed |
|---|---|---|
ollama run qwen2.5-coder:1.5b |
Run with default settings (likely 4-bit quantized in Ollama) | ~1.5-2 GB RAM |
ollama pull qwen2.5-coder:1.5b-q4_K_M |
Explicitly pull 4-bit quantized version | ~1.2 GB disk / RAM |
ollama pull qwen2.5-coder:1.5b-fp16 |
Full precision version (higher quality, more RAM) | ~3 GB RAM |
Hardware Requirements Summary for Your Model
| Deployment Scenario | Model | Minimum RAM | Recommended Hardware | Performance Expectation |
|---|---|---|---|---|
| Mobile / Raspberry Pi | qwen2.5-coder:1.5b (4-bit) | 2 GB | Raspberry Pi 5 4GB, Android phone | 10-20 tok/s (Pi 5) |
| Laptop (battery efficient) | qwen2.5-coder:1.5b (4-bit) | 4 GB | MacBook Air, any Windows laptop | 50-80 tok/s |
| Desktop (quality focus) | qwen2.5-coder:1.5b (FP16) | 8 GB | Any desktop with 8GB+ RAM | 70-100 tok/s |
| Workstation | Qwen 2.5 Coder 7B | 16 GB | RTX 3060+ | 40-60 tok/s |
Key Facts About Your qwen2.5-coder:1.5b
- Release date: 2024 (part of Qwen 2.5 family)
- Architecture: Transformer decoder-only optimized for code
- Training data: 5.5 trillion tokens including 5.7 trillion code tokens
- Languages supported: 92+ programming languages including Python, JavaScript, Java, C++, TypeScript, Go, Rust
- Context window: 32K tokens (can handle medium-sized code files)
- License: Apache 2.0 (free for commercial use)
- Ollama pull command:
ollama pull qwen2.5-coder:1.5b - File size on disk: ~1.1 GB (Ollama 4-bit default)
- Performance: Outperforms much larger models on HumanEval coding benchmark
- Ideal for: Local development, VS Code integration, learning programming concepts
Important Notes for 2026
Your model is excellent for its size: qwen2.5-coder:1.5b achieves 61.5% on HumanEval, beating many 7B models from previous years.
Memory efficiency: In Ollama's default 4-bit quantization, your model uses only ~1.2GB RAM and runs smoothly on any laptop made in the last 5 years.
VS Code integration: You can use it with Continue.dev extension in VS Code for inline code completion.
Comparison to base model: The coder version outperforms the base Qwen 2.5 1.5B on all programming benchmarks while using the same memory footprint.
Upgrade path: If you need more capability, qwen2.5-coder:7b fits in 4-6GB RAM (4-bit) and qwen2.5-coder:32b fits in 16-20GB RAM (4-bit).
Ai context:
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