If you are searching for Qwen2.5-Coder 14B VRAM requirements, this is the focused answer. Qwen2.5-Coder 14B is a dense 14B-parameter coding-specialist model from Alibaba (released November 2024) that scores 83.5 on HumanEval+ and 27.0 on SWE-bench Verified — competitive with much larger general-purpose models for pure coding tasks.
Quick answers
- Q4_K_M: ~8.7 GB
- Q5_K_M: ~10.7 GB
- Q6_K: ~12.8 GB
- Q8_0: ~14.7 GB
- FP16: ~28.0 GB
These are weight-only estimates using the standard formula (params × bits-per-weight / 8). Add 1–2 GB for KV cache and runtime overhead at typical context sizes (8K–32K tokens). With the full 128K context window active, KV cache can add several GB more.
Qwen2.5-Coder 14B VRAM by Quantization
| Quantization | VRAM (weights) | Total with overhead | Fits on |
|---|---|---|---|
| Q4_K_M | ~8.7 GB | ~10–11 GB | RTX 4070 12GB (tight), RTX 4060 Ti 16GB |
| Q5_K_M | ~10.7 GB | ~12–13 GB | RTX 4070 12GB, RTX 3060 12GB, M4 Pro 18GB |
| Q6_K | ~12.8 GB | ~14–15 GB | RTX 4080 16GB, RTX 4060 Ti 16GB, M4 Pro 24GB |
| Q8_0 | ~14.7 GB | ~16–17 GB | RTX 4080 16GB, RTX 5070 Ti 16GB, M4 Pro 24GB |
| FP16 | ~28.0 GB | ~30+ GB | RTX 4090 24GB (tight), RTX 5090 32GB, M4 Max 64GB |
Recommendation by tier:
- 12 GB GPU: Q5_K_M is the sweet spot. Q4_K_M fits but leaves minimal headroom.
- 16 GB GPU: Q8_0 is comfortable. Near-lossless quality for coding tasks.
- 24 GB GPU or Mac: Q8_0 easily, or FP16 on RTX 4090 at reduced context.
Architecture
| Feature | Value |
|---|---|
| Total parameters | 14 billion |
| Architecture | Dense transformer |
| Context window | 128K tokens |
| License | Apache 2.0 |
| HuggingFace | Qwen/Qwen2.5-Coder-14B-Instruct |
| Ollama | qwen2.5-coder:14b |
GPU Hardware Guide
12 GB — RTX 4070, RTX 3060 12GB, RTX 4070 Super
This is the minimum comfortable tier for Qwen2.5-Coder 14B.
- RTX 4070 12GB: Q5_K_M fits with a slim margin. Expect 20–35 tok/s depending on prompt length.
- RTX 3060 12GB: Q5_K_M workable but slower; better if you keep context under 16K.
Practical advice: avoid Q4_K_M on 12 GB if you can — the extra 2 GB for Q5 is worth it for code syntax accuracy.
16 GB — RTX 4080, RTX 4060 Ti 16GB, RTX 5070 Ti
This is the sweet spot tier for Qwen2.5-Coder 14B.
- Q8_0 (~14.7 GB) loads with 1–2 GB headroom for KV cache at moderate context lengths.
- Speed on RTX 4080: approximately 40–55 tok/s at Q8_0.
Best daily-driver setup: Q8_0 on a 16 GB GPU gives near-lossless code generation at practical inference speeds.
24 GB — RTX 4090, RTX 5090 32GB
Qwen2.5-Coder 14B is straightforward at this tier.
- RTX 4090 24GB: FP16 is feasible if you stay under 64K context. Q8_0 runs with ample headroom.
- RTX 5090 32GB: FP16 with comfortable context budget.
For users with 24 GB+ hardware who want the best coding model per GB, consider stepping up to Qwen 3 Coder 30B-A3B which fits at Q4 in ~17 GB and outperforms on SWE-bench.
Apple Silicon Macs
Unified memory removes the hard VRAM ceiling — the model shares memory with system RAM.
| Mac | Recommended Quant | Experience |
|---|---|---|
| M4 Air 16GB | Q4_K_M (tight) | Possible but limited context headroom |
| M3 Pro / M4 Pro 18GB | Q5_K_M | Good daily-driver setup |
| M4 Pro 24GB | Q6_K or Q8_0 | Excellent; ~30–45 tok/s on M4 Pro |
| M4 Max 36GB+ | Q8_0 or FP16 | No compromises |
For Apple Silicon, use ollama run qwen2.5-coder:14b or pull a GGUF from unsloth/Qwen2.5-Coder-14B-Instruct-GGUF via LM Studio.
Qwen2.5-Coder 14B vs Sibling Sizes
| Model | VRAM Q4 | HumanEval+ | SWE-bench | Best for |
|---|---|---|---|---|
| Qwen2.5-Coder 7B | ~4.7 GB | ~72% | ~19% | 8 GB GPUs, fast iteration |
| Qwen2.5-Coder 14B | ~8.7 GB | 83.5% | 27.0% | 12–16 GB, quality jump |
| Qwen2.5-Coder 32B | ~19.6 GB | ~88% | ~33% | 24 GB, best Qwen2.5 coder |
The 14B hits the most useful efficiency crossover: a meaningful quality step over the 7B while staying within reach of 12 GB GPUs at Q5.
Best Quant for Coding
Code is syntax-sensitive — a misplaced bracket or quote breaks the output. General guidance:
Q4_K_M: acceptable for code chat and simple generation; occasional syntax slips on complex functionsQ5_K_M: recommended minimum for real coding workflowsQ6_KorQ8_0: strongly preferred for multi-file refactors, agentic use (Cursor, Continue.dev)FP16: unnecessary for most workflows; reserve for research or benchmarking
Quick Start
# Ollama
ollama run qwen2.5-coder:14b
# LM Studio
# Search: Qwen2.5-Coder-14B-Instruct-GGUF
# Recommended: Q5_K_M (12GB GPU) or Q8_0 (16GB GPU)
Related Guides
- Best Local Coding LLMs for Apple Silicon 24GB — ranked picks for 24GB Macs
- Qwen 3 Coder vs DeepSeek Coding — next-gen coding model comparison
- Qwen 3.5 9B VRAM Requirements — smaller Qwen sibling
- How Much VRAM Do LLMs Need? — complete reference guide
- VRAM Calculator — check Qwen2.5-Coder 14B against your specific GPU