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Qwen2.5-Coder 14B VRAM Requirements — Q4, Q5, Q8, FP16 Hardware Guide

Exact VRAM for Qwen2.5-Coder 14B at every quantization level. Q4_K_M needs ~8.7 GB, Q8 needs ~14.7 GB. Best GPUs and Macs for local coding inference.

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

QuantizationVRAM (weights)Total with overheadFits on
Q4_K_M~8.7 GB~10–11 GBRTX 4070 12GB (tight), RTX 4060 Ti 16GB
Q5_K_M~10.7 GB~12–13 GBRTX 4070 12GB, RTX 3060 12GB, M4 Pro 18GB
Q6_K~12.8 GB~14–15 GBRTX 4080 16GB, RTX 4060 Ti 16GB, M4 Pro 24GB
Q8_0~14.7 GB~16–17 GBRTX 4080 16GB, RTX 5070 Ti 16GB, M4 Pro 24GB
FP16~28.0 GB~30+ GBRTX 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

FeatureValue
Total parameters14 billion
ArchitectureDense transformer
Context window128K tokens
LicenseApache 2.0
HuggingFaceQwen/Qwen2.5-Coder-14B-Instruct
Ollamaqwen2.5-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.

MacRecommended QuantExperience
M4 Air 16GBQ4_K_M (tight)Possible but limited context headroom
M3 Pro / M4 Pro 18GBQ5_K_MGood daily-driver setup
M4 Pro 24GBQ6_K or Q8_0Excellent; ~30–45 tok/s on M4 Pro
M4 Max 36GB+Q8_0 or FP16No 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

ModelVRAM Q4HumanEval+SWE-benchBest for
Qwen2.5-Coder 7B~4.7 GB~72%~19%8 GB GPUs, fast iteration
Qwen2.5-Coder 14B~8.7 GB83.5%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 functions
  • Q5_K_M: recommended minimum for real coding workflows
  • Q6_K or Q8_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

Frequently Asked Questions

How much VRAM does Qwen2.5-Coder 14B need?

Qwen2.5-Coder 14B needs approximately 8.7 GB at Q4_K_M, 10.7 GB at Q5_K_M, 12.8 GB at Q6_K, 14.7 GB at Q8_0, and 28.0 GB at FP16. Add 1–2 GB for KV cache at typical context lengths.

Can I run Qwen2.5-Coder 14B on an 8GB GPU?

At Q4_K_M (~8.7 GB) the model weights exceed the 8 GB limit on their own. An 8 GB GPU like the RTX 4060 cannot load it without aggressive context reduction and CPU offloading. A 12 GB GPU is the minimum practical target.

What GPU is best for Qwen2.5-Coder 14B?

The RTX 4070 12GB runs Q5_K_M comfortably. The RTX 4080 16GB or RTX 4060 Ti 16GB handles Q8_0 with headroom. On Apple Silicon, the M3 Pro or M4 Pro with 18–24 GB unified memory is ideal.

How does Qwen2.5-Coder 14B compare to 7B for coding?

Qwen2.5-Coder 14B scores 83.5 on HumanEval+ versus the 7B's roughly 72. The 14B handles multi-file refactors and complex logic more reliably. If your GPU has 12 GB or more, the quality jump is worth the extra VRAM over the 7B.

Can I run Qwen2.5-Coder 14B on a MacBook Pro?

Yes. Any M-series Mac with 18 GB or more unified memory can run Qwen2.5-Coder 14B at Q5_K_M or higher. The M4 Pro 24GB gives a strong experience at Q6_K with headroom for context.