Qwen 2.5 Coder 14B is currently the reigning champion of local programming intelligence. On HumanEval and SWE-bench benchmarks, it matches the coding capabilities of original GPT-4 while operating completely offline.
However, if you own a mainstream gaming PC or laptop with an 8GB GPU (such as an NVIDIA RTX 4060, RTX 3070, or RTX 4070 Laptop), you run directly into a painful engineering dilemma: Can an 8GB card run a 14.8 billion parameter model without throwing CUDA Out-Of-Memory (OOM) or crawling at 2 tokens per second?
To answer this definitively, we placed an RTX 4060 8GB on the test bench and ran Qwen 2.5 Coder 14B across four distinct quantization precisions: Q8_0, Q5_K_M, Q4_K_M, and Q3_K_M. Here are the empirical results.
The Short Answer: Yes, But Only With the Right Quantization
If you type ollama run qwen2.5-coder:14b out of the box, Ollama pulls the Q4_K_M build. On an 8GB GPU, this results in approximately 7 to 10 layers being offloaded to system RAM, delivering a usable but sluggish 14.5 tokens/sec.
However, by downloading the Q3_K_M (or Q3_K_S) quantization, the entire model weights + 4K KV cache drop to 7.55 GB. This fits 100% inside your 8GB VRAM, unleashing full GPU compute at 38.2 tokens/second with virtually zero perceptible coding degradation.
Test Bench Benchmark Results Across 4 Quantizations
Tests conducted on Windows 11 (Desktop Window Manager active, 0.75 GB baseline VRAM overhead), Ollama v0.5.x, with FlashAttention enabled:
| Quantization | Weight Size | Total VRAM Needed (4K Ctx) | Fit Verdict on 8GB | GPU Layer Offload | Inference Speed |
|---|---|---|---|---|---|
| Q8_0 | 15.6 GB | 16.8 GB | ✗ OOM / Heavy Offload | 22 / 48 GPU layers | 2.2 tok/s (Unusable) |
| Q5_K_M | 10.3 GB | 11.4 GB | ⚠ Tight (RAM Offload) | 34 / 48 GPU layers | 7.8 tok/s (Slow) |
| Q4_K_M (Default) | 8.3 GB | 9.3 GB | ⚠ Tight (RAM Offload) | 41 / 48 GPU layers | 14.5 tok/s (Acceptable) |
| Q3_K_M (Recommended) | 6.7 GB | 7.6 GB | ✓ 100% GPU Offload | 48 / 48 GPU layers | 38.2 tok/s (Blazing) |
Want to check your exact PC hardware?
Use our real-time LLM Hardware Calculator to calculate exact weights, KV cache, and OS overhead for any GPU & RAM configuration.
⚡ Open LLM Hardware Calculator Pre-FilledWhy Q4_K_M Spills Over on 8GB Cards
Many developers look at the Q4_K_M file size on Hugging Face (approx 8.9 GB uncompressed) and wonder why it fails to fit into an 8,192 MB GPU.
The reason lies in The 3-Layer Runtime Memory Stack:
- Operating System Display Overhead: On Windows 11, the Desktop Window Manager (DWM) reserves between 600 MB and 850 MB of VRAM simply to drive your display and browser. Your 8GB card actually has only ~7.25 GB of usable VRAM for compute.
- Static Model Weights: Q4_K_M weights consume 8.30 GB.
- KV Cache & Context: At 4K context, Qwen 14B's KV cache requires 450 MB. At 16K context, it requires 1.8 GB!
When you add 8.30 GB (weights) + 0.45 GB (KV cache) + 0.60 GB (CUDA overhead), total demand hits 9.35 GB. Since only 7.25 GB is free on the GPU, llama.cpp / Ollama is forced to offload 7 layers to system RAM across the PCIe bus, dropping your generation speed by 60%.
How to Run Qwen 14B at 38 tok/s on an 8GB GPU
If you want full 100% GPU acceleration without buying new hardware, follow this exact configuration guide:
Step 1: Download the Q3_K_M GGUF Variant
In your terminal, pull the 3-bit quantized build instead of the default Q4 build:
# Run Q3_K_M directly in Ollama:
ollama run hf.co/bartowski/Qwen2.5-Coder-14B-Instruct-GGUF:Q3_K_MStep 2: Enable FlashAttention
FlashAttention compresses intermediate attention calculations, saving up to 400 MB of VRAM. Before starting Ollama, set this environment variable:
# On Windows (PowerShell):
$env:OLLAMA_FLASH_ATTENTION="1"
ollama serve
# On Linux / macOS:
export OLLAMA_FLASH_ATTENTION=1
ollama serveStep 3: Cap Your Context Window to 8,192 Tokens
While Qwen 14B supports up to 128K context, running past 8K on an 8GB card will push the KV cache into system RAM. For local code generation, inline autocomplete, and script writing, an 8K window is more than enough and guarantees zero memory spills.
Does Q3_K_M Lose Intelligence?
A common fear among engineers is that 3-bit quantization ruins reasoning accuracy. With older architectures (like Llama 1), 3-bit was indeed catastrophic.
However, modern GGUF k-quants (Q3_K_M) use variable bit-rate assignment. Crucial attention tensors and output heads remain at 4-bit and 5-bit precision, while non-critical feed-forward matrices are compressed. On the HumanEval Python coding benchmark:
- FP16 (Uncompressed): 85.4% pass@1
- Q5_K_M: 85.1% pass@1
- Q4_K_M: 84.7% pass@1
- Q3_K_M: 83.9% pass@1
You sacrifice less than 1% of coding accuracy in exchange for a 2.6x speed improvement (38.2 tok/s vs 14.5 tok/s). On an 8GB GPU, Q3_K_M is the undisputed winner.