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Qwen 14B vs Llama 3 8B: Which Runs Better Locally?

Quick Answer

Llama 3 8B fits in 6 GB VRAM and runs faster. Qwen 2.5 14B needs 10+ GB but scores higher on benchmarks. If you have 12 GB VRAM, Qwen 14B wins on quality.

  • ▸Llama 3 8B Q4_K_M: 6 GB VRAM, ~25 tok/s on RTX 3060
  • ▸Qwen 2.5 14B Q4_K_M: 10 GB VRAM, ~15 tok/s on RTX 3060
  • ▸Qwen 14B is better quality; Llama 8B is faster

Updated: 2026-05

Model ComparisonsIntermediate

Key Takeaways

  • ✓Llama 3 8B Q4_K_M needs only 6 GB VRAM and delivers ~25 tok/s on RTX 3060 — the right pick for interactive speed
  • ✓Qwen 2.5 14B Q4_K_M needs 10 GB VRAM and runs at ~15 tok/s — but scores 8–10 points higher on MMLU and reasoning benchmarks
  • ✓The VRAM crossover point is 12 GB: below that, Llama 8B is the only option; at 12 GB, Qwen 14B wins on quality
  • ✓For coding tasks specifically, the gap widens further in Qwen 14B's favor — Qwen Coder variants add additional code-benchmark advantage

Llama 3 8B Wins on Speed and VRAM Fit

Llama 3 8B at Q4_K_M quantization uses 6 GB VRAM and runs at ~25 tokens per second on an RTX 3060 12 GB — making it the default choice for any setup with under 10 GB VRAM. Its 8B parameter count translates into snappy, interactive-speed responses that feel natural for chat and short code sessions.

Qwen 2.5 14B at Q4_K_M requires approximately 10 GB VRAM and produces ~15 tok/s on the same card. The lower throughput is noticeable in real-time conversations but acceptable for batch summarization or longer document processing where quality matters more than latency.

The speed difference (25 vs 15 tok/s) means Llama 3 8B generates a 200-token answer in about 8 seconds, while Qwen 2.5 14B takes about 13 seconds. For single-turn queries this gap is minor; for multi-turn chat sessions it compounds.

Use CaseWinnerWhy
Coding & reasoningQwen 2.5 14BHigher parameter count improves multi-step logic
Chat & instructionLlama 3 8BOptimized for fast interactive responses
MultilingualTiedBoth strong on European and East Asian languages
RAM-constrained (≤8 GB)Llama 3 8BFits in 6 GB; Qwen 14B needs 10 GB
Long context (16K+)Qwen 2.5 14BBetter recall at extended context lengths

Qwen 2.5 14B Wins on Quality When VRAM Allows

Qwen 2.5 14B scores 74.8% on MMLU versus 66.6% for Llama 3 8B — an 8-point gap that reflects in noticeably better multi-step reasoning, instruction following, and structured output consistency. The difference is particularly visible on tasks that require holding and applying context across multiple paragraphs.

If your primary use case is code completion, the quality gap grows. Qwen 2.5 Coder 14B (the code-tuned variant of the same base) scores 78.4% on HumanEval. Llama 3 8B generic scores around 55% on the same benchmark — a 23-point difference on coding tasks.

≤8 GB VRAM: Llama 3 8B Q4_K_M fits with ~2 GB headroom — Qwen 14B is not an option. 10–12 GB VRAM: Qwen 2.5 14B Q4_K_M fits at the tipping point. 16+ GB VRAM: either model works; Qwen 2.5 14B Q5 becomes practical.

For a deeper look at coding model performance including benchmark tables, see the best 14B models for coding comparison.

Quick Answers About Qwen 14B vs Llama 8B

Can Qwen 2.5 14B run on a 6 GB VRAM GPU?▾
No. Qwen 2.5 14B at Q4_K_M requires approximately 10 GB VRAM. On a 6 GB card you would need to drop to Q2_K quantization, which causes significant quality degradation. Llama 3 8B is the correct model for 6 GB VRAM.
Is Qwen 2.5 14B or Llama 3 8B better for coding?▾
Qwen 2.5 14B is substantially better for coding. Qwen Coder 14B (the code-tuned variant) scores 78.4% on HumanEval versus ~55% for Llama 3 8B. Use Llama 3 8B only when VRAM prevents running Qwen.
Does Qwen 2.5 14B support longer context than Llama 3 8B?▾
Qwen 2.5 14B supports a 128k context window natively. Llama 3 8B supports 8k by default, though RoPE-extended variants can reach 128k with some quality loss. For long-document tasks, Qwen 2.5 14B has a clear advantage even before accounting for its larger parameter count.
Does context length affect which model to choose for chat?▾
Yes. For typical single-turn or short multi-turn chat (under 4k tokens), both models are fine — choose based on VRAM. For long conversations or document-heavy sessions, Qwen 2.5 14B's 128k native context window is a meaningful advantage over Llama 3 8B's default 8k limit.
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