This article was originally published on Best GPU for LLM. The full version with interactive tools, FAQ, and live pricing is on the original site.
Qwen 2.5 7B runs well on an RTX 4060 Ti 16GB at $400. For Qwen 2.5 32B — the quality sweet spot of the lineup — you need an RTX 4090 24GB at minimum. Qwen 2.5 72B requires an RTX 5090 with heavy quantization or dual GPUs.
See the recommended pick on the original guide
Qwen 2.5 full model lineup
Alibaba's Qwen 2.5 series covers an unusually wide range of sizes, from edge deployment at 0.5B to near-frontier performance at 72B:
| Model | Parameters | FP16 Size | Q4_K_M Size | Minimum VRAM |
|---|---|---|---|---|
| Qwen 2.5 0.5B | 0.5B | ~1GB | ~0.4GB | 4GB |
| Qwen 2.5 1.5B | 1.5B | ~3GB | ~1GB | 4GB |
| Qwen 2.5 3B | 3B | ~6GB | ~2GB | 6GB |
| Qwen 2.5 7B | 7B | ~14GB | ~4.5GB | 8GB |
| Qwen 2.5 14B | 14B | ~28GB | ~8.5GB | 12GB |
| Qwen 2.5 32B | 32B | ~64GB | ~19GB | 24GB |
| Qwen 2.5 72B | 72B | ~144GB | ~42GB | 32GB+ |
The 0.5B to 3B models run on virtually any hardware including integrated graphics. The 7B and 14B models are the sweet spot for most local users. The 32B model is where Qwen really stands out — its reasoning quality rivals many 70B competitors while fitting on a single RTX 4090.
VRAM chart available at the original article
Qwen 2.5 coding model variants
Qwen 2.5 includes dedicated coding variants alongside the general models:
| Variant | Sizes Available | Specialty |
|---|---|---|
| Qwen 2.5-Coder | 0.5B, 1.5B, 3B, 7B, 14B, 32B, 72B | Code generation, completion, review |
| Qwen 2.5-Math | 1.5B, 7B, 72B | Mathematical reasoning |
| Qwen 2.5 (base) | 0.5B–72B | General instruction following |
Qwen 2.5-Coder 32B is particularly notable — it rivals GPT-4o on several coding benchmarks while fitting on a single RTX 4090. If coding is your primary use case, the Coder variant at 32B on a 4090 is one of the most compelling local setups available.
Best GPUs for Qwen 2.5 7B and 14B
These are the most popular sizes for local deployment. The 7B handles fast chat and general tasks; the 14B adds noticeably better reasoning without requiring a large VRAM upgrade.
| GPU | VRAM | Qwen 7B Q4 | Qwen 7B Q8 | Qwen 14B Q4 | Price |
|---|---|---|---|---|---|
| RTX 5090 | 32GB | ~95 tok/s | ~80 tok/s | ~65 tok/s | ~$2,000 |
| RTX 4090 | 24GB | ~65 tok/s | ~55 tok/s | ~45 tok/s | ~$1,600 |
| RTX 5080 | 16GB | ~55 tok/s | ~45 tok/s | ~38 tok/s | ~$1,000 |
| RTX 4070 Ti Super | 16GB | ~40 tok/s | ~32 tok/s | ~28 tok/s | ~$700 |
| RTX 4060 Ti 16GB | 16GB | ~35 tok/s | ~28 tok/s | ~22 tok/s | ~$400 |
| RTX 3060 12GB (used) | 12GB | ~30 tok/s | ~18 tok/s | Tight | ~$250 |
Qwen 2.5 14B at Q4_K_M needs about 8.5GB for model weights plus context overhead. A 16GB card fits it with moderate context headroom. A 24GB card gives full breathing room for Qwen 2.5's native 32K+ context support.
Qwen 2.5 14B: optimal quantization by VRAM
| VRAM | Recommended Quant | Notes |
|---|---|---|
| 12GB | Q4_K_M | Fits at short-medium context; tight at 8K+ |
| 16GB | Q6_K | Good balance of quality and context headroom |
| 24GB | Q8 | Full quality, 32K context comfortable |
| 32GB | FP16 | Maximum quality, all context lengths |
Qwen 2.5 14B at Q6_K on a 16GB card is one of the best value propositions in local LLM. The 14B model at Q6 consistently outperforms older 7B models at FP16 on reasoning tasks. For the full quantization-by-quantization VRAM math on the 14B specifically, see how much VRAM for Qwen 14B. For Qwen 3 specifically, see how much VRAM Qwen 3 needs across the full model lineup.
Best GPUs for Qwen 2.5 32B
The 32B model is the standout in the lineup. At Q4_K_M it needs ~19GB, landing squarely in RTX 4090 territory.
| GPU | Quantization | VRAM Used | Fits? | Notes |
|---|---|---|---|---|
| RTX 5090 (32GB) | Q6_K | ~24GB | Yes | Near-Q8 quality, 8K context comfortable |
| RTX 5090 (32GB) | Q4_K_M | ~19GB | Yes | Comfortable fit, long context OK |
| RTX 4090 (24GB) | Q4_K_M | ~19GB | Yes | Good fit with 4K–8K context |
| RTX 4090 (24GB) | Q6_K | ~24GB | Tight | Short context only |
| RTX 5080 (16GB) | Q3_K_M | ~14GB | Tight | Quality degraded, minimal context |
The RTX 4090 at Q4_K_M is the best value entry point for Qwen 32B. The RTX 5090 lets you push to Q6_K for noticeably better output quality with full context support.
See the recommended pick on the original guide
Qwen 2.5 72B: dual GPU or cloud
Like other 70B-class models, Qwen 72B needs ~42GB at Q4_K_M. A single RTX 5090 can handle Q2_K (~26GB) or a very tight Q3_K_M (~33GB), but quality suffers below Q4. For quality Qwen 72B locally:
- 2x RTX 4090 (48GB) — fits Q4_K_M cleanly via tensor splitting
- RTX 5090 + CPU offload — possible but slow; inference drops significantly
- Cloud inference — Vast.ai or RunPod for occasional use without buying hardware
Qwen 2.5 vs Llama 3 vs Mistral: which is best for what?
| Use Case | Best Model | Why |
|---|---|---|
| General chat | Llama 3 8B or Qwen 7B | Similar quality, Qwen slightly stronger multilingual |
| Coding | Qwen 2.5-Coder 14B or 32B | Dedicated coding training; beats Llama 3 at code |
| Mathematics | Qwen 2.5-Math or Qwen 32B | Purpose-built math training |
| Multilingual | Qwen 2.5 (any size) | Best non-English support, especially Chinese/Japanese/Korean |
| Reasoning at 32B | Qwen 2.5 32B | Beats most 70B competitors at this size |
| Fast responses | Mistral 7B | Extremely efficient for its quality level |
Qwen 2.5 32B is competitive with Llama 3 70B on most English reasoning benchmarks while using roughly half the VRAM. If you need multilingual capability or coding, Qwen is the clear winner at its respective size.
Tok/s benchmarks: Qwen 2.5 vs comparable models
At Q4_K_M on an RTX 4060 Ti 16GB:
| Model | Size | Tok/s | Notes |
|---|---|---|---|
| Mistral 7B | 7B | ~35 tok/s | Fastest 7B-class |
| Qwen 2.5 7B | 7B | ~33 tok/s | Slightly larger vocab overhead |
| Llama 3 8B | 8B | ~32 tok/s | Larger vocab than Llama 2 |
| Qwen 2.5 14B | 14B | ~22 tok/s | Fits 16GB at Q4_K_M |
| Llama 2 13B | 13B | ~20 tok/s | Older architecture |
The larger vocabulary in Qwen 2.5 adds a small overhead compared to Mistral 7B, but the quality difference for most tasks — especially multilingual and coding — more than compensates.
Which GPU should you buy for Qwen?
Running Qwen 2.5 7B for chat and general tasks? → RTX 4060 Ti 16GB ($400). Runs Q8 quantization comfortably with 8K context. Best budget entry point.
Running Qwen 2.5 14B as your daily driver? → RTX 4060 Ti 16GB ($400) minimum, RTX 4070 Ti Super ($700) preferred. 16GB fits Q6_K; the extra VRAM on 700-class cards helps with context headroom.
Running Qwen 2.5-Coder 32B (the best local coding setup)? → RTX 4090 ($1,600). Fits Q4_K_M (~19GB) comfortably with room for 4K–8K coding context.
Running Qwen 2.5 32B for quality reasoning? → RTX 4090 ($1,600). Same reasoning as above. Q4_K_M quality rivals many 70B models at half the VRAM.
Running Qwen 2.5 72B locally? → RTX 5090 ($2,000) for Q3_K_M single-card, 2x RTX 4090 ($3,200) for Q4_K_M quality.
Common mistakes to avoid
- Overlooking Qwen 32B in favor of 72B — Qwen 2.5 32B rivals many 70B models in reasoning quality while fitting on a single RTX 4090. It is one of the best intelligence-per-dollar options available locally.
- Buying 8GB VRAM for Qwen 7B — Qwen 7B fits at Q4 in 8GB, but you cannot run the excellent 14B variant at all. A 16GB card opens up both models.
- Ignoring Qwen's long context capability — Qwen 2.5 supports 32K+ context natively. This capability requires significant KV cache VRAM; a 16GB card will run out at 32K on even the 7B model.
- Not checking the Coder variant — Qwen 2.5-Coder models are trained specifically for code generation. If coding is your primary use case, the Coder variant outperforms the base model at equivalent sizes.
Our recommendation
| Your goal | Best GPU | Price |
|---|---|---|
| Qwen 7B daily use | RTX 4060 Ti 16GB | ~$400 |
| Qwen 14B comfortable | RTX 4070 Ti Super | ~$700 |
| Qwen 32B (best value) | RTX 4090 | ~$1,600 |
| Qwen 32B (best quality) | RTX 5090 | ~$2,000 |
| Qwen 72B | 2x RTX 4090 | ~$3,200 |
Qwen 2.5 32B on an RTX 4090 is one of the best price-to-intelligence ratios in local LLM right now. If your budget allows, start there.
See the recommended pick on the original guide
See the recommended pick on the original guide
If you run multiple Qwen variants through Ollama, keep in mind that Ollama loads one model at a time by default, so your VRAM only needs to fit the largest model you plan to run. For Qwen 3 itself, see our best GPU for Qwen 3 guide, and for the latest release in the family our best GPU for Qwen 3.6 guide. For VRAM planning across all models, the VRAM requirements guide covers every size systematically.
Related guides on Best GPU for LLM
- Best GPU for Qwen 3.6 in 2026 (35B-A3B MoE Guide)
- Best Budget GPU for Local LLM 2026: RTX 3060 to $350
- Best GPU for Continue.dev (Local AI Coding) in 2026
Continue on Best GPU for LLM for the complete guide with interactive calculators and current GPU prices.
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