Qwen3 14B · 45 GPUs measured first-party · llama.cpp Q4_K_M · Updated October 2026

What GPU Do You Need for Qwen3 14B?

Qwen3 14B is the middle child of the lineup: noticeably smarter than the 8B, but in our view not yet the tier where local work gets genuinely good, that starts at 30B-A3B and above. We measured it on 45 GPUs (llama.cpp, Q4_K_M): 165 tok/s on the B300, ~10GB peak VRAM, which makes 12GB the realistic minimum card class.

Benchmarked weights: Qwen/Qwen3-14B-GGUF

Fastest we measured

NVIDIA B300

165.4 tok/s on Qwen3 14B, the ceiling. Measured on our bench. 288GB of VRAM, $40,000 at launch.

Pros

• 165.4 tok/s on Qwen3 14B
• 288GB, clears the Qwen3 14B floor
• Rentable by the hour rather than bought

Cons

• 1400W board rating
• Datacenter or workstation hardware, not a retail purchase

Best consumer card

NVIDIA GeForce RTX 5090

143.1 tok/s on Qwen3 14B, fastest card you can buy at retail. Measured on our bench. 32GB of VRAM, $1,999 at launch.

Pros

• 143.1 tok/s on Qwen3 14B
• 32GB, clears the Qwen3 14B floor

Cons

• 575W board rating

Cheapest card that runs it

NVIDIA GeForce RTX 3060

36.45 tok/s on Qwen3 14B, lowest launch price that still fits. Measured on our bench. 12GB of VRAM, $329 at launch.

Pros

• 36.45 tok/s on Qwen3 14B
• 12GB, clears the Qwen3 14B floor

Cons

• 170W board rating

Best value

GeForce RTX 5070

60.87 tok/s on Qwen3 14B, most speed per dollar. Measured on our bench. 12GB of VRAM, $549 at launch. That is 110.9 tok/s per $1,000 of launch price.

Pros

• 60.87 tok/s on Qwen3 14B
• 12GB, clears the Qwen3 14B floor

Cons

• 250W board rating

Honest positioning. With 14B you can start doing real local LLM work. It holds context better and hallucinates less than the 8B. But my honest advice is that if your hardware allows it, step up: the 30B-A3B MoE needs 20GB but generates nearly twice as fast in our measurements (309 vs 165 tok/s at the top of each chart) while being a stronger model. 14B is the right stop when your card has 12GB and nothing more: a $179 Arc B580 or an RTX 3060 12GB fits it, and nothing in the 30B class ever will.

Fit and throughput notes. The ~10GB measured peak makes 12GB cards a real but snug fit. Long contexts will push against the ceiling, so keep expectations modest there. On speed, the usual pattern holds: B300 wins the headline at 165 tok/s and 315W, the H200 does 155 tok/s at 123W, 2.4× the efficiency for a 6% speed loss. At the bottom, the L4 manages 28 tok/s, which is workable for a single user but nothing more.

About Qwen3 14B. Qwen3 14B: from Qwen, 15B parameters, on Hugging Face since April 2025, Apache 2.0 licence. 8,315,943 downloads in the last 30 days and 4 community quantizations.

How it compares. H100 80GB HBM3: Qwen3 14B 151.2 tok/s, Qwen2.5-Coder-14B 144.8 (15B), Qwen2.5-14B 144.7 (15B), DeepSeek-Coder-V2-Lite 309.1 (16B), Ornith 1.5 9B 221.4 (10B). 2 of 4 beat Qwen3 14B here.

Cost on a rented GPU. 1M generated tokens of Qwen3 14B: $0.27 on a RTX 3060 ($0.036/hr, 7.6 hours), $11.65 on a B300 ($6.94/hr, 101 min, 42.5x the cost).

Speed tiers for Qwen3 14B. 30+ tok/s: 41 (RTX 5090, RTX 4090, RTX 3090 Ti); 10-30 tok/s: 4 (GTX 1080 Ti). 30 tok/s is roughly where replies outpace reading.

Reading your prompt. Before Qwen3 14B writes anything it reads the input: 8106.4 tok/s on the RTX 5090 (0.5s for a 4,000-token prompt), 6183.0 on the RTX 4090 (0.6s), 426.1 on the TITAN Xp (power capped) (9.4s). Long documents and big code files feel this number more than the generation speed.

VRAM for Qwen3 14B. Measured peak 8.5GB, so 12GB is the smallest common card size; smallest card it ran on: RTX 3080 (10GB). With long context: Q4_K_M 11GB (tested), Q2_K 8GB, Q3_K_M 9GB, Q5_K_M 13GB, Q6_K 15GB.

Power on Qwen3 14B. Most efficient: RTX PRO 6000 Blackwell Workstation Edition, 175W, 0.35 kWh per 1M generated tokens. Hungriest: RTX 3090 Ti, 319W, 0.96 kWh. At $0.15/kWh: $0.052 per 1M generated tokens.

Our verdict

Qwen3 14B: 165 tok/s peak, ~10GB floor: the best model that fits a 12GB card, and that's exactly how we'd use it. If you have 24GB, skip it for the 30B-A3B, which is both faster and stronger. Every number is first-party llama.cpp measurement with logged power.

FAQ

Can a 12GB card run Qwen3 14B?

Yes, ~10GB measured peak at Q4_K_M fits a 12GB card like the $179 Arc B580 or RTX 3060 12GB, though long contexts run close to the ceiling. 8GB cards don't fit it.

Should I run 14B or step up to 30B-A3B?

If you have 24GB of VRAM, step up: Qwen3 30B-A3B measured nearly twice as fast (309 vs 165 tok/s at the top of each chart) because only ~3B parameters are active per token, and it's a stronger model. 14B is the right choice specifically for 12GB cards.

How fast is Qwen3 14B on a datacenter card?

B300: 165 tok/s. H200: 155 tok/s at 2.4× better tokens-per-watt (1.26 vs 0.52). For a single-user session, anything above ~30 tok/s feels instant, even the A10G's 51 tok/s is comfortable.

Is 14B good enough for coding?

It's serviceable, but the coder-tuned models are the better tool: Qwen2.5-Coder 14B at the same size, or Qwen3 Coder 30B-A3B if you have the VRAM. We benchmark all of them on the same harness.

What quantization did you test?

Q4_K_M via llama.cpp: the quality-per-GB sweet spot most local users run, and the same pinned setup as every LLM on this site, so cross-model comparisons are apples to apples.