Qwen2-7B-Instruct · 4 GPUs measured first-party · llama.cpp · Updated October 2026
What GPU Do You Need for Qwen2-7B-Instruct?
Qwen2-7B-Instruct on 4 GPUs, measured first-party: NVIDIA GeForce RTX 5090 leads at 277.6 tok/s, RTX 4060 Ti 16GB trails at 58.8 tok/s, and it peaked at 5GB of VRAM.
Benchmarked weights: Qwen/Qwen2-7B-Instruct-GGUF
NVIDIA GeForce RTX 5090
277.6 tok/s on Qwen2-7B-Instruct, the ceiling. Measured on our bench. 32GB of VRAM, $1,999 at launch.
- 277.6 tok/s on Qwen2-7B-Instruct
- 32GB, clears the Qwen2-7B-Instruct floor
- 575W board rating
NVIDIA GeForce RTX 4090
177.4 tok/s on Qwen2-7B-Instruct, fastest card you can buy at retail. Measured on our bench. 24GB of VRAM, $1,599 at launch.
- 177.4 tok/s on Qwen2-7B-Instruct
- 24GB, clears the Qwen2-7B-Instruct floor
- 450W board rating
NVIDIA GeForce RTX 3060
68.27 tok/s on Qwen2-7B-Instruct, lowest launch price that still fits. Measured on our bench. 12GB of VRAM, $329 at launch.
- 68.27 tok/s on Qwen2-7B-Instruct
- 12GB, clears the Qwen2-7B-Instruct floor
- 170W board rating
NVIDIA GeForce RTX 4060 Ti 16GB
58.84 tok/s on Qwen2-7B-Instruct, most speed per dollar. Measured on our bench. 16GB of VRAM, $499 at launch. That is 117.9 tok/s per $1,000 of launch price.
- 58.84 tok/s on Qwen2-7B-Instruct
- 16GB, clears the Qwen2-7B-Instruct floor
- 165W board rating
What GPU Do You Need for Qwen2-7B-Instruct?, tok/s by GPU
Efficiency: tok/s per 100W drawn
Power is the average pulled during the run, sampled at 1Hz. The fastest card is often not the one here, and for anything left running this is the number that shows up on the bill.
Value: tok/s per $1,000 of MSRP
Launch price, not street price, so it ages. A speed leaderboard always crowns the most expensive card; this is the counterweight.
Qwen2-7B-Instruct. Measured tokens per second by GPU
| GPU | tok/s | Prompt t/s | tok/W | Avg power |
|---|---|---|---|---|
| NVIDIA GeForce RTX 5090 | 277.6 | 15037.7 | 0.96 | 290.3 W |
| NVIDIA GeForce RTX 4090 | 177.4 | 11259 | 0.94 | 188.8 W |
| NVIDIA GeForce RTX 3060 | 68.27 | 2395.2 | 0.51 | 134.6 W |
| NVIDIA GeForce RTX 4060 Ti 16GB | 58.84 | 3727.1 | 0.54 | 109.3 W |
What the numbers show. Across 4 GPUs measured on our own bench, RTX 5090 is fastest at 278 tok/s. The slowest, RTX 4060 Ti 16GB, manages 58.8, so the spread is 4.7x from top to bottom. Per dollar of launch price, RTX 3060 gives the most (207.5 tok/s per $1,000). The fastest card with 16GB or less is RTX 3060 at 68.3 tok/s.
About Qwen2-7B-Instruct. Qwen2-7B-Instruct: from Qwen, 7.6B parameters, on Hugging Face since June 2024, Apache 2.0 licence. 504,440 downloads in the last 30 days and 1 community quantizations.
How it compares. RTX 5090: Qwen2-7B-Instruct 277.6 tok/s, Qwen2.5-7B 284.6 (8B), Qwen2.5-Coder 7B 284.6 (8B), OLMo 3 7B Think 266.7 (7B), OLMo 3 7B Instruct 266.7 (7B). 2 of 4 beat Qwen2-7B-Instruct here.
Cost on a rented GPU. 1M generated tokens of Qwen2-7B-Instruct: $0.15 on a RTX 3060 ($0.036/hr, 4.1 hours), $0.39 on a RTX 5090 ($0.39/hr, 60 min, 2.7x the cost).
Qwen2-7B-Instruct: cost per 1M generated tokens on rented GPUs
| GPU | Cheapest rate | Speed (tok/s) | Cost per 1M generated tokens |
|---|---|---|---|
| NVIDIA GeForce RTX 3060 | $0.036/hr | 68.27 | $0.15 |
| NVIDIA GeForce RTX 5090 | $0.39/hr | 277.6 | $0.39 |
| NVIDIA GeForce RTX 4090 | $0.34/hr | 177.4 | $0.53 |
Cheapest hourly rate we track on RunPod and Vast.ai, divided by the measured speed. Startup time and storage are extra.
Speed tiers for Qwen2-7B-Instruct. 30+ tok/s: 4 (RTX 5090, RTX 4090, RTX 3060). 30 tok/s is roughly where replies outpace reading.
Reading your prompt. Before Qwen2-7B-Instruct writes anything it reads the input: 15037.7 tok/s on the RTX 5090 (0.3s for a 4,000-token prompt), 11259.0 on the RTX 4090 (0.4s), 2395.2 on the RTX 3060 (1.7s). Long documents and big code files feel this number more than the generation speed.
VRAM for Qwen2-7B-Instruct. Measured peak 4.6GB, so 8GB is the smallest common card size; smallest card it ran on: RTX 3060 (12GB). With long context: Q4_K_M 6GB (tested), Q2_K 4GB, Q3_K_M 5GB, Q5_K_M 7GB, Q6_K 9GB.
Power on Qwen2-7B-Instruct. Most efficient: RTX 5090, 290W, 0.29 kWh per 1M generated tokens. At $0.15/kWh: $0.044 per 1M generated tokens.
Our verdict
Fastest on Qwen2-7B-Instruct: NVIDIA GeForce RTX 5090, 277.6 tok/s. Cheapest consumer card that ran it: NVIDIA GeForce RTX 3060 ($329, 68.27 tok/s). Cheapest to rent per job: NVIDIA GeForce RTX 3060, $0.15 per 1M generated tokens.
FAQ
- What GPU do I need to run Qwen2-7B-Instruct?
- About 5GB. Cheapest consumer card that ran it: NVIDIA GeForce RTX 3060 (12GB, 68.27 tok/s).
- How fast is Qwen2-7B-Instruct on the NVIDIA GeForce RTX 5090?
- 277.6 tok/s at 290W, faster than every datacenter card we ran it on.
- How much does it cost to run Qwen2-7B-Instruct in the cloud?
- $0.15 per 1M generated tokens on a NVIDIA GeForce RTX 3060 at $0.036/hr, cheapest of 3 rentable cards we measured.
- Can I run Qwen2-7B-Instruct on a 12GB, 16GB or 24GB card?
- It used 4.6GB at the precision we tested. 12GB: yes; 16GB: yes; 24GB: yes.
- Is the RTX 5090 or the RTX 4090 faster for Qwen2-7B-Instruct?
- The RTX 5090: 277.6 vs 177.4 tok/s, 57% faster on our bench.
- Should I buy or rent a RTX 5090 for Qwen2-7B-Instruct?
- Its $1,999 launch price buys 5,139 rented hours at $0.39/hr, enough for about 5,136x 1M generated tokens of Qwen2-7B-Instruct. Buy only if you'll run more than that.
How we test
llama.cpp llama-bench at Q4_K_M, 512-token prompt and 128 generated tokens, three runs after a warmup, full GPU offload, with power and VRAM sampled throughout. Token generation is memory-bandwidth-bound, so the ranking tracks bandwidth closely, which makes it a fair guide to cards we haven't run yet.