Qwen2 0.5B · 4 GPUs measured first-party · llama.cpp · Updated October 2026
What GPU Do You Need for Qwen2 0.5B?
Qwen2 0.5B on 4 GPUs, measured first-party: NVIDIA H100 80GB HBM3 leads at 881.9 tok/s, L4 trails at 397 tok/s, and it peaked at 2GB of VRAM.
Benchmarked weights: Qwen/Qwen2-0.5B-Instruct-GGUF
NVIDIA H100 80GB HBM3
881.9 tok/s on Qwen2 0.5B, the ceiling. Measured on our bench. 80GB of VRAM, $30,000 at launch.
- 881.9 tok/s on Qwen2 0.5B
- 80GB, clears the Qwen2 0.5B floor
- Rentable by the hour rather than bought
- 700W board rating
- Datacenter or workstation hardware, not a retail purchase
What GPU Do You Need for Qwen2 0.5B?, 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 0.5B. Measured tokens per second by GPU
| GPU | tok/s | Prompt t/s | tok/W | Avg power |
|---|---|---|---|---|
| NVIDIA H100 80GB HBM3 | 881.9 | 42556.6 | 6.02 | 146.5 W |
| NVIDIA L40S | 715.2 | 48479 | 7.72 | 92.7 W |
| NVIDIA A10G | 497 | 23523.8 | 6.59 | 75.4 W |
| NVIDIA L4 | 396.9 | 25843.8 | 10.39 | 38.2 W |
What the numbers show. Across 4 GPUs measured on our own bench, H100 80GB HBM3 is fastest at 882 tok/s. The slowest, L4, manages 397, so the spread is 2.2x from top to bottom. L4 is the most efficient, 397 tok/s at 38W. Per dollar of launch price, A10G gives the most (177.5 tok/s per $1,000).
About Qwen2 0.5B. Qwen2 0.5B: from Qwen, 0.5B parameters, on Hugging Face since June 2024, Apache 2.0 licence. 737,019 downloads in the last 30 days and 1 community quantizations.
How it compares. H100 80GB HBM3: Qwen2 0.5B 881.9 tok/s, Qwen2.5-0.5B 890.0, SmolLM2-360M 787.6, Qwen3 0.6B 713.5, gemma-3-1b 504.2 (1B). 1 of 4 beat Qwen2 0.5B here.
Cost on a rented GPU. 1M generated tokens of Qwen2 0.5B: $0.31 on a L40S ($0.79/hr, 23 min), $0.67 on a H100 80GB HBM3 ($2.14/hr, 19 min, 2.2x the cost).
Qwen2 0.5B: cost per 1M generated tokens on rented GPUs
| GPU | Cheapest rate | Speed (tok/s) | Cost per 1M generated tokens |
|---|---|---|---|
| NVIDIA L40S | $0.79/hr | 715.2 | $0.31 |
| NVIDIA L4 | $0.44/hr | 396.9 | $0.31 |
| NVIDIA H100 80GB HBM3 | $2.14/hr | 881.9 | $0.67 |
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 0.5B. 30+ tok/s: 4 (H100 80GB HBM3, L40S, A10G). 30 tok/s is roughly where replies outpace reading.
Reading your prompt. Before Qwen2 0.5B writes anything it reads the input: 48479.0 tok/s on the L40S (0.1s for a 4,000-token prompt), 23523.8 on the A10G (0.2s). Long documents and big code files feel this number more than the generation speed.
VRAM for Qwen2 0.5B. Measured peak 1.1GB, so 8GB is the smallest common card size; smallest card it ran on: A10G (24GB). With long context: Q4_K_M 1GB (tested), Q2_K 1GB, Q3_K_M 1GB, Q5_K_M 1GB, Q6_K 2GB.
Power on Qwen2 0.5B. Most efficient: L4, 38W, 26.7 Wh per 1M generated tokens. Hungriest: H100 80GB HBM3, 146W, 46.1 Wh.
Our verdict
Fastest on Qwen2 0.5B: NVIDIA H100 80GB HBM3, 881.9 tok/s. Cheapest to rent per job: NVIDIA L40S, $0.31 per 1M generated tokens.
FAQ
- What GPU do I need to run Qwen2 0.5B?
- About 1GB. Smallest card that ran it: NVIDIA A10G (24GB).
- How much does it cost to run Qwen2 0.5B in the cloud?
- $0.31 per 1M generated tokens on a NVIDIA L40S at $0.79/hr, cheapest of 3 rentable cards we measured.
- Can I run Qwen2 0.5B on a 12GB, 16GB or 24GB card?
- It used 1.1GB at the precision we tested. 12GB: yes; 16GB: yes; 24GB: yes.
- Is the H100 80GB HBM3 or the L40S faster for Qwen2 0.5B?
- The H100 80GB HBM3: 881.9 vs 715.2 tok/s, 23% faster on our bench.
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.