Agents-A1-4B · 4 GPUs measured first-party · llama.cpp · Updated October 2026
Agents-A1-4B on 4 GPUs, measured first-party: NVIDIA GeForce RTX 5090 leads at 329.0 tok/s, RTX 4060 Ti 16GB trails at 83 tok/s, and it peaked at 4GB of VRAM.
Benchmarked weights: InternScience/Agents-A1-4B-Q4_K_M-GGUF

329.0 tok/s on Agents-A1-4B, the ceiling. Measured on our bench. 32GB of VRAM, $1,999 at launch.

219.4 tok/s on Agents-A1-4B, fastest card you can buy at retail. Measured on our bench. 24GB of VRAM, $1,599 at launch.

89.57 tok/s on Agents-A1-4B, lowest launch price that still fits. Measured on our bench. 12GB of VRAM, $329 at launch.

82.98 tok/s on Agents-A1-4B, most speed per dollar. Measured on our bench. 16GB of VRAM, $499 at launch. That is 166.3 tok/s per $1,000 of launch price.
What GPU Do You Need for Agents-A1-4B?, 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.
Agents-A1-4B. Measured tokens per second by GPU
| GPU | tok/s | Prompt t/s | tok/W | Avg power |
|---|---|---|---|---|
| NVIDIA GeForce RTX 5090 | 329 | 15558.2 | 1.34 | 244.9 W |
| NVIDIA GeForce RTX 4090 | 219.4 | 12084.4 | 1.37 | 159.7 W |
| NVIDIA GeForce RTX 3060 | 89.57 | 2620.6 | 0.69 | 129.9 W |
| NVIDIA GeForce RTX 4060 Ti 16GB | 82.98 | 4309.3 | 0.82 | 100.8 W |
What the numbers show. Across 4 GPUs measured on our own bench, RTX 5090 is fastest at 329 tok/s. The slowest, RTX 4060 Ti 16GB, manages 83.0, so the spread is 4.0x from top to bottom. RTX 4090 is the most efficient, 219 tok/s at 160W. Per dollar of launch price, RTX 3060 gives the most (272.2 tok/s per $1,000). The fastest card with 16GB or less is RTX 3060 at 89.6 tok/s.
About Agents-A1-4B. Agents-A1-4B: from InternScience, 4.5B parameters, on Hugging Face since July 2026, Apache 2.0 licence. 479,819 downloads in the last 30 days and 1 community quantizations.
How it compares. RTX 5090: Agents-A1-4B 329.0 tok/s, Spark-X2.5-4B 352.3 (4B), Qwen3-4B 375.6 (4B), Nemotron 3 Nano 4B 410.9 (4B), Granite 4.1 3B 375.4 (3B). All 4 beat Agents-A1-4B here.
Cost on a rented GPU. 1M generated tokens of Agents-A1-4B: $0.11 on a RTX 3060 ($0.036/hr, 3.1 hours), $0.33 on a RTX 5090 ($0.39/hr, 51 min, 2.9x the cost).
Agents-A1-4B: 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 | 89.57 | $0.11 |
| NVIDIA GeForce RTX 5090 | $0.39/hr | 329 | $0.33 |
| NVIDIA GeForce RTX 4090 | $0.34/hr | 219.4 | $0.43 |
Cheapest hourly rate we track on RunPod and Vast.ai, divided by the measured speed. Startup time and storage are extra.
Speed tiers for Agents-A1-4B. 30+ tok/s: 4 (RTX 5090, RTX 4090, RTX 3060). 30 tok/s is roughly where replies outpace reading.
Reading your prompt. Before Agents-A1-4B writes anything it reads the input: 15558.2 tok/s on the RTX 5090 (0.3s for a 4,000-token prompt), 12084.4 on the RTX 4090 (0.3s), 2620.6 on the RTX 3060 (1.5s). Long documents and big code files feel this number more than the generation speed.
VRAM for Agents-A1-4B. Measured peak 3.2GB, so 8GB is the smallest common card size; smallest card it ran on: RTX 3060 (12GB).
Power on Agents-A1-4B. Most efficient: RTX 4090, 160W, 0.20 kWh per 1M generated tokens. Hungriest: RTX 5090, 245W, 0.21 kWh. At $0.15/kWh: $0.030 per 1M generated tokens.
Fastest on Agents-A1-4B: NVIDIA GeForce RTX 5090, 329.0 tok/s. Cheapest consumer card that ran it: NVIDIA GeForce RTX 3060 ($329, 89.57 tok/s). Cheapest to rent per job: NVIDIA GeForce RTX 3060, $0.11 per 1M generated tokens.
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.