GPU Comparison
Select up to 2 GPUs to analyze their pricing, performance, and specifications side-by-side.
The RTX A1000 has more VRAM (8GB vs 4GB), making it better suited for large models and memory-intensive workloads. Its memory bandwidth is 75% higher (224 GB/s vs 128 GB/s), translating directly to faster inference throughput. The RTX A400 is $294 EUR cheaper than the RTX A1000.
Maximum Capacity Reached. Remove a model to add another. (2/2)
Comparable or lower specs
RTX A1000 vs RTX A400: In-Depth Breakdown
VRAM: RTX A1000 vs RTX A400
The RTX A1000 carries 8GB of VRAM versus 4GB on the RTX A400. VRAM capacity is the primary constraint for running AI models without quantization — a 70B-parameter model in FP16 requires roughly 140GB, and even smaller models benefit from extra headroom. The 4GB advantage here means the RTX A1000 can run larger models natively and handle bigger batch sizes in production.
Inference Speed: Memory Bandwidth
Memory bandwidth determines how quickly data is fed to the compute units — it's the main bottleneck for autoregressive inference (token generation in LLMs). The RTX A1000 delivers 224 GB/s versus 128 GB/s on the RTX A400, a 75% edge. For models already loaded into VRAM, token generation speed scales closely with this number: the RTX A1000 will produce tokens proportionally faster in bandwidth-bound workloads.
AI Training & Compute
For model training, scientific simulation, and rendering, FP32 throughput is the key metric. The RTX A1000 delivers 6 TFLOPS against 3 TFLOPS for the RTX A400 — a 100% compute advantage. Training runs and heavy matrix operations will complete proportionally faster on the RTX A1000.
Price & Value
The RTX A400 lists from $170 EUR, $294 EUR less than the RTX A1000 at $465 EUR. For budget-constrained teams, the savings may outweigh the spec gap — especially if the smaller card covers your typical workload.
Which should you buy: RTX A1000 or RTX A400?
Choose the RTX A1000 for maximum capacity — it leads on VRAM, bandwidth, and compute, making it the better fit for large models and training jobs. The RTX A400 is the more budget-friendly option ($294 EUR less) — a solid choice if your models fit within its 4GB and inference volume is moderate.
Frequently Asked Questions
Can the RTX A1000 or RTX A400 run large language models?
Which is faster for LLM inference, the RTX A1000 or the RTX A400?
Which is better for AI training?
Technical Specifications Comparison
Architecture & Cores
| Specification | RTX A1000 | RTX A400 |
|---|---|---|
| Architecture | Ada Lovelace | Ada Lovelace |
| CUDA Cores (CUDA Cores / CUDA Cores) | 1,280✓ | 768 |
Memory
| Specification | RTX A1000 | RTX A400 |
|---|---|---|
| VRAM Capacity | 8 GB✓ | 4 GB |
| Memory Type | GDDR6 | GDDR6 |
| Memory Bus | 128-bit✓ | 64-bit |
| Bandwidth | 224 GB/s✓ | 128 GB/s |
Connectivity & Power
| Specification | RTX A1000 | RTX A400 |
|---|---|---|
| Interface | PCIe 4.0 x16 | PCIe 4.0 x16 |
| TDP | 50 W | 50 W |
| Released | Aug 2023 | Aug 2023 |
Workstation
| Specification | RTX A1000 | RTX A400 |
|---|---|---|
| FP32 (TFLOPS) | 6 TFLOPS✓ | 3 TFLOPS |
| ECC | Yes | Yes |
| NVLink | No | No |
| Form factor | low-profile | low-profile |