GPU Comparison

Select up to 2 GPUs to analyze their pricing, performance, and specifications side-by-side.

Workstation Verdict

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 $53,000 JPY cheaper than the RTX A1000.

Maximum Capacity Reached. Remove a model to add another. (2/2)

VS
NVIDIA
RTX A1000
Price
¥89,980
VRAM
8 GB GDDR6✓
Mem. Speed
224 GB/s✓
FP32 Compute
6 TFLOPS✓
Key Specs Advantage
+100% Memory Bus (128-bit vs 64-bit)
+100% FP32 (TFLOPS) (6 TFLOPS vs 3 TFLOPS)
+75% Bandwidth (224 GB/s vs 128 GB/s)
NVIDIA
RTX A400
Price
¥36,980✓
VRAM
4 GB GDDR6
Mem. Speed
128 GB/s
FP32 Compute
3 TFLOPS
Key Specs Advantage

Comparable or lower specs

Skip to Spec Sheets ↓

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 $36,980 JPY, $53,000 JPY less than the RTX A1000 at $89,980 JPY. 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 ($53,000 JPY 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?

Both can, but the RTX A1000 (8GB) handles larger models without quantization. The RTX A400 (4GB) works well for smaller or heavily quantized models.

Which is faster for LLM inference, the RTX A1000 or the RTX A400?

The RTX A1000 is faster for token generation — its 224 GB/s memory bandwidth vs 128 GB/s on the RTX A400 is the primary driver of inference throughput in autoregressive models.

Which is better for AI training?

The RTX A1000 has the advantage at 6 TFLOPS vs 3 TFLOPS, making training runs proportionally faster than on the RTX A400.

Technical Specifications Comparison

Architecture & Cores

SpecificationRTX A1000RTX A400
ArchitectureAda LovelaceAda Lovelace
CUDA Cores (CUDA Cores / CUDA Cores)1,280✓768

Memory

SpecificationRTX A1000RTX A400
VRAM Capacity8 GB✓4 GB
Memory TypeGDDR6GDDR6
Memory Bus128-bit✓64-bit
Bandwidth224 GB/s✓128 GB/s

Connectivity & Power

SpecificationRTX A1000RTX A400
InterfacePCIe 4.0 x16PCIe 4.0 x16
TDP50 W50 W
ReleasedAug 2023Aug 2023

Workstation

SpecificationRTX A1000RTX A400
FP32 (TFLOPS)6 TFLOPS✓3 TFLOPS
ECCYesYes
NVLinkNoNo
Form factorlow-profilelow-profile
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