B300 vs A10
Explore a head to head comparison of specifications, performance, and pricing.
B300
The NVIDIA B300 delivers high-performance computing capabilities for AI, machine learning, and data science applications.
A10
The NVIDIA A10 delivers high-performance computing capabilities for AI, machine learning, and data science applications.
B300 vs A10: Which Should You Choose?
The B300 offers 288 GB of VRAM — 12× the 24 GB on the A10 — making it better suited for large model workloads that require holding more parameters in GPU memory. On FP16 throughput, the A10 delivers 31.24 TFLOPS versus 1 TFLOPS on the B300 — 31× faster for mixed-precision training and inference. Memory bandwidth favors the A10 at 0.60 TB/s compared to 0.01 TB/s on the B300, which directly impacts inference latency for memory-bandwidth-bound models. Architecturally, the B300 is built on Blackwell Ultra while the A10 uses Ampere, reflecting different generational capabilities and optimizations. On Shadeform, the A10 starts from $1.29/hr versus $7.40/hr for the B300 — 474% more expensive — reflecting the performance premium. The B300 is available across 2 cloud providers on Shadeform compared to 1 for the A10, giving more options for region and pricing flexibility.
B300 — Best Use Cases
- •Next-generation LLM pre-training at scale
- •Trillion-parameter model inference
- •Ultra-high-throughput AI workloads
- •Advanced HPC and scientific computing
Choose B300 when:
- ✓You need 288 GB+ VRAM for large models or long context windows
- ✓Maximum performance justifies the higher cost
- ✓Your workload does not require peak FP16 throughput
- ✓You need flexibility across multiple cloud providers or regions
A10 — Best Use Cases
- •General-purpose deep learning training
- •Fine-tuning models up to 13B parameters
- •AI inference at moderate throughput
- •Computer vision and NLP workloads
Choose A10 when:
- ✓24 GB VRAM is sufficient for your workload
- ✓Cost efficiency is your primary concern
- ✓You are training large models or running high-throughput inference
- ✓Your preferred provider already has availability
See how the B300 & A10 compare
Compare detailed hardware specifications and average pricing for the B300 and A10.
Compare Hardware Specifications
| B300 | A10 | |
|---|---|---|
| GPU Type | B300 | A10 |
| VRAM per GPU | 288 GB | 24 GB |
| Manufacturer | NVIDIA | NVIDIA |
| Architecture | Blackwell Ultra | Ampere |
| Interconnect | SXM6 | PCIe Gen4 |
| Memory Bandwidth | 8 TB/s | 600 GB/s |
| FP16 TFLOPS | 1,231.8 TFLOPS (16:1) | 31.24 TFLOPS (1:1) |
| CUDA Cores | 20480 | 9216 |
| Tensor Cores | 640 (5th Gen) | 288 (3rd Gen) |
| RT Cores | N/A | 72 (2nd Gen) |
| Base Clock | 1665 MHz | 885 MHz |
| Boost Clock | 2032 MHz | 1695 MHz |
| TDP | 1000W | 150W |
| Process Node | TSMC 4NP | TSMC 8nm |
| Data Formats | FP4, FP6, FP8, INT8, BF16, FP16, TF32, FP32, FP64 | INT4, INT8, BF16, FP16, TF32, FP32 |
Compare Average On-Demand Pricing
| B300 | A10 | |
|---|---|---|
| 1 GPU | $7.40 /hr | $1.29 /hr |
| 2 GPUs | $14.80 /hr | N/A |
| 4 GPUs | $29.20 /hr | N/A |
| 8 GPUs | $64.81 /hr | N/A |
Frequently Asked Questions: B300 vs A10
The main differences are VRAM (288 GB vs 24 GB), FP16 throughput (1 vs 31.24 TFLOPS), architecture (Blackwell Ultra vs Ampere). The B300 uses the Blackwell Ultra architecture while the A10 is based on Ampere, giving each GPU different generational capabilities.
The A10 is generally better for large language model training due to its higher throughput and 24 GB of VRAM, which allows fitting larger models or larger batch sizes in a single pass. For smaller models or fine-tuning tasks where cost matters more, both GPUs can be effective.
On Shadeform, the A10 is available from $1.29/hr. The B300 starts from $7.40/hr. Prices vary by provider, region, and contract length. Reserved commitments can reduce hourly costs significantly compared to on-demand pricing.
The B300 has more VRAM at 288 GB, compared to 24 GB on the A10. Higher VRAM allows you to run larger models without quantization, use longer context windows, and process larger batch sizes — all of which improve throughput and reduce latency for memory-bound workloads.
Based on TFLOPS per dollar, the A10 offers better raw compute value at current Shadeform on-demand rates. However, the best choice depends on your specific workload — if you need the extra VRAM or throughput of the B300, paying the premium may be justified by faster job completion and lower total cost.
The B300 is currently available across 2 cloud providers on Shadeform's network, compared to 1 for the A10. Shadeform lets you deploy either GPU across all available providers from a single platform, so you can always find available capacity without manually checking each cloud.
Mixing different GPU types in a single training cluster is generally not recommended, as it creates performance bottlenecks where faster GPUs wait for slower ones. For best results, use a homogeneous cluster of either B300 or A10. Shadeform supports on-demand clusters of up to 64 GPUs of the same type with no commitment required.
Explore B300 & A10 Instances
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