B300 vs RTX 4000 Ada
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.
RTX 4000 Ada
The NVIDIA RTX 4000 Ada delivers high-performance computing capabilities for AI, machine learning, and data science applications.
B300 vs RTX 4000 Ada: Which Should You Choose?
The B300 offers 288 GB of VRAM — 14× the 20 GB on the RTX 4000 Ada — making it better suited for large model workloads that require holding more parameters in GPU memory. On FP16 throughput, the RTX 4000 Ada delivers 26.73 TFLOPS versus 1 TFLOPS on the B300 — 27× faster for mixed-precision training and inference. Memory bandwidth favors the RTX 4000 Ada at 0.36 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 RTX 4000 Ada uses Ada Lovelace, reflecting different generational capabilities and optimizations. On Shadeform, the RTX 4000 Ada starts from $0.79/hr versus $7.40/hr for the B300 — 837% more expensive — reflecting the performance premium. The B300 is available across 2 cloud providers on Shadeform compared to 1 for the RTX 4000 Ada, 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
RTX 4000 Ada — Best Use Cases
- •LLM inference and model serving
- •Image generation and diffusion models
- •Smaller fine-tuning runs
- •Cost-efficient GPU compute
Choose RTX 4000 Ada when:
- ✓20 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 & RTX 4000 Ada compare
Compare detailed hardware specifications and average pricing for the B300 and RTX 4000 Ada.
Compare Hardware Specifications
| B300 | RTX 4000 Ada | |
|---|---|---|
| GPU Type | B300 | RTX 4000 Ada |
| VRAM per GPU | 288 GB | 20 GB |
| Manufacturer | NVIDIA | NVIDIA |
| Architecture | Blackwell Ultra | Ada Lovelace |
| Interconnect | SXM6 | PCIe Gen4 |
| Memory Bandwidth | 8 TB/s | 360 GB/s |
| FP16 TFLOPS | 1,231.8 TFLOPS (16:1) | 26.73 TFLOPS (1:1) |
| CUDA Cores | 20480 | 6144 |
| Tensor Cores | 640 (5th Gen) | 192 (4th Gen) |
| RT Cores | N/A | 48 (3rd Gen) |
| Base Clock | 1665 MHz | 1500 MHz |
| Boost Clock | 2032 MHz | 2175 MHz |
| TDP | 1000W | 130W |
| Process Node | TSMC 4NP | TSMC 4N |
| Data Formats | FP4, FP6, FP8, INT8, BF16, FP16, TF32, FP32, FP64 | FP8, INT8, BF16, FP16, TF32, FP32 |
Compare Average On-Demand Pricing
| B300 | RTX 4000 Ada | |
|---|---|---|
| 1 GPU | $7.40 /hr | $0.79 /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 RTX 4000 Ada
The main differences are VRAM (288 GB vs 20 GB), FP16 throughput (1 vs 26.73 TFLOPS), architecture (Blackwell Ultra vs Ada Lovelace). The B300 uses the Blackwell Ultra architecture while the RTX 4000 Ada is based on Ada Lovelace, giving each GPU different generational capabilities.
The RTX 4000 Ada is generally better for large language model training due to its higher throughput and 20 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 RTX 4000 Ada is available from $0.79/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 20 GB on the RTX 4000 Ada. 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 RTX 4000 Ada 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 RTX 4000 Ada. 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 RTX 4000 Ada. Shadeform supports on-demand clusters of up to 64 GPUs of the same type with no commitment required.
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