B300 vs RTX 4090
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 4090
The NVIDIA RTX 4090 delivers high-performance computing capabilities for AI, machine learning, and data science applications.
B300 vs RTX 4090: Which Should You Choose?
The B300 offers 288 GB of VRAM — 12× the 24 GB on the RTX 4090 — making it better suited for large model workloads that require holding more parameters in GPU memory. On FP16 throughput, the RTX 4090 delivers 82.58 TFLOPS versus 1 TFLOPS on the B300 — 83× faster for mixed-precision training and inference. Memory bandwidth favors the B300 at 0.01 TB/s compared to 0.00 TB/s on the RTX 4090, which directly impacts inference latency for memory-bandwidth-bound models. Architecturally, the B300 is built on Blackwell Ultra while the RTX 4090 uses Ada Lovelace, reflecting different generational capabilities and optimizations. On Shadeform, the RTX 4090 starts from $0.60/hr versus $7.40/hr for the B300 — 1133% more expensive — reflecting the performance premium. The RTX 4090 is available across 3 cloud providers on Shadeform compared to 1 for the B300, 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
- ✓Your preferred provider already has availability
RTX 4090 — Best Use Cases
- •LLM inference and model serving
- •Image generation and diffusion models
- •Smaller fine-tuning runs
- •Cost-efficient GPU compute
Choose RTX 4090 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
- ✓You need flexibility across multiple cloud providers or regions
See how the B300 & RTX 4090 compare
Compare detailed hardware specifications and average pricing for the B300 and RTX 4090.
Compare Hardware Specifications
| B300 | RTX 4090 | |
|---|---|---|
| GPU Type | B300 | RTX 4090 |
| VRAM per GPU | 288 GB | 24 GB |
| Manufacturer | NVIDIA | NVIDIA |
| Architecture | Blackwell Ultra | Ada Lovelace |
| Interconnect | SXM6 | PCIe Gen4 |
| Memory Bandwidth | 8 TB/s | 1.008 TB/s |
| FP16 TFLOPS | 1,231.8 TFLOPS (16:1) | 82.58 TFLOPS (1:1) |
| CUDA Cores | 20480 | 16384 |
| Tensor Cores | 640 (5th Gen) | 512 (4th Gen) |
| RT Cores | N/A | 128 (3rd Gen) |
| Base Clock | 1665 MHz | 2235 MHz |
| Boost Clock | 2032 MHz | 2520 MHz |
| TDP | 1000W | 450W |
| 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 4090 | |
|---|---|---|
| 1 GPU | $7.40 /hr | $0.60 /hr |
| 2 GPUs | $14.80 /hr | $1.20 /hr |
| 4 GPUs | $29.20 /hr | $2.40 /hr |
| 8 GPUs | $57.56 /hr | $3.97 /hr |
Frequently Asked Questions: B300 vs RTX 4090
The main differences are VRAM (288 GB vs 24 GB), FP16 throughput (1 vs 82.58 TFLOPS), architecture (Blackwell Ultra vs Ada Lovelace). The B300 uses the Blackwell Ultra architecture while the RTX 4090 is based on Ada Lovelace, giving each GPU different generational capabilities.
The RTX 4090 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 RTX 4090 is available from $0.60/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 RTX 4090. 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 4090 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 RTX 4090 is currently available across 3 cloud providers on Shadeform's network, compared to 1 for the B300. 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 4090. Shadeform supports on-demand clusters of up to 64 GPUs of the same type with no commitment required.
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