BTC
ETH
USDT
XMR
LTC
GPU comparison · Hopper vs Blackwell Ultra
NVIDIA H100 vs B300.
Two generations apart. The B300 (Blackwell Ultra) has 3.6× the memory (288 GB of HBM3e against 80 GB of HBM3), 2.4× the bandwidth, about 2.3× the dense FP8 and BF16 throughput, FP4 tensor cores and NVLink 5. The H100 (Hopper) costs $2,240 less per GPU per month and keeps much stronger FP64.
- 80 vs 288 GBGPU memory
- 3.35 vs 8 TB/smemory bandwidth
- $1,779 vs $4,019per GPU, per month
H100 vs B300: which one to rent.
Choose the H100 if
- Your model, its KV cache and batch fit in 80 GB per GPU (up to about 60B parameters in FP8 by our sizing rule).
- You run FP64 simulation or HPC codes: 34 TFLOPS of FP64 against 1.25 on the B300.
- You want the lower monthly price per GPU: $1,779 against $4,019.
Choose the B300 if
- You serve very large models or long contexts: 288 GB per GPU holds about 220B parameters in FP8 by our sizing rule.
- You want the highest throughput per GPU: about 2.3× the dense FP8 and BF16 throughput, plus FP4 tensor cores.
- You want the lower price per GB of GPU memory: $13.95 against $22.24 a month.
H100 vs B300 specs, side by side.
Figures from NVIDIA’s published specifications, per GPU. Tensor figures are peak dense throughput.
| Per GPU | NVIDIA H100 | NVIDIA B300 | B300 / H100 |
|---|---|---|---|
| Architecture | Hopper, TSMC 4N | Blackwell Ultra, TSMC 4NP | — |
| Form factor | SXM5 module | SXM module (HGX B300) | — |
| GPU memory | 80 GB HBM3, 5,120-bit | 288 GB HBM3E | 3.6× |
| Memory bandwidth | 3.35 TB/s | Up to 8 TB/s | 2.4× |
| GPU-to-GPU link | NVLink 4, 900 GB/s per GPU | NVLink 5, 1.8 TB/s per GPU | 2× |
| FP4 tensor (dense) | Not supported | 13.5 PFLOPS | — |
| FP8 tensor (dense) | 1,979 TFLOPS | 4,500 TFLOPS | 2.3× |
| FP16 / BF16 tensor (dense) | 989 TFLOPS | 2,250 TFLOPS | 2.3× |
| FP64 | 34 / 67 TFLOPS | 1.25 TFLOPS | 0.04× |
| Max power | Up to 700 W, configurable | Not published | — |
| GPUs per server | 1, 2, 4, 8 | 1, 2, 4, 8 | — |
| Per GPU in our servers | 20 vCPU · 200 GB RAM · 2 TB | 32 vCPU · 384 GB RAM · 3.84 TB | — |
Sources: NVIDIA H100 · NVIDIA HGX · Blackwell Ultra technical blog. Full sheets: H100 · B300
H100 vs B300 rental price.
At GPUCenter, the H100 rents for $1,779 and the B300 for $4,019 per GPU per month, 30% below the market median of public on-demand prices, paid in crypto with no KYC. Hourly figures are the monthly price divided by 730 hours.
| GPUCenter | NVIDIA H100 | NVIDIA B300 | B300 / H100 |
|---|---|---|---|
| Price per GPU, per month | $1,779 | $4,019 | 2.3× |
| Equivalent per GPU-hour | $2.44 | $5.51 | — |
| Per GB of GPU memory, per month | $22.24 | $13.95 | 0.63× |
| Per PFLOPS of dense FP8, per month | $899 | $893 | Same |
| Largest server | 8× · $14,232/mo | 8× · $32,152/mo | — |
| Market median, per GPU | $2,548 | $5,745 | — |
| Below the median | −30% | −30% | — |
Same price per GPU at every server size, no hourly metering. How we compare prices · Configure an H100 server · Configure a B300 server
Which models fit on each.
Smallest server that holds each model, by precision. Rule of thumb with headroom for the KV cache, 92% of GPU memory usable.
| Model (total parameters) | H100 · FP8 | B300 · FP8 | H100 · 4-bit | B300 · 4-bit |
|---|---|---|---|---|
| Qwen3.5-9B | 1× | 1× | 1× | 1× |
| Gemma 4 31B | 1× | 1× | 1× | 1× |
| Llama 3.3 70B | 2× | 1× | 1× | 1× |
| gpt-oss-120b | 2× | 1× | 2× | 1× |
| DeepSeek-V4-Flash | 8× | 2× | 4× | 1× |
| Qwen3.5-397B-A17B | 8× | 2× | 4× | 1× |
| DeepSeek-R1 (671B) | — | 4× | 8× | 2× |
| Kimi K2.6 (1T) | — | 8× | — | 4× |
FP8 ≈ 1.2 bytes and 4-bit ≈ 0.65 bytes per parameter. — = larger than the biggest server of that GPU. Size another model · How much VRAM does an LLM need?
H100 vs B300 FAQ.
More on each GPU: NVIDIA H100 · NVIDIA B300.
Is the B300 worth the extra cost over the H100?
When one GPU has to hold a large model, yes: one B300 has the memory of 3.6 H100 for about 2.3× the price, with 2.4× the bandwidth. For models that fit in 80 GB and compute-bound jobs, the H100 costs $2,240 less per GPU per month.
Do the H100 and B300 use the same software?
Yes. Both run the CUDA stack with PyTorch, vLLM, SGLang and TensorRT-LLM, and FP8 checkpoints run on both. The B300 adds FP4 tensor cores and NVLink 5.
What fits on one GPU?
By our rule of thumb, about 61B parameters in FP8 on one H100, and about 220B in FP8 or a 405B model in 4-bit on one B300.
Other GPU comparisons.
Rent the H100 or the B300.
Dedicated servers with 1 to 8 GPUs, one monthly price, paid in crypto. Online in under 10 minutes, no KYC.