Rent the B300 if…
your model, its KV cache or its experts need more than 180 GB per GPU, or you serve reasoning models at FP4 and want the fastest Blackwell available.
Deploy a B300 · $3,790/moQuestions? Open a ticket from your console — an engineer replies within one business day.
Blackwell Ultra against Blackwell: 288 GB of HBM3e on the B300 against 180 GB on the B200, the same 8 TB/s, and about 1.5× the dense FP4 throughput — for 1.4× the monthly price.
The short answer
The B300 is a B200 with 60% more memory and a faster FP4 engine. If your model and its KV cache fit in 180 GB, the B200 does the same Blackwell work for $1,100 less a month. If they do not — the largest mixture-of-experts models, contexts past 128k tokens at high concurrency, models whose experts must stay resident — the B300 saves you from sharding across more GPUs, which is usually worth more than the price difference.
your model, its KV cache or its experts need more than 180 GB per GPU, or you serve reasoning models at FP4 and want the fastest Blackwell available.
Deploy a B300 · $3,790/moyour model fits in 180 GB — most 70B-class training and inference does — and you want Blackwell throughput at the lower monthly price.
Deploy a B200 · $2,690/moSpecifications
NVIDIA's published figures for each part, tensor throughput without sparsity. The last column is how the B300 compares, as a multiple of the B200.
| Specification | NVIDIA B300 | NVIDIA B200 | B300 vs B200 |
|---|---|---|---|
| Architecture | Blackwell Ultra · 2025 | Blackwell · 2024 | — |
| GPU memory | 288 GB HBM3e | 180 GB HBM3e | 1.6× |
| Memory bandwidth | 8 TB/s | 8 TB/s | Same |
| FP16 / BF16 tensor (dense) | ~2,250 TFLOPS | ~2,250 TFLOPS | Same |
| FP8 tensor (dense) | ~4,500 TFLOPS | ~4,500 TFLOPS | Same |
| FP4 tensor (dense) | ~14,000 TFLOPS | ~9,000 TFLOPS | 1.6× |
| Maximum power | Up to 1,100 W | Up to 1,000 W | 1.1× |
| Form factor | SXM (HGX B300) | SXM (HGX B200) | — |
| GPU-to-GPU link | NVLink 5 · 1.8 TB/s | NVLink 5 · 1.8 TB/s | — |
| Multi-Instance GPU (MIG) | Up to 7 instances | Up to 7 instances | — |
| ECC memory | Yes | Yes | — |
| Video engines | 7 NVDEC · 7 NVJPG | 7 NVDEC · 7 NVJPG | — |
| CUDA compute capability | 10.3 | 10.0 | — |
| Dedicated host here | 32 vCPU · 512 GB RAM · 4 TB NVMe | 32 vCPU · 384 GB RAM · 4 TB NVMe | — |
| Price per month here | $3,790 | $2,690 | 1.4× |
Sources: NVIDIA datasheets and product pages for each GPU. Tensor figures are dense (NVIDIA often prints them doubled “with sparsity”). Prices are this site's flat monthly rates for a dedicated server with the host listed, in USD excluding VAT.
Value
Price per month divided into the two resources most jobs run out of first — memory and memory bandwidth — and what the same GPU costs kept on for a month at the market-median hourly rate.
Model sizes
The largest model whose weights fit on a single GPU, keeping 10% of the memory for the runtime, activations and a small KV cache. Long contexts and large batches need more — size them with the GPU memory calculator.
| Weights precision | NVIDIA B300 | NVIDIA B200 | Typical use |
|---|---|---|---|
| 16-bit (BF16 / FP16) | up to ~120B parameters | up to ~81B parameters | Full-precision inference; the base weights of a LoRA fine-tune |
| 8-bit (FP8 / INT8) | up to ~240B parameters | up to ~160B parameters | FP8 serving with vLLM or TensorRT-LLM on GPUs that support it; INT8 weights elsewhere |
| 4-bit (GPTQ, AWQ, Q4) | up to ~440B parameters | up to ~290B parameters | Quantised inference on a budget; QLoRA fine-tuning |
Parameters in billions, weights only: 2 bytes per parameter at 16-bit, 1 at FP8 or INT8, about 0.55 at 4-bit (GPTQ, AWQ, GGUF Q4). Mixture-of-experts models need memory for all their experts, not just the active ones.
Prices
Per dedicated GPU server, in USD excluding VAT — CPU cores, RAM, NVMe, 20 TB of transfer and 24/7 support included.
| Term | NVIDIA B300 | NVIDIA B200 | Difference per month |
|---|---|---|---|
| Monthly | $3,790 | $2,690 | $1,100 |
| 3 months · −5% | $3,601 | $2,556 | $1,045 |
| 6 months · −10% | $3,411 | $2,421 | $990 |
| 12 months · −15% | $3,222 | $2,287 | $935 |
| Dedicated host | 32 vCPU · 512 GB RAM · 4 TB NVMe · 25 Gbps | 32 vCPU · 384 GB RAM · 4 TB NVMe · 25 Gbps | included |
The B300 (Blackwell Ultra) carries 288 GB of HBM3e against 180 GB on the B200, with the same 8 TB/s of bandwidth and about 1.5× the dense FP4 throughput. Here it costs 1.4× the B200 per month.
Those whose weights, KV cache or experts exceed 180 GB per GPU once sharded: trillion-parameter-class mixture-of-experts models with room for their cache on one 8-GPU node, and long-context serving at high concurrency.
Yes: both are Blackwell data center GPUs, served by the same CUDA 12.8+ toolkits and framework builds. Code tuned for the B200 runs on the B300.
5 of 8 B300 units were free at the last stock update; 8 of 12 B200 units were free at the last stock update (18 September 2026). Both are provisioned automatically, in under 10 minutes after payment.
Yes. Both are rented with an email address and paid from a prepaid balance in Bitcoin, Ether or USDT — $3,790 and $2,690 a month, dedicated, with no identity check and no card.
Dedicated, single-tenant and live in under 10 minutes: the B300 at $3,790 or the B200 at $2,690 a month, paid in BTC, ETH or USDT with no KYC.