# B200 vs B300: is Blackwell Ultra worth the upgrade?

> B200 vs B300 compared: 180 vs 288 GB of HBM3e, the FP4 uplift and 400G vs 800G networking. See what fits, the cost per GB and when the B200 is enough.

Source: https://overgpu.com/compare/b200-vs-b300  
Updated: 2026-10-23  
Publisher: OverGPU (https://overgpu.com/)

**In short:** For FP8 and BF16 work that fits in 180 GB, the B200 does the same job for less and is in stock; the B300 earns its 13% premium with 288 GB, faster FP4 and 800G networking, if you can plan around limited stock.

## B200 vs B300 specifications

| Specification | NVIDIA B200 | NVIDIA B300 |
| --- | --- | --- |
| Architecture | Blackwell | Blackwell Ultra |
| GPU memory | 180 GB HBM3e | 288 GB HBM3e |
| Memory bandwidth | 8 TB/s | 8 TB/s |
| FP4 | 9,000 TFLOPS | 13,500 TFLOPS |
| FP8 | 4,500 TFLOPS | 4,500 TFLOPS |
| BF16 / FP16 | 2,250 TFLOPS | 2,250 TFLOPS |
| GPU interconnect | NVLink 5 · 1.8 TB/s | NVLink 5 · 1.8 TB/s |
| Form factor | SXM module · HGX B200 | SXM module · HGX B300 |
| Max power | 1,000 W | 1,100 W |

## Monthly prices at OverGPU

| Configuration | Type | GPU memory | CPU | RAM | Storage | Network | Price per month (USD) | Per GPU-hour | Availability |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| [NVIDIA B200](https://overgpu.com/gpus/nvidia-b200) | Single GPU | 180 GB HBM3e | 26 vCPU | 256 GB | 2.75 TB NVMe | 25 Gbps | $4,590 | $6.29 | In stock, delivered in under 10 minutes after payment |
| [8× NVIDIA HGX B200](https://overgpu.com/gpus/nvidia-b200) | 8-GPU server | 8 × 180 GB HBM3e | 2× Xeon 8570 · 112 cores | 2 TB DDR5 | 8× 3.84 TB NVMe | 8× 400G ConnectX-7 | $34,900 | $5.98 | In stock, delivered in under 10 minutes after payment |
| [NVIDIA B300](https://overgpu.com/gpus/nvidia-b300) | Single GPU | 288 GB HBM3e | 28 vCPU | 384 GB | 3.5 TB NVMe | 25 Gbps | $5,190 | $7.11 | Limited stock, delivered in under 10 minutes after payment |
| [8× NVIDIA HGX B300](https://overgpu.com/gpus/nvidia-b300) | 8-GPU server | 8 × 288 GB HBM3e | 2× Xeon 6776P · 128 cores | 4 TB DDR5 | 8× 3.84 TB NVMe | 8× 800G ConnectX-8 | $39,900 | $6.83 | Limited stock, delivered in under 10 minutes after payment |

Per GPU-hour = monthly price ÷ number of GPUs ÷ 730 hours, shown only to compare with hourly offers: every plan is billed as one flat monthly price.

## Choose the B200 if…

- Your model and KV cache fit in 180 GB per GPU and you want capacity that is in stock now.
- You run FP8 or BF16 workloads, where NVIDIA lists the same compute for both GPUs.
- Your jobs stay on a single server, where 800G networking brings no benefit.

## Choose the B300 if…

- You need more than 180 GB per GPU for large models, long contexts or big batches.
- You serve models in FP4 and want 50% more vendor-listed dense FP4 throughput.
- You train across several servers and want 800G per GPU between nodes.
- You want more GPU memory per dollar, about $18 per GB a month against $25.50 on the B200.

## Overview

The **B200 vs B300** choice is narrower than it looks. Both are Blackwell-generation GPUs with about 8 TB/s of memory bandwidth, NVLink 5 at 1.8 TB/s per GPU and, per NVIDIA's published specifications, the same dense FP8 (4.5 PFLOPS) and BF16 (2.25 PFLOPS) throughput. The B300, NVIDIA's Blackwell Ultra, changes three things: 288 GB of HBM3e instead of 180 GB, 13.5 PFLOPS of dense FP4 instead of 9, and, on our HGX servers, 800G networking per GPU instead of 400G. Everything on this page follows from those three differences.

## B200 vs B300: the short answer

If your work runs in FP8 or BF16 and fits in 180 GB per GPU, the [B200](https://overgpu.com/gpus/nvidia-b200) does the same job, costs $600 less per GPU and $5,000 less per 8-GPU server, and is in stock. The [B300](https://overgpu.com/gpus/nvidia-b300) is worth its premium when you are short of memory, when you serve in FP4, or when you train across several servers and need faster links between them.

> B300 stock is limited. Every order is delivered in under 10 minutes after payment.

## 288 GB vs 180 GB: what the extra memory changes

108 GB more per GPU is the B300's main advantage. The table compares approximate memory needs with capacity, before runtime overhead, for which you should keep 10–30% in reserve:

| Workload | Memory needed (approx.) | B200 | B300 |
| --- | --- | --- | --- |
| 70B model in BF16, 1 GPU | 140 GB of weights | About 40 GB free | About 148 GB free |
| 405B model in FP4, 1 GPU | About 200 GB of weights | Does not fit | About 85 GB free |
| 671B MoE in BF16, 8 GPUs | About 1,340 GB of weights | About 100 of 1,440 GB free: not workable | About 960 of 2,304 GB free |
| Full fine-tune of a 70B model with Adam, 8 GPUs | About 1,120–1,400 GB before activations | At or over the limit | Fits, with room for activations |

Extra memory also buys concurrency. Serving a 70B model in FP8 on one GPU, the B200 has about 110 GB left for the KV cache and the B300 about 218 GB, roughly twice as many tokens in flight. For long-context serving and large-batch inference, that can matter more than any compute figure. It can also change how many GPUs a model needs: a model that has to be split across two B200s may fit on one B300, which removes the communication between them entirely.

## Compute: the FP4 uplift and what stays the same

NVIDIA lists the B300 at 13.5 PFLOPS of dense FP4 against 9 PFLOPS for the B200, 50% more. Sparse FP4 is 18 PFLOPS on both, and dense FP8 and BF16 are identical. In practice:

- **Serving in FP4:** compute-bound phases such as prefill on long prompts and large-batch decoding can benefit from the higher dense FP4 peak.
- **Training and fine-tuning in BF16 or FP8:** no compute difference. The B300 helps only through memory: bigger micro-batches, longer sequences, fewer GPUs per model replica.
- **Bandwidth-bound decoding:** both have about 8 TB/s, so reading weights and KV cache runs at the same pace on either GPU.

FP4 brings its own trade-offs in accuracy and tooling: quality depends on the model and the quantization recipe, so validate on your own evaluation set. Our [FP8 and FP4 explainer](https://overgpu.com/blog/fp8-fp4-precision-explained) covers the formats.

## Which workloads gain the most from the B300

- **Mixture-of-experts models.** They activate only a fraction of their parameters per token, so compute per token is modest while every expert must stay in memory. Capacity, not compute, decides how many GPUs they need, which is exactly where the B300 differs.
- **Long-context serving.** The KV cache grows with context length and concurrency; twice the free memory means about twice the tokens in flight.
- **Several models on one GPU.** A main model plus a draft model for speculative decoding, an embedding model and a reranker fit together more easily in 288 GB.
- **Large fine-tunes on one node.** Full fine-tuning of a 70B model fits on one HGX B300 instead of spilling onto a second server.

A worked example: a mixture-of-experts model of about 670B parameters served in BF16 needs about 1,340 GB for its weights alone. One HGX B300 holds it with about 960 GB to spare, for $39,900 a month. On B200s it needs two HGX servers, $69,800 a month, plus the networking to link them. If your models fit in 180 GB per GPU, none of this applies, and the B200 is the better buy.

## 800G vs 400G: networking between servers

Inside the server, both HGX platforms connect eight GPUs over NVLink 5 at 1.8 TB/s per GPU, so single-node jobs see no difference. Between servers they differ: our HGX B300 has 8× 800G ConnectX-8 adapters, one per GPU, against 8× 400G ConnectX-7 on the HGX B200. Twice the per-GPU network bandwidth shortens gradient exchanges in data-parallel training and transfers in pipeline and expert parallelism across nodes. The host is larger too: two Xeon 6776P CPUs with 128 cores in total and 4 TB of DDR5, against two Xeon 8570 with 112 cores and 2 TB on the B200 server.

If you plan to link several servers, ask for [private networking](https://overgpu.com/products/private-networking) between them in the notes for our engineers when you order. On a single server, the 800G adapters alone are not a reason to choose the B300.

## Cost per month, cost per GB and stock

A single B300 costs $5,190 a month against $4,590 for a B200: $600 more, or 13%, for 60% more memory. The 8-GPU HGX B300 costs $39,900 against $34,900, $5,000 more (14%). Per GB of memory the B300 is clearly cheaper: about $18.02 against $25.50 a month on single-GPU plans, and $17.32 against $24.24 on 8-GPU servers. Per unit of bandwidth or of FP8 and BF16 compute, the B200 is cheaper, because those are equal and it costs less.

The single-GPU plans also differ beyond the GPU: the B300 comes with 28 vCPUs, 384 GB of RAM and 3.5 TB of NVMe, the B200 with 26 vCPUs, 256 GB and 2.75 TB. For deployments beyond one server, the [GB300 NVL72](https://overgpu.com/gpus/nvidia-gb300-nvl72) rack places 72 Blackwell Ultra GPUs in one NVLink domain, on 12–36 month terms.

## How to decide

1. Size the memory: weights at your precision, plus KV cache for your context and concurrency, plus 10–30% headroom. Under about 160 GB per GPU, the B200 covers it.
2. Check your precision: in FP8 or BF16 the compute is the same, so pay for the B300 only if you need the memory.
3. Check your topology: on one server either works; for several servers training together, the 800G links help.
4. Check stock: the B200 is in stock; the B300 has limited stock, and either one ordered on the [deployment page](https://overgpu.com/deploy) is delivered in under 10 minutes after payment.

Limited stock does not have to block you. Terms are monthly with no cancellation fee, so you can start on a B200 that is in stock today, order a B300 when you need it, delivered in under 10 minutes after payment, and cancel the B200 at the end of its paid month.

## Frequently asked questions

### Is the B300 faster than the B200?

Only for some workloads. NVIDIA lists the same dense FP8 and BF16 compute and about the same 8 TB/s of bandwidth for both; the B300 adds 50% more dense FP4 throughput and 60% more memory, which helps FP4 serving, long contexts and large batches.

### How much more does the B300 cost?

$5,190 a month against $4,590 for a single GPU, and $39,900 against $34,900 for an 8-GPU HGX server, about 13–14% more. Per GB of GPU memory, the B300 is about 29% cheaper.

### Does the 800G networking matter on a single server?

No. Inside one server both platforms use NVLink 5 at 1.8 TB/s per GPU; the 800G ConnectX-8 adapters matter when several servers work on the same job.

### Why is B300 stock limited?

GPU capacity is tight across the market in 2026, and Blackwell Ultra is the newest generation we offer in 8-GPU servers. A B300 in limited stock is still delivered in under 10 minutes after payment.

### Can I run the same software on both?

Yes. Both are Blackwell-generation NVIDIA GPUs and run the same CUDA software stack with a recent CUDA release and current framework builds.

## Related pages

- [Rent NVIDIA B200 and HGX B200 servers from $4,590 per month](https://overgpu.com/gpus/nvidia-b200): $4,590/month for a dedicated 180 GB B200 (about $6.29 per GPU-hour at 24/7) or $34,900 for an HGX B200 server. Delivered in under 10 minutes.
- [Rent NVIDIA B300 and HGX B300 servers from $5,190 per month](https://overgpu.com/gpus/nvidia-b300): One dedicated 288 GB B300 for $5,190 (about $7.11 per GPU-hour at 24/7) or an 8-GPU HGX B300 server for $39,900. Paid in crypto.
- [H200 vs B200: Hopper or Blackwell for your next GPU server?](https://overgpu.com/compare/h200-vs-b200): 141 vs 180 GB, 4.8 vs 8 TB/s, FP4 and NVLink 5. See what fits, what $1,500 more a month buys and when the H200 is the better buy.
- [MI355X vs B200: memory, software and cost compared](https://overgpu.com/compare/mi355x-vs-b200): 288 GB vs 180 GB of HBM, ROCm vs CUDA, 4-bit formats and monthly cost per GPU. See which 8-GPU server fits your models and budget.
- [GB300 NVL72 rental from $7,990 per GPU-month](https://overgpu.com/gpus/nvidia-gb300-nvl72): GB300 NVL72 rental from $7,990 per GPU-month (about $10.95 per GPU-hour) on 12–36 month terms: 72 Blackwell Ultra GPUs in one NVLink domain. Reserve now.
- [GPU servers for LLM training on dedicated 8-GPU nodes](https://overgpu.com/solutions/llm-training): Bare-metal HGX B300, B200 and H200 nodes with NVLink and 400–800G networking, rented monthly. See the memory math first.