NVIDIA shipped the B300 — officially "Blackwell Ultra" — in January 2026. It's the company's most powerful single GPU to date, with 288 GB of HBM3e memory, 8 TB/s bandwidth, and 14 petaFLOPS of dense FP4 compute per chip. The DGX B300 user guide went live on January 20th, and cloud providers started listing instances within weeks.
But here's the question nobody in NVIDIA's marketing materials answers: when does the B300 actually make financial sense over an H200 or B200? At early cloud pricing of $4.95-18.00/hr per GPU, you need to know exactly which workloads justify the premium.
This guide breaks it all down — full specs, how it compares to every GPU you're probably considering, current pricing across providers, infrastructure requirements, and a clear decision framework for when to use it.
B300 Specs: What Changed from B200
The B300 isn't a new architecture. It's a binned, optimized version of Blackwell — higher clocks, more memory, better thermal design. Think of it as Blackwell pushed to its limits.
Per-GPU Specifications
| Spec | B300 | B200 | H200 | H100 |
|---|---|---|---|---|
| Architecture | Blackwell Ultra | Blackwell | Hopper | Hopper |
| VRAM | 288 GB HBM3e | 192 GB HBM3e | 141 GB HBM3e | 80 GB HBM3 |
| Memory Bandwidth | 8 TB/s | 8 TB/s | 4.8 TB/s | 3.35 TB/s |
| FP4 Dense (TFLOPS) | 14,000 | 9,000 | N/A | N/A |
| FP8 Dense (TFLOPS) | 7,000 | 4,500 | 756 | 756 |
| FP16 (TFLOPS) | 3,500 | 2,250 | 756 | 756 |
| TDP | 1,400W | 1,000W | 700W | 700W |
| Interconnect | NVLink 5 (1.8 TB/s) | NVLink 5 (1.8 TB/s) | NVLink 4 (900 GB/s) | NVLink 4 (900 GB/s) |
| Networking | ConnectX-8 (1.6T) | ConnectX-7 (800G) | ConnectX-7 (800G) | ConnectX-7 (800G) |
What Stands Out
Memory: 288 GB per GPU. This is the B300's headline feature. A single B300 can hold a full 70B parameter model in FP16 without quantization — with 100+ GB to spare for KV cache and batch processing. The B200's 192 GB required quantization for 70B models at higher batch sizes. The H200's 141 GB forced sharding across multiple GPUs for anything above ~65B parameters.
In an 8-GPU DGX B300 system, you get 2.3 TB of total GPU memory. That's enough to run a 400B+ parameter model entirely in GPU memory without model parallelism tricks.
FP4 compute: 14 petaFLOPS. The B300 is the first GPU where FP4 inference is a first-class citizen. At 14 PFLOPS dense FP4, it delivers 55.6% more compute than the B200's 9 PFLOPS. This matters because modern inference engines (TensorRT-LLM, vLLM) increasingly support FP4 quantization with minimal quality loss — and FP4 doubles your effective throughput compared to FP8.
1,400W TDP. The B300 draws 40% more power than the B200. This isn't just a number — it has real infrastructure implications. More on that below.
ConnectX-8 networking. Upgraded from 800G to 1.6T optical modules. For multi-node training and distributed inference, this doubles the available inter-node bandwidth. If your workload is communication-bound (large-scale training with frequent gradient syncs), this alone can cut distributed training time.
DGX B300 vs HGX B300 vs GB300 NVL72
NVIDIA ships the B300 in three system configurations. Understanding the differences matters for pricing and deployment.
DGX B300 — The "AI factory" system. 8x B300 GPUs with Intel Xeon 6776P CPUs, 2.1 TB total GPU memory, 144 PFLOPS sparse FP4. This is the turnkey system NVIDIA sells directly or through partners. It requires installation by NVIDIA-certified personnel.
HGX B300 — The server-grade baseboard. 8x B300 GPUs that OEMs (like Supermicro, Dell, Lenovo) integrate into their own server chassis. Same GPU performance as DGX, but you choose the CPU, cooling, and chassis configuration. Generally more flexible and often cheaper than DGX.
GB300 NVL72 — The rack-scale monster. 72 B300 GPUs + 36 Grace CPUs in a single liquid-cooled rack. Designed for massive inference workloads that need tight inter-GPU communication. This is what hyperscalers buy for serving frontier reasoning models. Not something most teams need.
For cloud GPU access, you're typically renting time on DGX B300 or HGX B300 systems.
Current Cloud Pricing (February 2026)
The B300 is still in early availability, so pricing varies widely. Here's what we're seeing across providers:
| Provider | Price Per GPU/hr | Instance Type | Notes |
|---|---|---|---|
| Spheron AI (Spot) | $2.90/hr | B300 | Lowest B300 pricing available |
| Spheron AI (Dedicated) | $5.80/hr | B300 | Guaranteed availability |
| Other marketplace listings | $4.95+ | HGX B300 | Variable availability |
| Premium cloud providers | $12-18 | DGX B300 | Full managed stack |
Spheron AI's B300 spot pricing at $2.90/hr is remarkably competitive — it's cheaper than what most providers charge for H200 instances ($3.50-3.80/hr). The dedicated rate at $5.80/hr guarantees availability for production workloads while still undercutting premium providers by 50-70%.
For comparison, H200 SXM instances currently run $2.50-3.80/hr and H100 SXM instances are $1.49-2.99/hr across providers.
Will pricing drop? Almost certainly. Every GPU generation follows the same pattern: premium pricing at launch, then rapid decline as supply ramps. The H100 went from $8/hr in 2024 to under $3/hr in 2026. Expect B300 pricing to compress similarly over the next 6-12 months as more providers bring capacity online.
Infrastructure Requirements: Not a Drop-In Replacement
You can't just swap B300s into existing H100 or B200 infrastructure. The B300 has specific requirements that affect deployment planning.
Liquid Cooling Is Mandatory
At 1,400W per GPU (11.2 kW for an 8-GPU system, before CPUs and networking), air cooling isn't viable. The DGX B300 and HGX B300 require direct liquid cooling (DLC). Supermicro's DLC-2 technology for the HGX B300 captures up to 98% of heat through liquid cooling.
If you're running GPU workloads in a traditional air-cooled datacenter, B300 deployment requires cooling infrastructure upgrades. This is one reason cloud access is attractive — the provider handles the cooling.
Power Requirements
An 8-GPU DGX B300 draws approximately 14 kW at peak load. That's roughly 2x what an equivalent H100 DGX system draws. For on-premises deployment, verify that your rack power budget and PDU capacity can handle this before ordering.
Software Stack
The B300 uses the same CUDA toolchain as B200 — CUDA 12.x, cuDNN 9.x, TensorRT-LLM. If your code runs on B200, it runs on B300 without modification. The new FP4 capabilities require TensorRT-LLM 0.15+ or vLLM with FP4 quantization support.
When to Use B300 vs H200 vs B200 vs H100
This is the decision that actually matters. Here's a practical framework.
Use B300 When:
You're running inference on 70B+ parameter models at scale. The 288 GB VRAM eliminates multi-GPU sharding for models up to ~130B parameters. Fewer GPUs per model instance means lower cost per query, even at a higher per-GPU price. If you're currently running Llama 70B across 2x H200s, a single B300 may serve the same workload at lower total cost.
You need maximum inference throughput per GPU. The 14 PFLOPS FP4 compute is 55% more than B200 and roughly 18x more than H100. For high-traffic inference APIs where throughput-per-GPU determines how many GPUs you need, the B300 reduces fleet size.
You're serving reasoning models with large KV caches. Models like OpenAI's o-series and DeepSeek R1 generate massive KV caches during chain-of-thought reasoning. The 288 GB VRAM gives you headroom to maintain large context windows without evicting the KV cache — which directly impacts reasoning quality and latency.
You're doing multi-node training and communication is the bottleneck. The ConnectX-8 upgrade to 1.6T networking doubles inter-node bandwidth. If your profiling shows gradient sync time dominating your training loop, the B300's networking improvements help.
Stick With H200 When:
Your models fit in 141 GB. If you're running 7B-30B parameter models or serving 70B models with aggressive quantization (INT4/FP4), the H200 delivers strong performance at $2.50-3.80/hr. No reason to pay the B300 premium for memory you won't use.
Pricing matters more than peak throughput. At current pricing, the H200 offers better cost-per-token for most standard inference workloads. The B300's advantage only materializes when you specifically need the extra VRAM or FP4 compute.
Stick With H100 When:
You're training, not inferencing. For training workloads, the H100 at $1.49-2.99/hr remains the best cost-per-FLOP option. Training throughput scales roughly linearly with compute, and the H100 still has plenty. The B300's inference-optimized FP4 capabilities don't help during training.
You're running batch inference or offline processing. Latency doesn't matter for batch jobs. H100s at marketplace prices give you the best throughput per dollar for non-real-time workloads.
Budget is the constraint. Eight H100s at $2/hr = $16/hr total. Eight B300s at $5.80/hr dedicated = $46.40/hr, or $23.20/hr on spot. If you can solve the problem with H100s, the cost difference may not be justified — unless the B300's throughput advantage means you need fewer GPUs to handle the same workload.
The Cost-Per-Token Math
Let's get concrete. Based on published benchmarks, the B300 delivers approximately 11-15x more inference throughput per GPU than the H100 for large language models.
| GPU | Est. Throughput (Llama 70B) | Price/hr | Relative Cost-per-Token |
|---|---|---|---|
| H100 SXM | ~21,800 tok/s | $2.00 | 1.0x (baseline) |
| H200 SXM | ~31,700 tok/s | $3.50 | 0.83x (17% cheaper) |
| B300 Spot (FP8) | ~100,000+ tok/s | $2.90 | ~0.21x (79% cheaper) |
| B300 Spot (FP4) | ~150,000+ tok/s | $2.90 | ~0.14x (86% cheaper) |
| B300 Dedicated (FP8) | ~100,000+ tok/s | $5.80 | ~0.42x (58% cheaper) |
The throughput numbers for B300 are early estimates based on NVIDIA's published improvement ratios. Real-world numbers will depend on model, batch size, and optimization stack. But the directional math is striking: at Spheron AI's spot rate of $2.90/hr, the B300 actually costs *less per hour* than an H200 while delivering 3-5x the inference throughput. The cost-per-token advantage is massive.
This is the key insight: judge GPUs by cost-per-output, not cost-per-hour.
Getting Access to B300 GPUs
B300 availability is still limited in February 2026, but options are growing fast.
[Spheron AI](https://spheron.ai) already has B300 GPUs in inventory — available as both Spot and Dedicated instances. Spot pricing gives you access to B300 hardware at significantly reduced rates for interruptible workloads like batch inference and experimentation. Dedicated instances guarantee availability for production inference and long-running training jobs. You get baremetal or VM GPU servers through a single console, with access to B300 alongside H200, H100, and other GPUs across 5+ providers.
Other cloud providers. A growing number of GPU cloud providers are bringing B300 capacity online through Q1-Q2 2026. Expect wider availability and pricing compression as supply ramps.
Direct from NVIDIA. DGX B300 systems are available for purchase or colocation through NVIDIA's partner network. Lead times are currently 8-12 weeks. Requires liquid cooling infrastructure and NVIDIA-certified installation.
What to Expect Next
The B300 is the beginning of the Blackwell Ultra generation, not the end. NVIDIA has already announced the GB300 NVL72 rack-scale system for hyperscaler deployment, and the next-generation Rubin architecture is on the roadmap for 2027.
For most teams, the practical advice is: don't wait for perfect hardware. The B300 is available now, cloud pricing will compress over the next 6 months, and the inference economics are already compelling for the right workloads. If you're currently running inference on H100s at scale, the B300's cost-per-token advantage means switching will pay for itself quickly — even at today's early-adopter pricing.
The teams that move fastest on new hardware generations capture the cost advantage before pricing adjusts. The teams that wait end up paying the same rate as everyone else.
