NVIDIA B200 Specs and Price: 192GB Blackwell GPU for AI (2026)

The NVIDIA B200 is the next-generation datacenter GPU based on the Blackwell architecture. It features 192GB HBM3e memory (2.4x the H100's 80GB), up to 8 TB/s bandwidth, and second-generation Transformer Engine with native FP4 support. NVIDIA claims up to 4x faster LLM inference than H100. The B200 is a datacenter GPU — it's the successor to H100/H200 in cloud environments. For current cloud GPU workloads, H100 and H200 are available today.

B200 Key Specifications

Architecture: What Blackwell Changes

Second-Generation Transformer Engine

The biggest improvement for AI workloads is the 2nd-gen Transformer Engine with native FP4 support:

• FP4 precision — 4-bit floating point for inference. Halves memory usage vs FP8, enabling larger models or higher batch sizes on a single GPU
• Dynamic precision management — automatically switches between FP4, FP8, and FP16 based on what each layer needs
• Higher throughput — combined with architectural improvements, NVIDIA claims up to 4x inference performance vs H100

FP4 is particularly impactful for LLM inference. A model that needs 80GB in FP8 on H100 would need only ~40GB in FP4 on B200, leaving 150GB+ free for KV cache and batching.

192GB HBM3e Memory

The memory jump is massive:

192GB means:

• Llama 70B in FP16 fits on a single GPU (140GB) with 52GB to spare for KV cache
• Llama 70B in FP8 needs only ~70GB, leaving 122GB for massive batch sizes
• Llama 405B in FP4 could potentially fit on 2 B200s
• Multiple models served simultaneously from a single GPU

NVLink 5th Generation

NVLink bandwidth doubles from 900 GB/s (H100/H200) to 1.8 TB/s per GPU. For multi-GPU training, this means:

• Faster gradient synchronization during distributed training
• More efficient tensor parallelism for large model inference
• Better scaling efficiency when using 4-8 GPUs per node

GB200 and NVL72

NVIDIA is also shipping the B200 in pre-configured rack-scale systems:

• GB200 — a compute module with 2 B200 GPUs + 1 Grace CPU, connected via NVLink
• GB200 NVL72 — a full rack with 36 Grace CPUs and 72 B200 GPUs interconnected via NVLink, delivering 720 petaFLOPS of FP4 compute

These are designed for large-scale training and inference at the datacenter level.

B200 vs H100 vs H200

For LLM Inference

*NVIDIA published claims. Real-world performance will vary by implementation and workload.

The B200's combination of more memory, higher bandwidth, and FP4 support could make single-GPU serving of 70B models practical at scale — something that's tight on H100 and comfortable but not optimal on H200.

For Training

NVIDIA claims up to 4x training performance on GPT-class models compared to H100, primarily from:

• Higher Tensor Core throughput
• FP8 training improvements (2nd-gen Transformer Engine)
• 2x NVLink bandwidth for better multi-GPU scaling
• More memory reducing the need for memory optimization techniques

For large model training, the B200 could reduce training time (and cost) by 3-4x compared to H100, assuming the software stack fully utilizes the new hardware features.

NVIDIA B200 Price: What to Expect

The NVIDIA B200 GPU is estimated to cost $30,000-$40,000 per unit for purchase, based on industry reports. NVIDIA's manufacturing cost is reportedly around $6,400 per chip, with memory accounting for roughly half.

B200 Cloud Rental Pricing

Cloud B200 pricing hasn't been widely established yet. For reference, here's how previous generations priced at launch vs. current rates:

Expect B200 cloud pricing to start at a premium and decrease as supply increases — the same pattern seen with every GPU generation. Check JarvisLabs pricing for current GPU rates.

What B200 Means for GPU Pricing

Historically, new GPU generations cause price drops on previous generations:

• When H100 launched, A100 prices dropped significantly
• When H200 became available, H100 prices decreased
• B200 availability will likely push H100/H200 prices lower

For current workloads: H100 and H200 remain excellent choices. The mature software ecosystem, wide framework support, and decreasing prices make them strong value propositions. As B200 ramps up in cloud providers, expect H100/H200 to become even more cost-effective.

Check current H100 and H200 pricing on JarvisLabs.

When Will B200 Be Available in the Cloud?

NVIDIA began shipping B200 GPUs in late 2025, with major cloud providers gradually adding B200 instances through 2026. Availability timeline:

• Hyperscalers (AWS, GCP, Azure): Early availability, typically reserved for large customers first
• Specialized GPU clouds: Rolling out as hardware becomes available
• General availability: Expected to widen throughout 2026

For workloads that need GPU compute today, the H100 and H200 are available now and deliver strong performance. The B200's advantages are most impactful for very large models and high-throughput inference — workloads where the extra memory and FP4 support create qualitative capability differences.

Should You Wait for B200?

Don't wait if:

• Your workloads run well on H100/H200 today
• You need GPU compute now (training deadlines, production inference)
• Your models fit in 80GB (H100) or 141GB (H200)
• You're doing fine-tuning or smaller-scale training

Consider waiting if:

• You're planning a massive training run (100B+ parameters) where 4x speedup saves significant money
• You need to serve very large models (200B+) on minimal GPUs
• You're building infrastructure for 2027+ and want the latest generation
• Your workload would uniquely benefit from FP4 inference

In practice, waiting for next-gen hardware often delays projects more than it saves. H100 and H200 handle the vast majority of AI workloads effectively, and their prices will decrease as B200 supply increases.

B200 Power and Cooling

The B200's 1000W TDP is a 43% increase over H100/H200's 700W. This has infrastructure implications:

• Higher electricity costs per GPU
• Increased cooling requirements — datacenter cooling infrastructure must handle the additional thermal load
• Rack density changes — fewer GPUs per rack due to power and cooling constraints

For cloud users, this is handled by the provider. For on-premise deployments, the power and cooling infrastructure requirements are substantial.

FAQ

How much memory does the B200 have?

192GB HBM3e with up to 8 TB/s bandwidth. This is 2.4x the H100's 80GB and 1.36x the H200's 141GB.

Is the B200 faster than the H100?

NVIDIA claims up to 4x faster for LLM inference and training. Real-world gains will depend on model architecture, precision (FP4 vs FP8), and software optimization. Expect 2-4x improvements for transformer workloads.

What is FP4 and why does it matter?

FP4 is 4-bit floating point precision, native to Blackwell's Transformer Engine. It halves memory usage compared to FP8, allowing larger models or bigger batch sizes on the same hardware. Quality impact is minimal for inference thanks to dynamic precision management.

How much will B200 cloud instances cost?

Pricing hasn't been widely published yet. Historically, new GPU generations launch at a premium and prices stabilize as supply increases. Expect B200 to be priced above current H100/H200 rates initially. Check JarvisLabs pricing for the latest available GPUs and rates.

Can I run the same code on B200 as H100?

Yes. Blackwell is backward-compatible with CUDA code written for Hopper. The same frameworks (PyTorch, TensorFlow, vLLM, TensorRT-LLM) will work. Taking full advantage of FP4 and 2nd-gen Transformer Engine features may require framework updates.

What's the difference between B200, B100, and GB200?

B200 is the standalone GPU (192GB HBM3e). B100 is a lower-tier Blackwell variant. GB200 is a compute module combining 2 B200 GPUs with 1 Grace ARM CPU via NVLink. GB200 NVL72 is a full rack-scale system with 72 B200 GPUs.

Will B200 replace H100 in the cloud?

Gradually, yes. Just as H100 replaced A100 as the default high-end offering, B200 will become the new standard for demanding workloads. H100 will remain available and become more cost-effective — similar to how A100 is still widely used and offered today.