What is the cheapest cloud provider to rent NVIDIA NVIDIA B200 180GB SXM6?

Based on our live rate telemetry, the lowest verified rate for NVIDIA B200 180GB SXM6 starts at $4.09/hr (spot) available on Verda.

Can I run ComfyUI and vLLM on NVIDIA B200 180GB SXM6?

Yes. With 180GB of HBM3e memory, the NVIDIA B200 180GB SXM6 is capable of running modern AI workloads. It easily hosts 70B parameter models in 16-bit or 4-bit precision with high concurrency.

Blackwell Architecture

NVIDIA B200 180GB SXM6 Cloud Rental Pricing

NVIDIA Blackwell B200 GPU featuring 180GB HBM3e memory, 8 TB/s memory bandwidth, and 5th-generation NVLink for trillion-parameter AI training.

Lowest On-Demand

$8.19/hr

Cheapest Spot Rate

$4.09/hr

VRAM Capacity

180 GB

Memory Type

HBM3e

Memory Bandwidth

8.0 TB/s

FP16 Compute

4500 TFLOPS

Power / TDP

1000W

Interconnect

NVLink 5 (1800 GB/s)

Live Cloud Instance Rates

Found 1 cloud instances for NVIDIA B200 180GB SXM6

Cloud Provider	Host Tier	GPU Config	Interconnect	vCPU & RAM	Network & Disk	Spot Price	On-Demand	Action
V Verda	Datacenter	1x (180GB)	NVLink	30 vCPUs / 170GB RAM	3.2 GB/s (~25 Gbps) Persistent Volume	$4.09/hr	$8.19/hr	Deploy

Get Restock & Flash Price-Drop Alerts for NVIDIA B200 180GB SXM6

We monitor NVIDIA B200 180GB SXM6 capacity across RunPod, Lambda, and Vast.ai 24/7. Get notified immediately when spot capacity opens below average rates.

Optimal Workloads & Workflows

High-density multi-GPU clusters, 70B+ LLM pre-training, Llama 3.3

Calculate exact model VRAM & context requirements in the AI Calculator

Architecture Deep-Dive & Hardware Analysis

NVIDIA B200 180GB SXM6: Architecture, VRAM Allocation & Cloud Rental Guide (2026)

NVIDIA B200 180GB SXM6 features 180GB of dedicated VRAM on NVIDIA's Blackwell architecture. NVIDIA Blackwell B200 GPU featuring 180GB HBM3e memory, 8 TB/s memory bandwidth, and 5th-generation NVLink for trillion-parameter AI training.

Engineering Decision Matrix: Is NVIDIA B200 180GB SXM6 Right For You?

Sweet Spot Workloads & Pipelines:

High-density multi-GPU clusters
70B+ LLM pre-training
Llama 3.3

Workloads To Avoid & Bottlenecks:

Workloads requiring more than 180GB contiguous physical VRAM on a single card
Distributed clusters requiring specialized ultra-high-bandwidth fabrics beyond the host interconnect

1. Silicon Architecture & Compute Specs: NVIDIA B200 180GB SXM6

NVIDIA B200 180GB SXM6 is engineered on NVIDIA's Blackwell platform with 180GB of dedicated high-speed memory. It delivers hardware-accelerated Tensor Cores for matrix multiplication, supporting standard modern precision datatypes including FP16 and INT8.

For AI developers, memory capacity and memory bandwidth are the governing constraints for inference latency and maximum context length. With 180GB of high-speed memory, this accelerator accommodates medium to large transformer architectures and high-resolution generative image pipelines.

Architecture Focus: When deploying NVIDIA B200 180GB SXM6, ensure your deep learning framework (PyTorch, vLLM, or TensorRT) is compiled against the corresponding CUDA Compute Capability for optimal kernel execution.

2. VRAM Budgeting & Sizing Breakdown

Effective VRAM budgeting is essential to prevent Out-Of-Memory (OOM) errors during peak batch inference. Model weights occupy approximately 2 bytes per parameter in FP16, 1 byte in FP8, and ~0.55 bytes in 4-bit quantization (AWQ/GPTQ).

On this 180GB accelerator, allocating sufficient headroom for the Key-Value (KV) cache buffer is vital. In continuous batching systems like vLLM and SGLang, reserving 15% to 25% of total VRAM for KV cache pages ensures stable throughput under concurrent user requests.

3. Cloud Rental Economics & Spot Price Optimization

Hourly rental rates for NVIDIA B200 180GB SXM6 vary between on-demand and spot pricing across different cloud providers. When selecting an instance, developers should balance raw hardware cost against network volume storage fees and host uptime guarantees.

For interactive prototyping and batch rendering, spot instances provide significant cost savings (often 40% to 60% below on-demand). For production API endpoints with strict latency requirements, dedicated on-demand instances in certified Tier 3 datacenters ensure continuous availability.

Framework Benchmarks & Execution Profiles

Deep Learning Model InferenceGOOD

Memory Footprint: 135 GB / 180 GB

Stable throughput across standard deep learning frameworks.

LoRA Parameter-Efficient Fine-TuningGOOD

Memory Footprint: 153 GB / 180 GB

Efficient gradient adaptation with gradient checkpointing enabled.

Technical FAQ: Renting & Deploying NVIDIA B200 180GB SXM6

What are the primary use cases for NVIDIA B200 180GB SXM6?

High-density multi-GPU clusters, 70B+ LLM pre-training, Llama 3.3

How does spot pricing work for NVIDIA B200 180GB SXM6?

Spot instances offer discounts up to 50% compared to on-demand rates, but may be preempted if demand spikes. We recommend using persistent storage volumes to checkpoint progress regularly.

GPU Hardware Watchdog & Audit

Hardware Architecture Traps & Sanity Benchmarks

Avoid fake multi-GPU speedups, PCIe bottlenecks, and slow disk thrashing before paying full price.

SXM5 vs PCIe Interconnect

An 8x H100 SXM5 cluster delivers 900 GB/s bidirectional NVLink. In contrast, 8x H100 PCIe cards run at only 64 GB/s. For multi-GPU LLM training and vLLM tensor parallelism, PCIe cards suffer 40% to 70% throughput collapse due to all-reduce bottlenecks.

Tier 3 Cloud vs P2P Miner Rigs

Community marketplace hosts (Vast.ai) can save up to 60%, but lack power redundancy. A host reboot during a 20-hour LoRA run loses your epoch if checkpoints aren’t saved to remote S3. Choose Tier-3 data centers for production training.

Disk IOPS & Model Weights

Loading a 70B Safetensors model (140GB) on a cheap HDD network share takes 15 minutes. On high-speed local NVMe (>2.5 GB/s), it loads in 45 seconds. Always verify IOPS before debugging slow vLLM startup times.

Run on Fresh SSH Instance (Instant Verification)Takes ~5 seconds to run

# --- GPUy Day-1 Cloud GPU Healthcheck & Benchmark ---
# 1. Check GPU Clocks, Temperature, Power, PCIe Bus & Driver
nvidia-smi --query-gpu=name,driver_version,temperature.gpu,utilization.gpu,power.draw,clocks.current.graphics,memory.total,pcie.link.gen.current,pcie.link.width.current --format=csv

# 2. Check Thermal Throttle Violations & Performance State
nvidia-smi -q -d PERFORMANCE,CLOCK | grep -E "Throttle Reason|Performance State" | head -n 8

# 3. Check NVLink Interconnect Status (Critical for Multi-GPU Training)
nvidia-smi nvlink -s 2>/dev/null || echo "No NVLink detected (PCIe bus fallback)"

# 4. Benchmark NVMe Disk Write Speed (Look for >1.0 GB/s)
dd if=/dev/zero of=/workspace/speed_test.tmp bs=1G count=2 oflag=direct 2>&1 | tail -n 1
rm -f /workspace/speed_test.tmp

# 5. Test Hugging Face CDN Download Speed
curl -s -w 'HuggingFace CDN Speed: %{speed_download} bytes/sec\n' -o /dev/null https://huggingface.co/bert-base-uncased/resolve/main/pytorch_model.bin

Idle Temp< 45°C (Load < 78°C)

PCIe Bus LinkGen4 x16 / Gen5 x16

NVMe Disk Speed> 1.2 GB/s Direct IO

Weights CDN> 50 MB/s (400 Mbps)