B200 NVLink 5.0 Scaling: When Does 8x B200 Beat 16x H100?

Direct answer

NVIDIA B200 SXM with NVLink 5.0 at 1.8 TB/s per GPU delivers exactly 2× the per-GPU interconnect bandwidth of H100 SXM5's NVLink 4.0 (900 GB/s). Because an 8-GPU B200 node and a 16-GPU H100 deployment have identical aggregate NVLink bandwidth (14,400 GB/s) at nearly identical hourly cost ($31.92 vs $30.24 on Vast.ai), the interconnect itself is a wash at node level.

What separates them is VRAM and compute density:

Metric	16x H100 SXM5	8x B200 SXM
Aggregate NVLink BW	14,400 GB/s	14,400 GB/s
Total VRAM	1,280 GB	1,536 GB
Hourly cost (Vast.ai)	$30.24	$31.92
70B FP8 fit	TP=2 required (81 GB > 80 GB/GPU)	single GPU (192 GB)
Site-record 70B FP8 throughput	~120 tok/s per 2-GPU replica	~180 tok/s per GPU
Aggregate 70B FP8 throughput	8 replicas × 120 = 960 tok/s	8 GPUs × 180 = 1,440 tok/s
Rack power	~11.2 kW	~8.0 kW

B200 wins when your model exceeds 80 GB per GPU (70B FP8 at 32K context = 81 GB), when you need maximum throughput per rack slot, or under power caps. 16x H100 wins on raw cost-adjusted interconnect bandwidth (~5% cheaper per GB/s) and ecosystem supply.

Key numbers (verified data)

Metric	H100 SXM5 (80GB)	B200 SXM (192GB)
NVLink generation	4.0 @ 900 GB/s	5.0 @ 1,800 GB/s
Typical node config	8x H100 SXM5	8x B200 SXM
Aggregate inter-GPU BW (8x node)	7,200 GB/s	14,400 GB/s
FP8 compute	1,979 TFLOPS	2,250 TFLOPS
Observed price (Vast.ai, 2026-10-03)	$1.89/hr	$3.99/hr

Current data

GPU specifications (from the OpenGPU Radar GPU spec database, src/lib/gpu-specs.ts):

• NVIDIA H100 SXM5: 80GB HBM3, 3.35 TB/s memory bandwidth, NVLink 4.0 @ 900 GB/s, 1,979 FP8 TFLOPS, 700W TDP
• NVIDIA B200 SXM: 192GB HBM3e, 8.0 TB/s memory bandwidth, NVLink 5.0 @ 1.8 TB/s, 2,250 FP8 TFLOPS, 1000W TDP

Cloud pricing (observed from providers.json, verified 2026-10-03):

Provider	H100 SXM5	B200
Vast.ai	$1.89/hr	$3.99/hr
Spheron	$2.29/hr	$4.49/hr
RunPod	$3.49/hr	$5.99/hr

Calculation / methodology

Inter-GPU bandwidth scaling

H100 per node = 8 GPUs × 900 GB/s  = 7,200 GB/s
B200 per node = 8 GPUs × 1,800 GB/s = 14,400 GB/s
16x H100      = 2 nodes             = 14,400 GB/s

The 8x B200 node matches a 16x H100 deployment's aggregate fabric bandwidth while costing 5.6% more per hour ($31.92 vs $30.24).

Cost-adjusted bandwidth efficiency (like-for-like: 14,400 GB/s)

16x H100: 14,400 GB/s ÷ $30.24/hr = 476 GB/s per $/hr
 8x B200: 14,400 GB/s ÷ $31.92/hr = 451 GB/s per $/hr

Result: 16x H100 is ~5% more cost-efficient on raw interconnect bandwidth — but raw bandwidth is rarely the deciding metric for inference, because VRAM capacity determines how many GPUs a model needs before any traffic crosses NVLink at all.

Cost-adjusted inference throughput (the metric that matters)

Site-record throughput for 70B FP8 batch=1 (gpu-specs.ts): H100 ~120 tok/s, B200 ~180 tok/s.

16x H100: 8 replicas × 120 tok/s =   960 tok/s ÷ $30.24/hr = 31.7 tok/s per $/hr
 8x B200: 8 replicas × 180 tok/s = 1,440 tok/s ÷ $31.92/hr = 45.1 tok/s per $/hr

B200 delivers 50% more aggregate 70B inference throughput at +5.6% cost — about 42% better throughput per dollar. The reason is per-GPU VRAM: 70B FP8 needs 81 GB at 32K context (canonical engine), which exceeds one H100 (80 GB) and forces TP=2, while a single B200 (192 GB) serves it with room for KV cache and batch growth.

Practical configurations

16x H100 setup (two 8-GPU nodes)

• Total VRAM: 1,280 GB
• Hourly cost: $1.89 × 16 = $30.24/hr
• 70B FP8: TP=2 per replica — 8 replicas at the H100's ~120 tok/s record
• R1 INT4 (417 GB): fits in one 8× H100 node (640 GB); R1 FP8 (786 GB) does not fit 8× H100 — see DeepSeek R1 FP8 vs INT4 memory analysis
• NVLink hops: intra-node 1-hop; 70B TP=2 replicas must stay node-local

8x B200 setup (single 8-GPU node)

• Total VRAM: 1,536 GB
• Hourly cost: $3.99 × 8 = $31.92/hr
• 70B FP8: single GPU per replica — 8 replicas at ~180 tok/s each
• DeepSeek R1 671B FP8 (786 GB): comfortably fits (1,536 GB available)
• NVLink hops: 1-hop across the node at 1.8 TB/s

Verdict for 70B-class models

For Llama 3.3 70B in FP8 (81 GB at 32K context):

• 16x H100 ($30.24/hr): TP=2 per replica, 8 replicas, ~960 tok/s aggregate
• 8x B200 ($31.92/hr): 1 GPU per replica, 8 replicas, ~1,440 tok/s aggregate
• Same replica count, same fabric bandwidth, 5% cost difference — B200 buys 50% more throughput and single-GPU headroom

What changes the result?

• Model size > 80 GB: B200's 192 GB enables single-GPU inference where H100 needs 2 GPUs — the crossover point of this whole comparison
• INT4 instead of FP8: 70B INT4 (42.5 GB) fits a single H100, collapsing the TP advantage — H100 wins on cost for quantized serving
• Throughput-critical: H100 has broader supply and a deeper rental ecosystem (more providers in providers.json)
• Power-constrained: 8x B200 draws ~8.0 kW vs ~11.2 kW for 16x H100 — 29% less power for more throughput
• Spot pricing volatility: rates above are single-day observations (2026-10-03) — re-check before committing

Alternatives

• B300 Blackwell Ultra upgrade path: 288 GB HBM3e, 9 TB/s, ~200 tok/s 70B FP8 record — pre-production specs per NVIDIA's whitepaper (see B300 deep dive)
• H200 (141 GB): the pragmatic middle — 70B FP8 (81 GB) fits single-GPU at $2.79/hr (Vast.ai), with NVLink 4.0
• H100 PCIe variants: cheaper per unit but PCIe-class interconnect cannot sustain tensor-parallel all-reduce; not in the site's observed rate set

Conclusion

Scenario	Winner	Reason
70B FP8, throughput per dollar	B200 SXM	1,440 vs 960 tok/s aggregate at +5.6% cost (42% better tok/s per $)
Raw cost-adjusted NVLink bandwidth	H100 SXM5	476 vs 451 GB/s per $/hr (~5%)
INT4-quantized 70B	H100 SXM5	42.5 GB fits one GPU — no TP tax, far cheaper
700B+ models, 8-GPU node	B200 SXM	1,536 GB, single node for R1 FP8 (786 GB)
Power-constrained racks	B200 SXM	8.0 kW vs 11.2 kW for equivalent fabric

Calculate VRAM requirements for your specific model with the Inference Cost Calculator, or compare live B200 rates at /gpu/nvidia-b200-sxm.