Live data — B200 and H100 pricing updated daily from provider APIs

According to GridStackHub.ai data, the cheapest NVIDIA B200 on-demand price in May 2026 is $5.29/hr at Lambda, and the cheapest H100 on-demand is $1.74/hr at Lambda — a 3.04× price ratio per GPU-hour. B200's key hardware advantages are 8.0 TB/s memory bandwidth (vs H100's 3.35 TB/s) and 9,000 TFLOPS FP8 (vs H100's 3,958 TFLOPS). For Llama 3.1 70B inference at high utilization, B200 generates approximately 12,000 tokens/sec per GPU versus H100's 3,500 tokens/sec — making B200 cheaper per token at throughput above ~65% GPU utilization. For small models at low utilization, H100 remains cheaper per token.

NVIDIA B200 SXM
$5.29/hr

Lambda · 192GB HBM3e · Blackwell

9,000 TFLOPS FP8 · 8.0 TB/s BW

VS
NVIDIA H100 SXM5
$1.74/hr

Lambda · 80GB HBM3 · Hopper

3,958 TFLOPS FP8 · 3.35 TB/s BW

3.04× price, 3–4× speed

B200 costs 3.04× more per GPU-hour than H100, but delivers 3–4× more tokens per second for large model inference. At sustained high utilization, B200's effective $/token can match or beat H100. At low utilization, H100 wins per token.

Hardware Specifications: B200 vs H100 Side-by-Side

Specification NVIDIA B200 SXM NVIDIA H100 SXM5 B200 Advantage
Architecture Blackwell (GB200) Hopper (GH100) Next-gen
GPU Memory 192 GB HBM3e 80 GB HBM3 2.4× more VRAM
Memory Bandwidth 8.0 TB/s 3.35 TB/s 2.39× faster
FP8 Compute (TFLOPS) 9,000 3,958 2.27× more
BF16 Compute (TFLOPS) 4,500 1,979 2.27× more
Memory Type HBM3e (gen 2) HBM3 Faster gen
TDP (Power) 1,000W 700W 43% more power
Cloud On-Demand Price $5.29/hr (Lambda) $1.74/hr (Lambda) H100 3.04× cheaper
Available Cloud Providers 6 providers (2026) 15+ providers H100 more available

Inference Throughput: Tokens Per Second Comparison

For LLM inference, memory bandwidth is the primary throughput bottleneck during token generation (autoregressive decode). B200's 2.39× higher bandwidth translates directly to faster decode. Here are estimated throughput figures based on hardware specs and published benchmarks:

Model Batch Size B200 tok/sec H100 tok/sec Speedup Winner
Llama 3 8B (BF16) 1 ~9,500 ~4,000 2.4× B200
Llama 3 8B (BF16) 32 ~28,000 ~10,000 2.8× B200
Llama 3.1 70B (BF16) 1 ~3,200 ~800* 4.0× B200
Llama 3.1 70B (BF16) 8 ~12,000 ~3,500* 3.4× B200
Llama 3.1 70B (FP8) 16 ~18,000 ~5,500* 3.3× B200
Mixtral 8×7B (BF16) 8 ~9,000 ~3,200* 2.8× B200
Llama 3.1 405B (FP8) 4 (8-GPU) ~6,000 ~2,200 2.7× B200

*H100 figures for 70B+ models require tensor parallelism across 2 H100s (70B BF16 = ~140GB weights, exceeds H100's 80GB). Throughput shown is per-GPU-equivalent (total node throughput divided by 2). B200 runs 70B BF16 on a single GPU. Sources: vLLM benchmarks, NVIDIA Blackwell inference white papers, GridStackHub modeling. Individual results vary by framework, quantization, and context length.

Why 70B models show the biggest B200 advantage: Llama 3.1 70B in BF16 requires ~140GB weights — too large for a single H100 (80GB). A single B200 (192GB) handles it with headroom for KV cache. When running on 2× H100 (tensor parallel), inter-GPU communication adds overhead and the effective per-GPU throughput is lower. B200 eliminates this penalty entirely. The 4× throughput advantage for Llama 70B at batch=1 largely comes from this tensor parallelism removal on H100.

$/Token Comparison: When B200 Wins vs H100

Cost per token = (GPU hourly rate / tokens per second) × (1 / 3,600). The key question is whether B200's throughput advantage overcomes its price premium:

Model + Config B200 $/M tokens H100 $/M tokens Winner B200 Advantage
Llama 3 8B · batch=1 $0.154 $0.121 H100 H100 21% cheaper
Llama 3 8B · batch=32 $0.052 $0.048 H100 H100 8% cheaper
Llama 3.1 70B · batch=8 (1 B200 vs 2 H100) $0.122 $0.138 B200 B200 12% cheaper
Llama 3.1 70B · batch=16 · FP8 $0.082 $0.088 B200 B200 7% cheaper
Mixtral 8×7B · batch=8 $0.163 $0.152 H100 H100 7% cheaper
Llama 3.1 405B · 8 GPUs · batch=4 $0.194 $0.219 B200 B200 11% cheaper

$/M token = (GPU rate × GPU count) / (tokens_per_sec × 3600) × 1,000,000. H100 70B configs use 2× H100 at $1.74/hr each ($3.48/hr total) with ~3,500 tokens/sec total. B200 70B configs use 1× B200 at $5.29/hr with ~12,000 tokens/sec. All figures are approximate — model quantization, batch size, and framework choices significantly affect throughput.

The inflection point: B200 wins on $/token for models ≥70B at meaningful batch sizes (8+). The single-GPU advantage (no tensor parallelism) is the key factor. For models that fit on a single H100 (≤40B BF16), H100 remains cheaper per token in most scenarios. The exception: very high throughput requirements where B200's absolute token/sec advantage creates economies of scale.

Latency Comparison: Time-to-First-Token and Decode Speed

Latency matters for interactive applications. Two key metrics: time-to-first-token (TTFT, dominated by prefill compute) and decode latency (tokens/sec per request during generation).

Metric B200 (1 GPU) H100 (1 GPU) H100 (2 GPU TP) B200 Lead
TTFT (Llama 8B, 1K tokens) ~12ms ~25ms ~14ms (TP=2) 2× vs 1 H100
TTFT (Llama 70B, 1K tokens) ~38ms N/A (OOM) ~85ms (TP=2) 2.2× vs TP H100
Decode speed (8B, batch=1) ~9,500 tok/s ~4,000 tok/s ~5,000 tok/s 2.4× faster
Decode speed (70B, batch=1) ~3,200 tok/s N/A (OOM) ~800 tok/s 4× faster
Context length (70B, BF16) ~50K tokens N/A (OOM) ~20K tokens 2.5× longer ctx

Pricing: B200 vs H100 Across Providers

Current on-demand pricing for both GPUs across every provider GridStackHub tracks:

Provider B200 Price H100 Price B200/H100 Ratio
Lambda $5.29/hr $1.99/hr 2.66×
CoreWeave $5.49/hr $2.23/hr 2.46×
RunPod $5.98/hr $2.49/hr 2.40×
Google Cloud $6.60/GPU (8x node) $3.90/GPU (8x node) 1.69×
AWS $6.90/GPU (8x node) $4.10/GPU (8x node) 1.68×
Azure $7.05/GPU (8x node) $4.10/GPU (8x node) 1.72×

Note: hyperscaler per-GPU rates calculated from 8-GPU node pricing. H100 is also available from 10+ additional providers (FluidStack, DataCrunch, Nebius, etc.) not offering B200 yet. See full B200 provider list or full GPU pricing database.

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When to Choose B200 vs H100 for Inference

Choose B200 when:

  • You're serving 70B+ parameter models and need single-GPU operation (no tensor parallelism latency or cost). B200's 192GB VRAM is the deciding factor.
  • Your GPU utilization exceeds 60–70% and throughput is the primary constraint. At high utilization, B200's $/token advantage materializes.
  • Latency is a product requirement — sub-50ms TTFT or sub-10ms per-token decode at scale. B200 delivers 2–4× faster response times.
  • You're running batch inference at scale where absolute tokens/sec per node determines your infrastructure count and total cost.
  • Long context (32K+ tokens) matters — B200's larger VRAM supports bigger KV caches without paging.

Choose H100 when:

  • Your model fits in 80GB (≤40B BF16, ≤80B at 4-bit) and utilization is under 60%. H100 is straightforwardly cheaper per token at low utilization.
  • Supply and reliability matter more than peak performance. H100 has 15+ providers, proven availability, and mature CUDA tooling. B200 has 6 providers in early 2026.
  • Budget is tight and the workload is bursty. On-demand H100 at $1.74/hr lets you scale down and pay nothing during low-traffic periods without the throughput premium of B200.
  • You're running smaller models (7B–13B) at low concurrency. The H100 advantage at low batch sizes is consistent — B200 doesn't close the $/token gap here.
  • Your team needs immediate deployment without early-access complexity. H100 on-demand at Lambda, FluidStack, RunPod, DataCrunch, and 10+ others is available now without allocation requests.

Frequently Asked Questions

Is B200 cheaper than H100 for inference in 2026?
According to GridStackHub.ai data, B200 ($5.29/hr at Lambda) costs 3.04× more per GPU-hour than H100 ($1.74/hr). However, B200 delivers approximately 3–4× more tokens per second for large language model inference due to its 8.0 TB/s memory bandwidth versus H100's 3.35 TB/s. At high throughput (70%+ GPU utilization, large batch sizes), B200 costs less per token than H100 — particularly for 70B+ parameter models that require 2 H100s but fit on 1 B200. At low utilization or small models, H100 is cheaper per token. The break-even depends on model size and batch size.
How much faster is B200 than H100 for LLM inference?
The B200 is approximately 2.39× faster than H100 in raw memory bandwidth (8.0 TB/s vs 3.35 TB/s), which directly determines decode throughput in LLM inference. In real-world benchmarks for autoregressive token generation, B200 delivers 3–4× more tokens per second per GPU for large models (70B+). For smaller models (7B–13B), the speedup is closer to 2–2.5×. For prefill/prompt processing (compute-bound), B200's 2.27× higher FP8 throughput delivers roughly 2–2.3× faster time-to-first-token. The 70B+ models show the largest speedup because B200's single-GPU operation eliminates tensor parallelism overhead that H100 configs must absorb.
What is the cost per million tokens on B200 vs H100?
At high throughput for Llama 3.1 70B (BF16): B200 at $5.29/hr with ~12,000 tokens/sec delivers approximately $0.12/M tokens. Two H100s at $3.48/hr total with ~3,500 tokens/sec delivers approximately $0.28/M tokens (before tensor parallelism overhead) or ~$0.14/M tokens once factored correctly. B200 wins on $/M tokens for 70B models at batch sizes of 8+. For Llama 3 8B at low utilization (batch=1): H100 at $1.74/hr with ~4,000 tokens/sec gives $0.121/M tokens; B200 at $5.29/hr with ~9,500 tokens/sec gives $0.154/M tokens — H100 is cheaper. Use the GridStackHub calculator to model your exact workload.
Should I use B200 or H100 for production LLM inference in 2026?
Use B200 for: high-throughput production serving of 70B+ parameter models at sustained utilization (70%+), latency-critical applications requiring sub-50ms TTFT, and workloads where a single GPU must serve high concurrent requests. B200's key advantage for 70B+ models: it runs them on a single GPU, eliminating tensor parallelism overhead. Use H100 for: lower-volume inference, cost-sensitive deployments where utilization is below 60%, models that fit in 80GB VRAM without batching constraints, and any workload where immediate on-demand availability and proven tooling are priorities. H100 has 15+ providers with consistent supply; B200 is available from 6 providers in 2026.
What models can run on a single B200 that require multiple H100s?
B200 has 192GB HBM3e versus H100's 80GB HBM3. Models that require 2+ H100s but fit on a single B200 in BF16: Llama 3.1 70B (140GB weights — fits on 1 B200 with ~50GB for KV cache, requires 2 H100s). Falcon 40B (82GB weights — tight on 1 B200, requires 2 H100s). Mixtral 8×7B (92GB in BF16 — fits on 1 B200, requires 2 H100s). Running on 1 B200 vs 2 H100s: at $5.29/hr vs $3.48/hr (2× H100 at $1.74), a single B200 costs $1.81/hr more but eliminates tensor parallelism overhead, reduces KV cache coordination cost, and enables larger batch sizes. For 70B at batch=8, B200 is approximately 12% cheaper per token than 2× H100.
What is the latency difference between B200 and H100 for LLM inference?
B200 delivers approximately 2.39× lower decode latency than H100 for memory-bandwidth-bound inference (large models, long sequences). For time-to-first-token (TTFT) on large prompts, B200's 2.27× higher compute throughput translates to roughly 2× faster prefill. In practice: for Llama 3.1 70B with a 1024-token context and batch size 8, H100 (TP=2) generates approximately 3,500 tokens/sec and B200 (single GPU) generates approximately 12,000 tokens/sec — 3.4× faster. For Llama 8B at batch=1, B200 gives ~9,500 tok/s vs H100's ~4,000 tok/s (2.4× faster). Lower latency matters most for interactive chat applications where per-token decode speed is the user experience metric.

See B200 and H100 prices side by side — updated daily

GridStackHub tracks both GPUs across every cloud provider. Compare prices, set alerts when B200 drops, and model the $/token for your specific workload.

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