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NVIDIA Blackwell B200 192GB HBM3e (8,000 GB/s) vs H100 80GB SXM FP8 70B LLM Speed Matrix

Data-center GPU inference benchmark comparing NVIDIA Blackwell B200 192GB HBM3e (8.0 TB/s memory bandwidth) against H100 SXM 80GB HBM3 (3.35 TB/s) and H200 141GB (4.8 TB/s) on 70B FP8 workloads.

Maintained by: OmniToolCalc Systems & Creator Engineering Desk Last Reviewed: 2026-10-20 Documentation: Formula & Source Policy
Direct Answer • Verified Quantitative Summary Data-center GPU inference benchmark comparing NVIDIA Blackwell B200 192GB HBM3e (8.0 TB/s memory bandwidth) against H100 SXM 80GB HBM3 (3.35 TB/s) and H200 141GB (4.8 TB/s) on 70B FP8 workloads. Key verified metrics — Single-Stream 70B FP8 Decode Speed: 83.1 tok/s on B200 (vs 34.8 on H100); VRAM Capacity & Headroom (192 GB): 78.4 / 192 GB (113.6 GB KV Cache Free); 32K Context Prefill TTFT: 2.48s TTFT (2,250 TFLOPS Compute).
Presets:
Generation Speed (Tokens / Sec)
83.7 tok/s (12.0 ms/tok)
Total VRAM Required vs Available
87.4 / 192 GB (46% VRAM)
Agentic Prefill TTFT (32K Context)
2.49s TTFT (13,179 tok/s)
Live Sensitivity Scale (Model Parameter Size (Billions — e.g. 8B, 14B, 32B, 70B)) Output Comparison
0.75× Conservative
110.5 tok/s (9.0 ms/tok)
1.00× Active
83.7 tok/s (12.0 ms/tok)
1.50× Aggressive
55.8 tok/s (17.9 ms/tok)
100% GPU VRAM Resident (78.4 GB weights + 9.0 GB KV cache at 32K context). Interactive live token stream below is calibrated to 83.7 tok/s.
1. VRAM Allocation & Hybrid PCIe Spillover Telemetry 100% VRAM FIT: 104.6 GB Headroom
Weights: 78.4 GB (70B @ 8-bit) KV Cache (32k ctx): 9.0 GB Total Required: 87.4 GB / 192 GB Pool (1×192GB)
LIVE TOKEN/SEC STREAMING SIMULATOR
83.7 tok/s • TTFT: 2.49s (32k prompt)
[PREFILL OK: 2.49s for 32k tokens] Streaming 70B (8-bit) at 83.7 tok/s on 1x 192GB GPU: def evaluate_roofline_latency(params_b=70, bits=8, vram_gb=192): weights_gb = params_b * (bits / 8.0) * 1.12 kv_cache_gb = (32 * 1024 * params_b * 0.004) / 1024.0 return {"tps_gen": 83.7, "ttft_sec": 2.49, "spill_gb": 0.0} █
✓ Optimal 100% VRAM Resident Execution (83.7 tok/s • 2.49s TTFT) Your 1× 192GB pool holds all 87.4 GB (weights + 32k KV cache) with zero PCIe spillover, sustaining 6560 GB/s effective bandwidth. For burst multi-agent workloads or 128k+ context windows that exceed 192 GB, benchmark your local electricity/amortization cost against managed API endpoints using the LLM Token API Cost Calculator.

How Does NVIDIA Blackwell B200 192GB HBM3e (8,000 GB/s) v Work? (Worked Breakdown)

Data-center GPU inference benchmark comparing NVIDIA Blackwell B200 192GB HBM3e (8.0 TB/s memory bandwidth) against H100 SXM 80GB HBM3 (3.35 TB/s) and H200 141GB (4.8 TB/s) on 70B FP8 workloads.

Use the interactive Local LLM Hardware Speed, Multi-GPU VRAM & Agentic Prefill Simulator above to model custom production workloads and export CSV benchmark reports.

Need to run custom scenarios from scratch? Open the master Local LLM Hardware Speed, Multi-GPU VRAM & Agentic Prefill Simulator →

Complete Local LLM Speed & VRAM Simulator Reference Matrix (2026 Quick-Lookup Table)

Reference lookup matrix for Local LLM Hardware Speed, Multi-GPU VRAM & Agentic Prefill Simulator. Click any row to load that exact preset configuration:

Configuration / Tier Single-Stream 70B FP8 Decode Speed VRAM Capacity & Headroom (192 GB) 32K Context Prefill TTFT
NVIDIA Blackwell B200 192GB HBM3e (8,000 GB/s) vs H100 (Active) 83.1 tok/s on B200 (vs 34.8 on H100) 78.4 / 192 GB (113.6 GB KV Cache Free) 2.48s TTFT (2,250 TFLOPS Compute)
NVIDIA H100 SXM 80GB HBM3 (3,350 GB/s • vLLM FP8) 70B 34.8 tok/s (70B FP8 Single-Stream) 78.4 / 80 GB (98% HBM3) 5.65s TTFT (32K Context)
Mac Studio M4 Max (546 GB/s) vs M3 Ultra (819 GB/s) Ap 14.4 tok/s (M3 Ultra 70B Q4) 57.8 / 192 GB (30% Unified) 28.79s TTFT (64K Context)
Dual 2x RTX 3090 (48GB VRAM Pool) Llama-3.3 70B & Deep 25.9 tok/s (2x 3090 70B Q4) 46.6 / 48 GB (97% VRAM) 13.19s TTFT (8K Context)
NVIDIA RTX 5090 (32GB GDDR7 • 1,792 GB/s) vs RTX 4090 65.8 tok/s (RTX 5090 32B Q4) 23.2 / 32 GB (73% VRAM) 6.66s TTFT (32K Context)
2026 Local LLM GPU & Apple Silicon VRAM, Memory Bandwi 36.0 tok/s (RTX 4090 32B Q4) 21.7 / 24 GB (90% VRAM) 8.83s TTFT (1,856 tok/s)

How Does Scaling Model Parameter Size (Billions — e.g. 8B, 14B, 32B, 70B) Change Local LLM Speed & VRAM Simulator? (Benchmark Table)

Compare how scaling Model Parameter Size (Billions — e.g. 8B, 14B, 32B, 70B) affects your output (click any row to load those values into the calculator above):

Model Parameter Size (Billions — e.g. 8B, 14B, 32B, 70B) Generation Speed (Tokens / Sec) Total VRAM Required vs Available Agentic Prefill TTFT (32K Context)
35 167.3 tok/s (6.0 ms/tok) 43.7 / 192 GB (23% VRAM) 1.24s TTFT (26,357 tok/s)
53 110.5 tok/s (9.0 ms/tok) 66.1 / 192 GB (34% VRAM) 1.88s TTFT (17,406 tok/s)
70 (Baseline) 83.7 tok/s (12.0 ms/tok) 87.4 / 192 GB (46% VRAM) 2.49s TTFT (13,179 tok/s)
88 66.6 tok/s (15.0 ms/tok) 109.8 / 192 GB (57% VRAM) 3.13s TTFT (10,483 tok/s)
105 55.8 tok/s (17.9 ms/tok) 131.0 / 192 GB (68% VRAM) 3.73s TTFT (8,786 tok/s)
140 41.8 tok/s (23.9 ms/tok) 174.7 / 192 GB (91% VRAM) 4.97s TTFT (6,589 tok/s)

How Do You Calculate Local LLM Speed & VRAM Simulator? (Formula & Step-by-Step Guide)

Primary mathematical equation used by this model:

VRAM_weights = Params(B) × (Bits/8) × 1.12; VRAM_KV = ContextK × Params(B) × 0.004 × (Bits≥8 ? 1.0 : 0.75); TPS_gen = 1 ÷ [ (VRAM_weights × f_gpu)/(BW_gpu × η) + (VRAM_weights × f_cpu)/41.6 ]; TTFT(s) = (2 × Params(B) × ContextK × 1.024) ÷ Effective_TFLOPS
  1. Select a hardware + model preset (such as 1× RTX 4090 running Qwen-2.5-Coder-32B Q4_K_M, 2× RTX 3090 48GB running Llama-3.3-70B, RTX 5090 32GB, or Mac Studio M4 Max / M3 Ultra with Apple MLX) or customize the 8 parameters.
  2. Inspect the Live Token-Per-Second Terminal Simulator below to watch real-time text generation at your exact calculated tok/s rate and experience the Agentic Context Prefill (TTFT) delay.
  3. Review the VRAM Allocation & Spillover Bar and Hardware FinOps Advisory to see if your model fits 100% in VRAM or requires Multi-GPU / Cloud GPU scaling.
Primary Standards & References: Roofline Memory-Bandwidth Bound Inference Model (Williams et al., UC Berkeley) & llama.cpp / vLLM / Apple MLX Empirical Utilization Constants
Roofline token/sec and TTFT projections model dense transformer weights and GQA KV-cache memory footprints. MoE (Mixture-of-Experts) architectures require total parameter VRAM for storage but compute generation speed from active parameters per token.

Frequently Asked Questions — Local LLM Speed & VRAM Simulator

What is the benchmark calculation for Local LLM Speed & VRAM Benchmark: 32B & 70B on RTX 4090, RTX 5090 & Mac Studio?
Running a 32B parameter coding model at Q4_K_M (4.5 bits/weight) with a 32K agentic context window on 1× RTX 4090 (24 GB VRAM, 1,008 GB/s bandwidth, 165 FP16 TFLOPS) requires 23.2 GB total VRAM (20.2 GB weights + 3.1 GB KV cache), generating 36.0 tok/s (27.8 ms/tok) with a 17.65s Time-To-First-Token (TTFT) prefill.
Why does local LLM generation speed drop so drastically when VRAM overflows into System RAM?
Autoregressive token generation is strictly memory-bandwidth bound: every single generated token requires reading 100% of the active model weights from memory. While a discrete GPU delivers 936–1,792 GB/s of GDDR6X/GDDR7 bandwidth, offloading spilled layers to dual-channel DDR5 System RAM over PCIe drops effective read throughput on those layers to ~41.6 GB/s—causing a 5x to 15x slowdown.
How is the Local LLM Speed & VRAM Simulator equation verified against published standards?
This calculator executes the deterministic formula (VRAM_weights = Params(B) × (Bits/8) × 1.12; VRAM_KV = ContextK × Params(B) × 0.004 × (Bits≥8 ? 1.0 : 0.75); TPS_gen = 1 ÷ [ (VRAM_weights × f_gpu)/(BW_gpu × η) + (VRAM_weights × f_cpu)/41.6 ]; TTFT(s) = (2 × Params(B) × ContextK × 1.024) ÷ Effective_TFLOPS), documented against Roofline Memory-Bandwidth Bound Inference Model (Williams et al., UC Berkeley) & llama.cpp / vLLM / Apple MLX Empirical Utilization Constants and verified by the OmniToolCalc Systems & Creator Engineering Desk. Use the 6-tier Reference Benchmark Table above or export to CSV to compare how parameter changes scale across scenarios.

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Reference Matrix • 2026 Edition
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