omnitool / Dev & Cloud / Local LLM Speed & VRAM Simulator / NVIDIA H100 SXM 80GB HBM3 (3,350 GB/s • vLLM FP8

NVIDIA H100 SXM 80GB HBM3 (3,350 GB/s • vLLM FP8) 70B Throughput Matrix

Name: NVIDIA H100 SXM 80GB HBM3 (3,350 GB/s • vLLM FP8) 70B Throughput Matrix (2026) — Reference Dataset
Creator: omnitool
License: https://creativecommons.org/licenses/by/4.0/

Cloud H100 SXM 80GB HBM3 (3,350 GB/s bandwidth, 989 FP16/FP8 TFLOPS, vLLM 0.82 efficiency) single-stream latency and prefill benchmark for 70B 8-bit models. (Generation Speed (Tokens / Sec): 34.8 tok/s (70B FP8 Single-Stream) | Total VRAM Required )

Maintained by: OmniToolCalc Systems & Creator Engineering Desk Documentation: Formula & Source Policy

Key Quantitative Takeaway Generation Speed (Tokens / Sec): 34.8 tok/s (70B FP8 Single-Stream) | Total VRAM Required vs Available: 78.4 / 80 GB (98% HBM3) | Agentic Prefill TTFT (16K Context): 5.65s TTFT (32K Context)

Presets:

Model Parameter Size (Billions — e.g. 8B, 14B, 32B, 70B)

Quantization Precision (Bits/Weight — Q4_K_M=4.5, Q8_0=8.5, FP16=16)

Multi-GPU Count (1x, 2x, 4x, 8x Cards / Unified Node)

VRAM per GPU / Unified Memory (GB — 16, 24, 32, 48, 80, 128, 192)

Memory Bandwidth per GPU (GB/s — 3090=936, 4090=1008, 5090=1792, M4 Max=546)

FP16 Compute per GPU (TFLOPS — 3090=71, 4090=165, 5090=209, M4 Max=54, H100=989)

Agentic Context Prompt Length (K Tokens — 4K, 16K, 32K, 64K, 128K)

Inference Engine Efficiency η (vLLM/ExLlamaV2=0.78, Apple MLX=0.75, llama.cpp=0.68)

Generation Speed (Tokens / Sec)

5.4 tok/s (184.9 ms/tok)

Total VRAM Required vs Available

87.4 GB Needed (7.4 GB Spillover to RAM)

Agentic Prefill TTFT (32K Context)

6.18s TTFT (5,305 tok/s)

Live Sensitivity Scale (Model Parameter Size (Billions — e.g. 8B, 14B, 32B, 70B)) Output Comparison

0.75× Conservative

46.3 tok/s (21.6 ms/tok)

1.00× Active

5.4 tok/s (184.9 ms/tok)

1.50× Aggressive

0.9 tok/s (1127.2 ms/tok)

⚠️ VRAM Overflow: 7.4 GB offloaded to DDR5 System RAM (~41.6 GB/s PCIe/RAM bottleneck). Upgrade to 2x 80GB GPUs (160GB) or rent a Cloud 80GB A100/H100 GPU to restore full memory-bandwidth speed.

1. VRAM Allocation & Hybrid PCIe Spillover Telemetry VRAM OVERFLOW: +7.4 GB Spilled to DDR5 RAM

Weights: 78.4 GB (70B @ 8-bit) KV Cache (32k ctx): 9.0 GB Total Required: 87.4 GB / 80 GB Pool (1×80GB)

LIVE TOKEN/SEC STREAMING SIMULATOR

5.4 tok/s • TTFT: 6.18s (32k prompt)

[PREFILL OK: 6.18s for 32k tokens] Streaming 70B (8-bit) at 5.4 tok/s on 1x 80GB GPU:
def evaluate_roofline_latency(params_b=70, bits=8, vram_gb=80):
    weights_gb = params_b * (bits / 8.0) * 1.12
    kv_cache_gb = (32 * 1024 * params_b * 0.004) / 1024.0
    return {"tps_gen": 5.4, "ttft_sec": 6.18, "spill_gb": 7.4} █

⚠ Hardware Bottleneck & Cloud GPU FinOps Advisory (7.4 GB VRAM Overflow) Spilling layers across PCIe into system DDR5 (~64 GB/s) throttles generation to 5.4 tok/s and stretches 32k agentic prefill to 6.18s. To keep 87.4 GB 100% resident in high-bandwidth memory, scale to 4× 24GB GPUs (multi-GPU tensor parallel) or provision an on-demand 80GB H100 / A100 Cloud GPU instance (~$1.89–$2.99/hr for 75–120 tok/s). Compare self-hosted GPU hourly burn against managed API token billing in the LLM Token API Cost Calculator and 2026 LLM API Pricing Matrix.

In-Depth Analysis — NVIDIA H100 SXM 80GB HBM3 (3,350 GB/s • vLLM FP8

Cloud H100 SXM 80GB HBM3 (3,350 GB/s bandwidth, 989 FP16/FP8 TFLOPS, vLLM 0.82 efficiency) single-stream latency and prefill benchmark for 70B 8-bit models.

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)

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

Configuration / Tier	Generation Speed (Tokens / Sec)	Total VRAM Required vs Available	Agentic Prefill TTFT (16K Context)
NVIDIA H100 SXM 80GB HBM3 (3,350 GB/s • vLLM FP8) 70B (Active)	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)

Scenario Comparison & Benchmark Table (Local LLM Speed & VRAM Simulator)

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	70.1 tok/s (14.3 ms/tok)	43.7 / 80 GB (55% VRAM)	2.83s TTFT (11,585 tok/s)
53	46.3 tok/s (21.6 ms/tok)	66.1 / 80 GB (83% VRAM)	4.28s TTFT (7,651 tok/s)
70 (Baseline)	5.4 tok/s (184.9 ms/tok)	87.4 GB Needed (7.4 GB Spillover to RAM)	6.18s TTFT (5,305 tok/s)
88	1.5 tok/s (669.5 ms/tok)	109.8 GB Needed (29.8 GB Spillover to RAM)	9.76s TTFT (3,358 tok/s)
105	0.9 tok/s (1127.2 ms/tok)	131.0 GB Needed (51.0 GB Spillover to RAM)	13.89s TTFT (2,359 tok/s)
140	0.5 tok/s (2069.5 ms/tok)	174.7 GB Needed (94.7 GB Spillover to RAM)	24.68s TTFT (1,328 tok/s)

Mathematical 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
          

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.
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.
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.

Related Engineering, FinOps & Creator Reference Matrices

Mac Studio M4 Max (546 GB/s) vs M3 Ult 14.4 tok/s (M3 → 2026 Local LLM GPU & Apple Silicon VRA 36.0 tok/s (RT → IPv4 CIDR Subnet Mask Cheat Sheet (/8 254 Usable Hos → Twitch Partner Plus 70/30 vs 50/50 Sub $3,850 / month → Password Entropy Bits & Brute-Force Cr 104.9 Bits (Qu → /17 CIDR Subnet Mask: 255.255.128.0 (3 32,766 Hosts → /20 CIDR Subnet Mask: 255.255.240.0 (4 4,094 Hosts → /23 CIDR Subnet Mask: 255.255.254.0 (5 510 Hosts →

Popular Scenarios & Quick-Lookup Models

Reference Matrix • 2026 Edition

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