LLM Roofline
32 Verified Models 28 Local & Cloud GPUs MoE · MLA · UMA Tuned Limits

Real-Physics LLM VRAM, Token/s & Buy-vs-Cloud Sizing

Stop estimating speed by dividing file size by memory bandwidth. Model exact MoE active expert bandwidth, MLA / GQA / sliding-window KV cache growth, compute-bound prefill TTFT, OS unified-memory limits, and 36-month hardware vs cloud TCO directly in your browser.

Client-Side Deterministic Engine

Interactive VRAM, Bandwidth Roofline & TCO Simulator

30.5B (3.3B active) · GQA
400 GB/s · 28.4 TFLOPS
3. Weight Quantization 4.85 bpw
4. KV Cache
32,768 tok
Compute Roofline
COMFORTABLE FIT Memory-Bandwidth Bound
Max Safe Ctx: 40k
Memory Allocation Footprint 21.40 / 49.92 GiB usable
Weights: 17.57 GiB KV Cache: 3.00 GiB Overhead: 0.83 GiB Headroom: 28.52 GiB
Decode Speed
50.8
tok/s (batch=1)
Prefill Speed
2006
prompt tok/s
TTFT Latency
2.08s
for 4,096 tok
Active Params
3.3B
of 30.5B total
llama.cpp Server Command
llama-server -hf Qwen/Qwen3-30B-A3B:Q4_K_M -ngl 99 -fa on -c 32768 --port 8080
Context KV Cache Total VRAM Decode Status
4k 0.38 GiB 18.63 GiB 109.5 tok/s COMFORTABLE
8k 0.75 GiB 19.15 GiB 94.0 tok/s COMFORTABLE
16k 1.50 GiB 19.90 GiB 73.3 tok/s COMFORTABLE
32k 3.00 GiB 21.40 GiB 50.8 tok/s COMFORTABLE

Buy-vs-Cloud-vs-API Monthly TCO Break-Even

USE SERVERLESS API
Own Hardware/mo
$66.40
88.9h active + power
Cloud GPU/mo
$90.97
@$0.89/hr spot/pod
Serverless API/mo
$13.50
Payback: 221.3 mo

Popular Model Sizing Hubs

Verified against Hugging Face config.json and live OpenRouter pricing.

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Hardware Roofline Profiles

Apple Silicon UMA, Strix Halo Mini-PCs, Desktop GPUs, and SXM Cloud GPUs.

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