Open-Weight AI Hardware Fit & VRAM Allocation Analyzer

Model & Inference Config 70B | Q4_K_M

Foundation Model Presets

Meta Model 8B Edge / Single GPU

Meta Model 70B Workstation Grade

MoE 8x22B (120B) Sparse Activation

Meta Flagship 405B Datacenter Multi-GPU

Active Parameters 70.0 B

Quantization Precision (Weights)

Context Window Length 8,192 tokens

KV-Cache Quantization Precision

Concurrent Batched Sequences 1 sequence

Inference Engine Overhead

Total Required Memory

43.8 GB

Weights: 40.6 GB | KV: 0.7 GB

Est. Generation Speed

22.8 t/s

Based on selected hardware tier

Target Hardware Fit

Needs Multi-GPU

Target: RTX 4090 (24GB)

VRAM Allocation Breakdown vs Hardware Capacity Weights / KV / Context / Runtime

Hardware Reality Analysis: Running a 70B model at Q4_K_M quantization requires ~43.8 GB total VRAM. A single consumer RTX 4090 (24GB) will suffer Out-Of-Memory (OOM) or catastrophic PCIe offload slowdowns (~1.8 tokens/sec). Dual 24GB GPUs (48GB total) or an Apple Silicon Mac Studio (64GB+) are required for real-time local execution.

Hardware Tier Viability Matrix Click any tier to test deployment

Quantization Memory & Quality Matrix (70B Baseline)

Format	Bits/Weight	Model VRAM	Perplexity Impact	Target Recommendation

Audit the entered weight and cache sizing assumptions

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Read the explanation

The executable weight formula uses assigned bytes per parameter, not simply displayed bits divided by eight. At seventy billion parameters, FP16 two bytes gives one hundred forty decimal gigabytes; the source INT4 assignment of zero point five eight gives forty point six. Bars use three pixels per assigned decimal gigabyte. These assignments include source approximations and do not establish quantization quality or actual model allocation. At seventy billion parameters this source selects eighty layers, eight KV heads and head dimension one hundred twenty-eight. Keys and values at two bytes, eight thousand one hundred ninety-two tokens and batch one produce two point five binary gibibytes. Doubling context produces five. Bars use eighty-five pixels per gibibyte. The executable denominator is two to the thirtieth, despite the interface GB labels. Architecture is inferred from parameter thresholds rather than loaded from a real checkpoint. The runtime branch assigns two point five gigabytes to vLLM, zero point eight to llama cpp, and four to Transformers. Bars show two point five and zero point eight at one hundred eighty pixels per assigned unit. The source adds these allowances to decimal weights, binary cache and an activation approximation, so its displayed total mixes conventions. Treat the sum as a written planning heuristic, not measured runtime memory, performance, prices or hardware recommendations.