Model & Inference Config
70B | Q4_K_M
Meta Model 8B
Edge / Single GPU
Meta Model 70B
Workstation Grade
MoE 8x22B (120B)
Sparse Activation
Meta Flagship 405B
Datacenter Multi-GPU
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 |
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