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Open Model Local Inference & VRAM Allocation Matrix

Grounded in Hugging Face Hub open weights: Qwen 27B, Gemma 2, and compact local inference

Model Architecture Archetypes Hub Verified
Model Name
Parameter Count (Billions) 27 B
Transformer Layers 64
Hidden Dimension 5120
Attention Heads (Query / KV) 40 / 8 (GQA 5:1)
Runtime & Quantization Settings Local Serving
Weight Precision / Quantization
Context Window (Tokens) 8,192 tokens
Concurrent Batch Size (Streams) 2
Target Hardware GPU
✅ Fits comfortably (26.7% VRAM headroom)
Safe for sustained decoding and KV cache expansion under continuous load.
17.6 GB
of 24 GB VRAM
VRAM Allocation Breakdown 73.3% Allocated
0 GB Target Ceiling: 24 GB
Model Weights: 13.5 GB
KV Cache: 2.1 GB
Activations: 1.2 GB
CUDA / Driver: 0.8 GB
Free Headroom: 6.4 GB
Model Weights
13.5 GB
4-bit INT4 AWQ
KV-Cache Footprint
2.1 GB
8 GQA heads × 8k
Activation Buffers
1.2 GB
Sequence intermediate
Driver & Context
0.8 GB
CUDA runtime overhead
Recommended Engine & Runner: vLLM with tensor_parallel_size=1 and gpu_memory_utilization=0.85
Cross-Hardware Compatibility Matrix Consumer & Cloud GPUs
Hugging Face & vLLM Execution Configuration Copy & Paste 

        

      

    

  


  
Trace the saved weight, cache and placement equations
Read the explanation
This saved configuration labels its model twenty-seven billion parameters. Half a byte per parameter gives thirteen point five decimal gigabytes of four-bit weights; two bytes gives fifty-four for FP16. Both bars use eight pixels per decimal gigabyte. These are written storage assumptions, not verified checkpoint specifications or numerical quality measurements. The three-bit option actually uses point four bytes per parameter in this source. The actual cache equation uses sixty-four layers, eight KV heads, one hundred twenty-eight head dimensions, one byte per value, batch two, and eight thousand one hundred ninety-two tokens. Its result is two binary gibibytes, despite the initial markup suggesting two point one. Doubling context gives four. Both bars use one hundred ten pixels per gibibyte. The executable calculation, rather than stale initial text, controls the updated result. The default rounded allocation adds thirteen point five weights, two cache, one point two activation, and point eight driver allowance. This gives seventeen point five, leaving six point five against twenty-four. Both bars use twenty pixels per assigned memory unit. Decimal weights and binary cache are combined by the page, and activation and driver allowances are heuristics. The comfortable label and selected command are source branches, not a measured deployment guarantee.



  


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