Running frontier 70B parameter models on workstations equipped with 24GB or 48GB VRAM requires aggressive 4-bit weight quantization without destroying downstream reasoning perplexity. We benchmark GGUF (llama.cpp), AWQ (Activation-aware Weight Quantization), and EXL2 (ExLlamaV2) across generation speed and memory footprints.
1. Inference Throughput Benchmark on Dual RTX 4090 (48GB VRAM)
| Quantization Format | Tokens per Second (t/s) | VRAM Allocation | Perplexity Loss (WikiText-2) |
|---|---|---|---|
| EXL2 (4.0 bpw) | 44.2 t/s (Fastest GPU Kernel) | 38.4 GB | + 0.12 (Negligible) |
| AWQ (4-bit GEMM) | 32.8 t/s | 39.2 GB | + 0.08 (Lowest Loss) |
| GGUF Q4_K_M (CPU+GPU Offload) | 21.5 t/s | 41.0 GB | + 0.15 |
2. Selecting the Ideal Runtime for Production Workloads
For pure GPU inference servers handling high concurrency, EXL2 and vLLM AWQ provide the lowest kernel latency. For local Apple Silicon workstations with unified memory, llama.cpp Metal GGUF remains the gold standard for zero-configuration deployment.
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