Cohere Command-A 111B on NVIDIA A100 80GB SXM (80GB VRAM)
Full FP16 (231.33 GB) exceeds 80 GB VRAM, but 4-bit GGUF Q4_K_M / AWQ requires 71.99 GB (69.31 GB weights + 2 GB KV cache at 8K context), delivering ~18.8 tokens/sec.
🎛️ Interactive VRAM & KV-Cache Tuner
📊 Real-Time Memory Footprint
71.99 GB / 80 GB🚀 Ready-to-Run Launch Command
ollama run command-a:111b # Or with llama.cpp server (8K context, Flash Attention): llama-server -hf CohereForAI/c4ai-command-a-03-2025-GGUF -c 8192 -ngl 99 -fa
Need 71.99 GB+ VRAM to run Cohere Command-A 111B?
Recommended cloud setup for 4-bit: NVIDIA A100 / H100 (80GB) (or 231.33 GB for full FP16). Spin up a pre-configured vLLM / Ollama instance in 60 seconds:
| Provider | Best For | RTX 4090 (24GB) | A100 (80GB) | H100 (80GB) | Bonus / Trial | Action |
|---|---|---|---|---|---|---|
| RunPod | 1-Click vLLM / Ollama Pods | $0.44 / hr | $1.39 / hr | $2.39 / hr | Instant PyTorch / vLLM Pods + Serverless GPU | Deploy on RunPod → |
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All Quantization Formats: Cohere Command-A 111B on NVIDIA A100 80GB SXM
| Quantization Format | Bits/Weight | Weights VRAM | KV Cache (8K) | Total VRAM | Fits on 1x NVIDIA A100 80GB SXM (80GB)? | Est. Speed | GPUs Needed |
|---|---|---|---|---|---|---|---|
| FP16 / BF16 (Unquantized) | 16 bpw | 228.66 GB | 2 GB | 231.33 GB | No (Over by 151.3GB) | ~1.2 tok/s | 3x NVIDIA A100 80GB SXM |
| FP8 (W8A8 / E4M3) | 8.5 bpw | 121.48 GB | 2 GB | 124.15 GB | No (Over by 44.2GB) | ~1.4 tok/s | 2x NVIDIA A100 80GB SXM |
| GGUF Q8_0 (8-bit) | 8.5 bpw | 121.48 GB | 2 GB | 124.15 GB | No (Over by 44.2GB) | ~1.4 tok/s | 2x NVIDIA A100 80GB SXM |
| GGUF Q6_K (6-bit) | 6.56 bpw | 93.75 GB | 2 GB | 96.42 GB | No (Over by 16.4GB) | ~1.8 tok/s | 2x NVIDIA A100 80GB SXM |
| GGUF Q5_K_M (5-bit) | 5.69 bpw | 81.32 GB | 2 GB | 83.99 GB | No (Over by 4.0GB) | ~2.1 tok/s | 2x NVIDIA A100 80GB SXM |
| GGUF Q4_K_M / AWQ (4-bit) | 4.85 bpw | 69.31 GB | 2 GB | 71.99 GB | Tight (90%) | ~18.8 tok/s | 1x NVIDIA A100 80GB SXM |
| GGUF IQ4_XS (4.25 bpw) | 4.25 bpw | 60.74 GB | 2 GB | 63.41 GB | Yes (79%) | ~21.4 tok/s | 1x NVIDIA A100 80GB SXM |
| GGUF Q3_K_M (3-bit) | 3.91 bpw | 55.88 GB | 2 GB | 58.55 GB | Yes (73%) | ~23.3 tok/s | 1x NVIDIA A100 80GB SXM |
Frequently Asked Questions (Cohere Command-A 111B + NVIDIA A100 80GB SXM)
Can NVIDIA A100 80GB SXM (80GB) run Cohere Command-A 111B locally?
Yes — NVIDIA A100 80GB SXM (80GB) can run Cohere Command-A 111B using GGUF Q4_K_M / AWQ (4-bit). Full FP16 (231.33 GB) exceeds 80 GB VRAM, but 4-bit GGUF Q4_K_M / AWQ requires 71.99 GB (69.31 GB weights + 2 GB KV cache at 8K context), delivering ~18.8 tokens/sec.
How is KV Cache calculated for Cohere Command-A 111B?
Cohere Command-A 111B uses Dense-GQA with 64 transformer layers, 96 attention heads, and 8 KV heads (head dimension 128). At 8,192 tokens in FP16, KV cache consumes 2 GB. Enabling 8-bit KV cache (-ctk q8_0 -ctv q8_0 in llama.cpp or --kv-cache-dtype fp8 in vLLM) cuts this in half to 1.00 GB.