✅ PASST IN DEN VRAM (FP8 (W8A8 / E4M3))
Cohere Command-A 111B on NVIDIA H200 141GB SXM (141GB VRAM)
Unquantized FP16 (209.71 GB) exceeds 141 GB, but 4-bit Q4_K_M requires 66.19 GB (47% VRAM utilization) at ~49.2 tokens/sec.
🎛️ Interaktiver VRAM-, Kontext- & Offload-Konfigurator
📊 Echtzeit-Speicher- & Performance-Telemetrie
66.19 GB / 141 GB
🟦 Modellgewichte: 63.68 GB
🟪 KV-Cache: 2 GB
🟨 Engine-Puffer: 0.51 GB
Benötigter GPU-VRAM66.19 GB
Benötigter System-RAM (CPU)0.00 GB
Verfügbarer Hardware-VRAM141 GB (1x NVIDIA H200 141GB SXM)
VRAM-Reserve / Defizit74.81 GB
Geschätzte Decode-Geschwindigkeit~49.2 tokens/sec
Geschätzte Prefill-Geschwindigkeit~2317 tokens/sec
Time-to-First-Token (4K Prompt)~1768 ms
Transformer-TopologieDense-GQA (64L, 8/96 KV/Q heads)
🚀 Startbefehl (vLLM / llama.cpp / Ollama)
ollama run command-a:111b # Or llama.cpp server (8K context, Flash Attention): llama-server -hf CohereForAI/c4ai-command-a-03-2025-GGUF -c 8192 -ngl 99 -fa
CLOUD-GPU PREISVERGLEICH & MIETEN-VS-KAUFEN RECHNER
Nicht genug lokaler VRAM? Cloud-GPU in 60 Sekunden starten (Cohere Command-A 111B: 66.19 GB in 4-bit)
Recommended Cloud Tier: NVIDIA A100 / H100 (80GB) · Full FP16 Needs: 209.71 GB
Amortisationszeit (Kauf vs. Miete): ~32 months (vs $36,000 hardware)
| Cloud Provider | RTX 4090 (24GB) | A100 (80GB) | H100 (80GB) | Est. Monthly Cost (4h/day) | Free Credit / Trial | Deploy |
|---|---|---|---|---|---|---|
| RunPod | $0.44 / hr | $1.39 / hr | $2.39 / hr | $52.80 / mo | Instant PyTorch / vLLM Templates + Serverless GPU | Auf RunPod starten → |
| Vast.ai | $0.29 / hr | $0.95 / hr | $1.79 / hr | $34.80 / mo | Save 50–70% on RTX 4090 / 5090 / A100 rentals | Spot-GPU mieten → |
| Vultr Cloud GPU | — | $1.79 / hr | $2.69 / hr | $214.80 / mo (A100) | $300 Free Credit | $300 Guthaben sichern → |
| DigitalOcean GPU | — | — | $2.99 / hr | $358.80 / mo (H100) | $200 Free Credit (60d) | $200 Startguthaben → |
Alle Quantisierungsstufen im Detail: Cohere Command-A 111B × NVIDIA H200 141GB SXM
| Quantization Format | Core / Emb BPW | Weights (GiB) | KV Cache (8K) | Total VRAM | 1x NVIDIA H200 141GB SXM (141GB) | Decode / Prefill | Min GPUs |
|---|---|---|---|---|---|---|---|
| FP16 / BF16 (Unquantized) | 16 / 16 | 206.75 GB | 2 GB | 209.26 GB | Nein (+68.3GB) | ~1.2 / 2317 tok/s | 2x |
| FP8 (W8A8 / E4M3) | 8.25 / 8.5 | 106.79 GB | 2 GB | 109.3 GB | Ja (78%) | ~28.8 / 2317 tok/s | 1x |
| GGUF Q8_0 (8.50 bpw) | 8.5 / 8.5 | 109.84 GB | 2 GB | 112.34 GB | Ja (80%) | ~28 / 2317 tok/s | 1x |
| GGUF Q6_K (6.56 bpw) | 6.56 / 6.56 | 84.77 GB | 2 GB | 87.28 GB | Ja (62%) | ~36.2 / 2317 tok/s | 1x |
| GGUF Q5_K_M (5.69 bpw) | 5.69 / 6.56 | 74.16 GB | 2 GB | 76.67 GB | Ja (54%) | ~41.8 / 2317 tok/s | 1x |
| GGUF Q4_K_M / AWQ (4.83 bpw) | 4.83 / 6.56 | 63.68 GB | 2 GB | 66.19 GB | Ja (47%) | ~49.2 / 2317 tok/s | 1x |
| GGUF IQ4_XS (4.25 bpw) | 4.25 / 5.5 | 55.83 GB | 2 GB | 58.34 GB | Ja (41%) | ~55.9 / 2317 tok/s | 1x |
| GGUF Q3_K_M (3.91 bpw) | 3.91 / 5 | 51.32 GB | 2 GB | 53.83 GB | Ja (38%) | ~60.8 / 2317 tok/s | 1x |
Dieses Modell auf anderen GPUs testen
Cohere Command-A 111B on NVIDIA RTX 5090 (32GB)Cohere Command-A 111B on NVIDIA RTX 4090 (24GB)Cohere Command-A 111B on NVIDIA RTX 5080 (16GB)Cohere Command-A 111B on NVIDIA RTX 4080 Super (16GB)Cohere Command-A 111B on NVIDIA RTX 4070 Ti Super (16GB)Cohere Command-A 111B on NVIDIA RTX 3090 (24GB)Cohere Command-A 111B on NVIDIA RTX 4060 Ti 16GB (16GB)Cohere Command-A 111B on NVIDIA RTX 3060 12GB (12GB)Cohere Command-A 111B on NVIDIA RTX 6000 Ada (48GB)Cohere Command-A 111B on NVIDIA RTX A6000 (48GB)
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