✅ FITS IN (W8A8 QUANTIZATION
Cohere Command-A 111B on Apple M4 Max (128GB Unified) (128GB VRAM)
Full FP16 (231.33 GB) exceeds 128 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 ~5.5 tokens/sec.
🎛️ Interactive VRAM & KV-Cache Tuner
📊 Real-Time Memory Footprint
71.99 GB / 128 GB
🟦 Weights (69.31 GB)
🟪 KV Cache (2 GB)
🟨 Activation Buffer (0.67 GB)
Total Required VRAM71.99 GB
Available Hardware VRAM128 GB (Apple M4 Max (128GB Unified))
VRAM Headroom / Overflow56.01 GB
Estimated Generation Speed~5.5 tokens/sec
Architecture & AttentionDense-GQA (64L, 8 KV heads)
🚀 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
CLOUD GPU ALTERNATIVE
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 → |
| Vast.ai | Cheapest Spot GPU Market | $0.29 / hr | $0.95 / hr | $1.79 / hr | Save 50–70% on RTX 4090 / 5090 / A100 rentals | Check Spot GPUs → |
| Vultr Cloud GPU | Enterprise SLA & Bare Metal | — | $1.79 / hr | $2.69 / hr | $300 Free Credit for New Users | Claim $300 Credit → |
| DigitalOcean GPU Droplets | Managed H100 Droplets | — | — | $2.99 / hr | $200 Free Trial (60 Days) | Get $200 Free → |
All Quantization Formats: Cohere Command-A 111B on Apple M4 Max (128GB Unified)
| Quantization Format | Bits/Weight | Weights VRAM | KV Cache (8K) | Total VRAM | Fits on 1x Apple M4 Max (128GB Unified) (128GB)? | Est. Speed | GPUs Needed |
|---|---|---|---|---|---|---|---|
| FP16 / BF16 (Unquantized) | 16 bpw | 228.66 GB | 2 GB | 231.33 GB | No (Over by 103.3GB) | ~1.2 tok/s | 2x Apple M4 Max (128GB Unified) |
| FP8 (W8A8 / E4M3) | 8.5 bpw | 121.48 GB | 2 GB | 124.15 GB | Tight (97%) | ~3.1 tok/s | 1x Apple M4 Max (128GB Unified) |
| GGUF Q8_0 (8-bit) | 8.5 bpw | 121.48 GB | 2 GB | 124.15 GB | Tight (97%) | ~3.1 tok/s | 1x Apple M4 Max (128GB Unified) |
| GGUF Q6_K (6-bit) | 6.56 bpw | 93.75 GB | 2 GB | 96.42 GB | Yes (75%) | ~4.1 tok/s | 1x Apple M4 Max (128GB Unified) |
| GGUF Q5_K_M (5-bit) | 5.69 bpw | 81.32 GB | 2 GB | 83.99 GB | Yes (66%) | ~4.7 tok/s | 1x Apple M4 Max (128GB Unified) |
| GGUF Q4_K_M / AWQ (4-bit) | 4.85 bpw | 69.31 GB | 2 GB | 71.99 GB | Yes (56%) | ~5.5 tok/s | 1x Apple M4 Max (128GB Unified) |
| GGUF IQ4_XS (4.25 bpw) | 4.25 bpw | 60.74 GB | 2 GB | 63.41 GB | Yes (50%) | ~6.3 tok/s | 1x Apple M4 Max (128GB Unified) |
| GGUF Q3_K_M (3-bit) | 3.91 bpw | 55.88 GB | 2 GB | 58.55 GB | Yes (46%) | ~6.8 tok/s | 1x Apple M4 Max (128GB Unified) |
Frequently Asked Questions (Cohere Command-A 111B + Apple M4 Max (128GB Unified))
Can Apple M4 Max (128GB Unified) (128GB) run Cohere Command-A 111B locally?
Yes — Apple M4 Max (128GB Unified) (128GB) can run Cohere Command-A 111B using FP8 (W8A8 / E4M3). Full FP16 (231.33 GB) exceeds 128 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 ~5.5 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.
Compare Cohere Command-A 111B on Other GPUs
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