C4ai Command R Plus — Hardware Requirements & GPU Compatibility
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C4ai Command R Plus is a 103.8B-parameter open language model from Cohere in the Command R family. At BF16 it needs about 228.38 GB of VRAM — see which GPUs and Macs can run it below.
4.9K downloads
1.8K likesSpecifications
- Publisher
- Cohere
- Family
- Command R
- Parameters
- 103.8B
- Release Date
- 2024-04-03
- License
- CC BY-NC 4.0
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HuggingFace
How Much VRAM Does C4ai Command R Plus Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 228.4 GB | — | 207.62 GB | Brain floating point 16 — preferred for training |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run C4ai Command R Plus?
BF16 · 228.4 GBC4ai Command R Plus (BF16) requires 228.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 297+ GB is recommended. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run C4ai Command R Plus?
BF16 · 228.4 GB3 devices with unified memory can run C4ai Command R Plus, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does C4ai Command R Plus need?
C4ai Command R Plus requires 228.4 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 103.8B × 16 bits ÷ 8 = 207.6 GB
KV Cache + Overhead ≈ 20.8 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
BF16228.4 GB- Can NVIDIA GeForce RTX 5090 run C4ai Command R Plus?
No — C4ai Command R Plus requires at least 228.4 GB at BF16, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- Can I run C4ai Command R Plus on a Mac?
C4ai Command R Plus requires at least 228.4 GB at BF16, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run C4ai Command R Plus locally?
Yes — C4ai Command R Plus can run locally on consumer hardware. At BF16 quantization it needs 228.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is C4ai Command R Plus?
At BF16, C4ai Command R Plus can reach ~19 tok/s on AMD Instinct MI350X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B300 → 8000 ÷ 228.4 × 0.65 = ~23 tok/s
Estimated speed at BF16 (228.4 GB)
~23 tok/s~19 tok/s~19 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of C4ai Command R Plus?
At BF16, the download is about 207.62 GB.
- Which GPUs can run C4ai Command R Plus?
No single consumer GPU has enough VRAM to run C4ai Command R Plus at BF16 (228.4 GB). Multi-GPU or professional hardware is required.
- Which devices can run C4ai Command R Plus?
4 devices with unified memory can run C4ai Command R Plus at BF16 (228.4 GB), including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), NVIDIA DGX A100 640GB, NVIDIA DGX H100. Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.