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 Q4_K_M it needs about 68.52 GB of VRAM — see which GPUs and Macs can run it below.
C4ai Command R Plus runs on a 96 GB+ Mac or a workstation GPU
Best local pick → Mac Studio (M3 Ultra, 96GB) · Q4_K_M · 73.8 GB · ~8 tok/s
Macs from 64 GB (Q2_K · ~12 tok/s)
CC BY-NC 4.0
Not set up for it?
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run C4ai Command R Plus
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve CohereLabs/c4ai-command-r-plus --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on RTX PRO 6000 (96 GB) (22 GB headroom) · Q4_K_M
- Generation speed
- ~17 tok/s
- generation speed
- Cost per 1M output tokens
- $20.08
- per 1M output tokens
How Much VRAM Does C4ai Command R Plus Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 48.5 GB | 59.8 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Does not fit: needs 53.8 GB | |
| 55.7 GB | 66.9 GB | 3-bit medium — 3-bit medium quantization | Does not fit: needs 60.9 GB | |
| 68.5 GB | 79.8 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Does not fit: needs 73.8 GB | |
| 81.4 GB | 92.6 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Does not fit: needs 86.6 GB | |
| 94.2 GB | 105.5 GB | 6-bit, very good — 6-bit quantization, very good quality | Does not fit: needs 99.5 GB | |
| 114.2 GB | 125.5 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Does not fit: needs 119.5 GB | |
| 228.4 GB | 239.7 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Does not fit: needs 233.6 GB |
Selected Q4_K_M
4-bit medium quantization73.8GBat 16K context
- Weights
- 67.5 GB
- KV cache
- 6.0 GB
- Overhead
- 0.3 GB
Doesn't fit RTX 4090 (reference) · needs 49.8 GB more
Which Hardware Can Run C4ai Command R Plus?
Q4_K_M · 68.5 GBC4ai Command R Plus (Q4_K_M) requires 68.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 90+ GB is recommended. No consumer GPU has enough memory. 30 devices with unified memory can run C4ai Command R Plus, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Studio (M3 Ultra, 96GB).
64–80 GB
Q4_K_M · best fit Q3_K_M (60.9 GB)
3 Mac classes · 1 device family
M2 Ultra*64 GB~10Simulate output on Mac Pro M2 Ultra (64 GB)M5 Max64 GB~8Simulate output on Mac Studio M5 Max (64 GB)Orin~2Simulate output on NVIDIA Jetson AGX Orin 64GB96 GB
fits · 22.2 GB free · up to 32K ctx
2 Mac classes
M3 Ultra*96 GB~8Simulate output on Mac Studio (M3 Ultra, 96GB)M3 Max*96 GB~3Simulate output on MacBook Pro 14" M3 Max (96 GB)
By VRAM tier · typical GPUs and Macs at Q4_K_M
How good is C4ai Command R Plus?
llmrun Score blends public benchmark boards into one number; 100 = the average open model.
Not enough data yet for an llmrun Score.
A Score needs results on enough independent boards. How the Composite Score is built
Benchmarks
This is the single board showing results for this model; badges show who ran the test.
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Alternatives to C4ai Command R Plus
Command R family · 5 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Llama 4 Scout 17B 16E InstructMeta | 81.8 | 77 GB | —needs > 24 GB | same size |
| GLM 4.6VZ.ai | — | 66 GB | —needs > 24 GB | same size |
| Ring Flash 2.0Inclusion AI | — | 63.1 GB | —needs > 24 GB | same size |
| Ling Flash 2.0Inclusion AI | — | 63.1 GB | —needs > 24 GB | same size |
| Qwen1.5 110BAlibaba | — | 72.4 GB | —needs > 24 GB | same size |
| Sarvam 105Bsarvamai | — | 238.6 GB | —needs > 24 GB | same size |
Specifications
- Publisher
- Cohere
- Family
- Command R
- Parameters
- 103.8B
- Release Date
- 2024-04-03
- License
- CC BY-NC 4.0
Frequently Asked Questions
How much VRAM does C4ai Command R Plus need?
C4ai Command R Plus requires 68.5 GB of VRAM at Q4_K_M, or 228.4 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 103.8B × 4.8 bits ÷ 8 = 62.3 GB
KV Cache + Overhead ≈ 6.2 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
VRAM usage by quantization
Can NVIDIA GeForce RTX 5090 run C4ai Command R Plus?
No — C4ai Command R Plus requires at least 48.5 GB at Q2_K, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
What's the best quantization for C4ai Command R Plus?
For C4ai Command R Plus, Q4_K_M (68.5 GB) offers the best balance of quality and VRAM usage. Q5_K_M (81.4 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 48.5 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run C4ai Command R Plus on a Mac?
Yes — Mac Pro M2 Ultra (64 GB) and 41 other Macs can run C4ai Command R Plus, the smallest at Q2_K. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 68.5 GB of usable unified memory (RAM minus macOS overhead), from Mac Studio (M3 Ultra, 96GB).
Can I run C4ai Command R Plus locally?
Yes — C4ai Command R Plus can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is C4ai Command R Plus?
At Q4_K_M, C4ai Command R Plus can reach ~70 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 B200 → 8000 ÷ 68.5 × 0.65 = ~76 tok/s
Estimated speed at Q4_K_M (68.5 GB)
Real-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 Q4_K_M, the download is about 62.29 GB. The full-precision BF16 version is 207.62 GB. The smallest option (Q2_K) is 44.12 GB.
Which GPUs can run C4ai Command R Plus?
No single consumer GPU has enough VRAM to run C4ai Command R Plus at Q4_K_M (68.5 GB). Multi-GPU or professional hardware is required.
Which devices can run C4ai Command R Plus?
30 devices with unified memory can run C4ai Command R Plus at Q4_K_M (68.5 GB), including ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB), GMKtec EVO-X2 (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.