C4ai Command R 08 2024 — Hardware Requirements & GPU Compatibility
Chat
C4AI Command R 08-2024 is Cohere Labs' (formerly Cohere For AI's) 32.3-billion-parameter chat model, an August 2024 refresh of the original Command R built for retrieval-augmented generation with citations, single-step tool use, and multi-step agentic tool use. It is an auto-regressive transformer using grouped-query attention for faster inference, trained and evaluated across a wide multilingual set including English, French, Spanish, German, Japanese, Korean, Arabic, and Simplified Chinese, among others. Cohere positions this release around stronger grounded RAG capability and broader multilingual coverage rather than a change in scale. At 32.3 billion parameters, it needs a high-end consumer GPU or a multi-GPU setup once quantized. Context length is 128,000 tokens. It is released under a Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license plus Cohere's Acceptable Use Policy, restricting the model to non-commercial use. It was published in August 2024.
1.3K downloads
174 likesSpecifications
- Publisher
- Cohere
- Family
- Command R
- Parameters
- 32.3B
- Release Date
- 2024-08-19
- License
- CC BY-NC 4.0
Get Started
HuggingFace
How Much VRAM Does C4ai Command R 08 2024 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 15.1 GB | — | 13.73 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 15.5 GB | — | 14.13 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 17.3 GB | — | 15.74 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 21.3 GB | — | 19.38 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 25.3 GB | — | 23.01 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 29.3 GB | — | 26.64 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 35.5 GB | — | 32.30 GB | 8-bit quantization, near-lossless |
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 08 2024?
Q4_K_M · 21.3 GBC4ai Command R 08 2024 (Q4_K_M) requires 21.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 28+ GB is recommended. 7 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run C4ai Command R 08 2024?
Q4_K_M · 21.3 GB41 devices with unified memory can run C4ai Command R 08 2024, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightWhere to Download C4ai Command R 08 2024
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Benchmarks
Benchmark details →Related Models
Frequently Asked Questions
- How much VRAM does C4ai Command R 08 2024 need?
C4ai Command R 08 2024 requires 21.3 GB of VRAM at Q4_K_M, or 71.0 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 32.3B × 4.8 bits ÷ 8 = 19.4 GB
KV Cache + Overhead ≈ 1.9 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M21.3 GB- Can NVIDIA GeForce RTX 4090 run C4ai Command R 08 2024?
Yes, at Q4_K_M (21.3 GB) or lower. Higher quantizations like Q5_K_S (24.4 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for C4ai Command R 08 2024?
For C4ai Command R 08 2024, Q4_K_M (21.3 GB) offers the best balance of quality and VRAM usage. Q5_K_S (24.4 GB) provides better quality if you have the VRAM. The smallest option is IQ3_XS at 14.7 GB.
VRAM requirement by quantization
IQ3_XS14.7 GBIQ3_M16.0 GBIQ4_XS19.1 GBQ4_K_M ★21.3 GBQ5_K_M25.3 GBBF1671.0 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run C4ai Command R 08 2024 on a Mac?
C4ai Command R 08 2024 requires at least 14.7 GB at IQ3_XS, 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 08 2024 locally?
Yes — C4ai Command R 08 2024 can run locally on consumer hardware. At Q4_K_M quantization it needs 21.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is C4ai Command R 08 2024?
At Q4_K_M, C4ai Command R 08 2024 can reach ~225 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~31 tok/s. 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 ÷ 21.3 × 0.65 = ~244 tok/s
Estimated speed at Q4_K_M (21.3 GB)
~244 tok/s~31 tok/s~244 tok/s~225 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 08 2024?
At Q4_K_M, the download is about 19.38 GB. The full-precision BF16 version is 64.59 GB. The smallest option (IQ3_XS) is 13.32 GB.
- Which GPUs can run C4ai Command R 08 2024?
7 consumer GPUs can run C4ai Command R 08 2024 at Q4_K_M (21.3 GB). Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090. 1 GPU have plenty of headroom for comfortable inference.
- Which devices can run C4ai Command R 08 2024?
41 devices with unified memory can run C4ai Command R 08 2024 at Q4_K_M (21.3 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (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.