How much VRAM does Command R 35B need?

Command R 35B requires 30 GB VRAM at recommended quality (Q6_K). At lower quality settings, it can fit in as little as 16 GB.

What quantization should I use for Command R 35B?

For the best quality, use Q6_K (30 GB VRAM). If your GPU has limited VRAM, Q3_K_M (16 GB) is the most efficient option with acceptable quality.

ChatReasoningMulti-Purpose35B

Command R 35B

Cohere · CC-BY-NC-4.0

Cohere's RAG-optimized model with strong reasoning and long-context support.

Parameters: 35B
Context: 128,000 tokens
Released: 2024-03-11
Engines: llama.cpp, ollama, vLLM, TGI

Command R 35B (35B) requires 30 GB VRAM at recommended quality (Q6_K). At efficient quality (Q4_K_M), it fits in 20 GB VRAM. For the best experience, High-End Home AI Server ($3,842) is recommended.

— OwnRig methodology, data updated 2026-03-15

VRAM Requirements

Quality	Quantization	VRAM	File Size
full	Q8_0	38.5 GB	35 GB
recommended	Q6_K	30 GB	26 GB
recommended	Q5_K_M	25 GB	21.5 GB
efficient	Q4_K_M	20 GB	17.5 GB
compressed	Q3_K_M	16 GB	13.5 GB

Context Length Impact

KV cache VRAM at Q6_K quality. Longer context = more memory.

Context	KV Cache	Total VRAM
2K	614 MB	30.6 GBexceeds 24 GB
4K	1.1 GB	31.1 GBexceeds 24 GB
8K	2.2 GB	32.2 GBexceeds 24 GB
16K	4.5 GB	34.5 GBexceeds 24 GB
32K	9 GB	39 GBexceeds 24 GB
64K	17.9 GB	47.9 GBexceeds 24 GB

Compatible GPUs

Performance data for Command R 35B across different hardware.

Device	Quantization	Speed	Rating	Fits in VRAM
NVIDIA GeForce RTX 4060 8GB	Q2_K	—	Not Viable	✗ (offload)
NVIDIA GeForce RTX 4070 Ti 12GB	Q2_K	—	Not Viable	✗ (offload)
NVIDIA GeForce RTX 3080 10GB	Q2_K	—	Not Viable	✗ (offload)
Apple M3 Pro (18GB Unified)	Q3_K_M	—	Not Viable	✗ (offload)

Frequently Asked Questions

How much VRAM does Command R 35B need?: Command R 35B requires 30 GB VRAM at recommended quality (Q6_K). At lower quality settings, it can fit in as little as 16 GB.
What quantization should I use for Command R 35B?: For the best quality, use Q6_K (30 GB VRAM). If your GPU has limited VRAM, Q3_K_M (16 GB) is the most efficient option with acceptable quality.

Data confidence: estimated. Last updated: 2026-03-15. Source