How much VRAM does Command R+ 104B need?

Command R+ 104B requires approximately 0.0 GB VRAM at Q4_K_M quantization with 1K context and 10% overhead. At Q8_0 it needs ~0.0 GB, and at FP16 ~0.0 GB. Use the PCPARTGUIDE VRAM Calculator for exact requirements at any context length and overhead setting.

Can I run Command R+ 104B on a consumer GPU?

Command R+ 104B is a 104B parameter model and requires datacenter or multi-GPU setups. At Q4_K_M, it needs approximately 0.0 GB VRAM, which exceeds consumer GPU limits. It can run on H100 (80 GB) or A100 (80 GB) datacenter GPUs, or across multiple consumer GPUs with tensor parallelism.

What is Command R+ 104B's context window?

Command R+ 104B supports a maximum context window of 128,000 tokens. The default context is 8,192 tokens. Larger contexts require proportionally more VRAM for the KV cache — use the VRAM Calculator to estimate exact requirements at your target context length.

What quantization level should I use for Command R+ 104B?

For Command R+ 104B, start with Q4_K_M (~0.0 GB) if VRAM-limited. Q8_0 offers better quality at ~2× the VRAM. If you have enough VRAM, Q6_K (~1.5× Q4 VRAM) provides a good balance. For models this large, Q3_K_M may be necessary to fit on consumer hardware.

How does Command R+ 104B compare to similar models?

Command R+ 104B is a 104.0B Dense model from the Cohere family. Browse other Cohere models on the PCPARTGUIDE directory for comparable alternatives with different parameter counts and VRAM requirements. Use the Model Transparency scores to assess documentation quality and reproducibility.

CohereDenseCC-BY-NC 4.0

Command R+ 104B

Command R+ 104B is a dense transformer language model from the Cohere family, containing 104B parameters across 64 layers. It supports up to 128K tokens of context with a hidden dimension of 12288 and 8 KV heads for efficient grouped-query …

104.0B

Parameters

125K

Max Context

Dense

Architecture

—

Released

Text

Modality

About Command R+ 104B

ResearchEnterprise

Technical Specifications

Total Parameters104.0B

ArchitectureDense

Attention TypeGQA (Grouped Query Attention)

Hidden Dimensiond = 12,288

Transformer Layers64

Attention Heads96

KV Headsn_kv = 8

Head Dimensiond_head = 128

Activation FunctionSwiGLU

NormalizationRMSNorm

Position EmbeddingRoPE

System Requirements

Estimated VRAM at 10% overhead for different quantization methods and context sizes.

Quantization	1K ctx	125K ctx
Q4_K_M0.50 B/W ~97% of FP16	54.01Datacenter GPU	85.01Cluster / Multi-GPU
Q8_01.00 B/W ~100% of FP16	107.8Cluster / Multi-GPU	138.8Cluster / Multi-GPU
F162.00 B/W Reference	215.3Cluster / Multi-GPU	246.3Cluster / Multi-GPU

Fits 24 GB consumer GPU

Fits 80 GB datacenter GPU

Requires cluster / multi-GPU

Other Cohere Models

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Command R 35B

Params

35.0B

Layers

Context

128K

Find the right GPU for Command R+ 104B

Use the interactive VRAM Calculator to see exactly how much memory you need at any quantization level, context length, and overhead setting.

Open VRAM Calculator Best GPU Guide VRAM Guide