Cohere/Dense

CohereCommand-R+ 104B

Command R+ — Cohere's flagship open model. Excellent for RAG and tool use.

chatreasoningTool Use
104B
Parameters
128K
Context length
10
Benchmarks
17
Quantizations
50K
HF downloads
Architecture
Dense
Released
2024-04-04
Layers
64
KV Heads
8
Head Dim
128
Family
command

Quantization Options

QuantBitsVRAMQuality
IQ2_XXS2.3831.4 GBlow
IQ2_M2.9338.6 GBlow
Q2_K3.1641.6 GBlow
IQ3_XXS3.2542.7 GBlow
IQ3_XS3.546.0 GBlow
Q3_K_S3.6447.8 GBlow
IQ3_M3.7649.4 GBlow
Q3_K_M452.5 GBlow
Q3_K_L4.356.4 GBmoderate
IQ4_XS4.4658.5 GBmoderate
Q4_K_S4.6761.2 GBmoderate
Q4_K_M4.8964.1 GBgood
Q5_K_S5.5772.9 GBgood
Q5_K_M5.774.6 GBgood
Q6_K6.5685.8 GBexcellent
Q8_08.5111.0 GBlossless
FP1616208.5 GBlossless

Select your GPU above to see speed estimates and compatibility for each quantization.

READY TO RUN THIS?RENT BY THE HOUR

RENT A GPU AND RUN COMMAND-R+ 104B NOW

Spin up an A100 / H100 / 4090 in ~60s. Pay by the second. Cancel anytime.

Community Ratings

Loading ratings...

Benchmarks (10)

Arena Elo1230
IFEval85.4
HumanEval81.0
BBH74.0
MBPP63.5
MMLU-PRO56.0
MATH47.2
BigCodeBench33.8
MUSR19.8
GPQA13.4

Run this model

Easiest way to get started·Beginners
DOCS ↗
curl -fsSL https://ollama.com/install.sh | sh
$ollama run command:104b-q4_K_M

Tag may need adjustment — check ollama.com/library/command for available tags.

▸ SETUP GUIDE
>_

Auto-setup with fitmyllm CLI

Detects your GPU, recommends the best model, downloads it, and starts chatting — zero config. Benchmarks your speed and contributes anonymous data to improve predictions.

pip install fitmyllmthen run fitmyllmLearn more
Auto-detect GPULive tok/s in chatSpeed benchmarks9 inference engines

GPUs that can run this model

At Q4_K_M quantization. Sorted by minimum VRAM.

NVIDIA RTX PRO 5000 72 GB Blackwell
72 GB VRAM • 1340 GB/s
NVIDIA
$6999
NVIDIA H100 SXM5 80GB
80 GB VRAM • 3350 GB/s
NVIDIA
$25000
NVIDIA H100 PCIe 80GB
80 GB VRAM • 2000 GB/s
NVIDIA
$25000
NVIDIA A100 SXM 80GB
80 GB VRAM • 2039 GB/s
NVIDIA
$10000
NVIDIA A100 PCIe 80GB
80 GB VRAM • 1935 GB/s
NVIDIA
$10000
NVIDIA A100 SXM4 80 GB
80 GB VRAM • 2040 GB/s
NVIDIA
$15000
NVIDIA A100 PCIe 80 GB
80 GB VRAM • 1940 GB/s
NVIDIA
$10000
NVIDIA A100X
80 GB VRAM • 2040 GB/s
NVIDIA
NVIDIA H100 PCIe 80 GB
80 GB VRAM • 2040 GB/s
NVIDIA
$25000
NVIDIA H100 SXM5 80 GB
80 GB VRAM • 3360 GB/s
NVIDIA
$25000
NVIDIA H100 CNX
80 GB VRAM • 2040 GB/s
NVIDIA
$25000
NVIDIA A800 PCIe 80 GB
80 GB VRAM • 1940 GB/s
NVIDIA
NVIDIA A800 SXM4 80 GB
80 GB VRAM • 2040 GB/s
NVIDIA
NVIDIA H800 PCIe 80 GB
80 GB VRAM • 2040 GB/s
NVIDIA
NVIDIA H800 SXM5
80 GB VRAM • 3360 GB/s
NVIDIA
NVIDIA RTX 6000D
84 GB VRAM • 1570 GB/s
NVIDIA
$7500
NVIDIA B200
90 GB VRAM • 4100 GB/s
NVIDIA
$30000
NVIDIA H100 NVL 94 GB
94 GB VRAM • 3940 GB/s
NVIDIA
$30000
NVIDIA H100 SXM5 94 GB
94 GB VRAM • 3360 GB/s
NVIDIA
$25000
RTX Pro 6000
96 GB VRAM • 1792 GB/s
NVIDIA
$8565
NVIDIA H100 PCIe 96 GB
96 GB VRAM • 3360 GB/s
NVIDIA
$25000
NVIDIA H100 SXM5 96 GB
96 GB VRAM • 3360 GB/s
NVIDIA
$25000
Intel Data Center GPU Max 1350
96 GB VRAM • 2460 GB/s
INTEL
NVIDIA RTX PRO 6000 Blackwell Server
96 GB VRAM • 1790 GB/s
NVIDIA
$9999
NVIDIA RTX PRO 6000 Blackwell
96 GB VRAM • 1790 GB/s
NVIDIA
$9999
AMD Instinct MI300A
120 GB VRAM • 5300 GB/s
AMD
$12000
Apple M4 Max (128GB)
128 GB VRAM • 546 GB/s
APPLE
$3999
AMD Instinct MI250X
128 GB VRAM • 3277 GB/s
AMD
$10000
Apple M1 Ultra (128GB)
128 GB VRAM • 800 GB/s
APPLE
$4999
Apple M2 Ultra (128GB)
128 GB VRAM • 800 GB/s
APPLE
$3999

Find the best GPU for Command-R+ 104B

Build Hardware for Command-R+ 104B

Command R+ — Cohere's flagship open model. Excellent for RAG and tool use.

▸ SPEC SHEET

Command-R+ 104B104B Dense.

▸ SPECIFICATIONS
PARAMETERS
104B
ARCHITECTURE
Dense Transformer
CONTEXT LENGTH
128K tokens
CAPABILITIES
chat, reasoning
RELEASE DATE
2024-04-04
PROVIDER
Cohere
FAMILY
command
▸ VRAM REQUIREMENTS
QUANTBPWVRAMQUALITY
IQ2_XXS2.3831.4 GB65%
IQ2_M2.9338.6 GB75%
Q2_K3.1641.6 GB78%
IQ3_XXS3.2542.7 GB82%
IQ3_XS3.546.0 GB84%
Q3_K_S3.6447.8 GB85%
IQ3_M3.7649.4 GB86%
Q3_K_M452.5 GB88%
Q3_K_L4.356.4 GB90%
IQ4_XS4.4658.5 GB92%
Q4_K_S4.6761.2 GB93%
Q4_K_M4.8964.1 GB94%
Q5_K_S5.5772.9 GB96%
Q5_K_M5.774.6 GB96%
Q6_K6.5685.8 GB97%
Q8_08.5111.0 GB100%
FP1616208.5 GB100%
§ 01BENCHMARK SCORES
HumanEval81.0
MMLU-PRO56.0
MATH47.2
IFEval85.4
BBH74.0
GPQA13.4
MUSR19.8
MBPP63.5
BigCodeBench33.8
Arena Elo1230.0