FitMyLLM
Cohere/Dense

Coherec4ai-command-r-v01 35B

🚨 This model is non-quantized version of Cohere Labs Command-R. You can find the quantized version of Cohere Labs Command-R using bitsandbytes here.

chatTool Use
35B
Parameters
128K
Context length
7
Benchmarks
14
Quantizations
0
Architecture
Dense
Released
2024-03-11
Layers
40
KV Heads
8
Head Dim
128
Family
command

Quantization Options

Context length:
QuantBitsVRAM @ 16KQuality
IQ3_XXS3.25
16.6 GB
14.7 + 1.9 KV
low
IQ3_XS3.5
17.7 GB
15.8 + 1.9 KV
low
Q3_K_S3.64
18.3 GB
16.4 + 1.9 KV
low
IQ3_M3.76
18.8 GB
16.9 + 1.9 KV
low
Q3_K_M4
19.9 GB
18.0 + 1.9 KV
low
Q3_K_L4.3
21.2 GB
19.3 + 1.9 KV
moderate
IQ4_XS4.46
21.9 GB
20.0 + 1.9 KV
moderate
Q4_K_S4.67
22.8 GB
20.9 + 1.9 KV
moderate
Q4_K_M4.89
23.8 GB
21.9 + 1.9 KV
good
Q5_K_S5.57
26.7 GB
24.9 + 1.9 KV
good
Q5_K_M5.7
27.3 GB
25.4 + 1.9 KV
good
Q6_K6.56
31.1 GB
29.2 + 1.9 KV
excellent
Q8_08.5
39.6 GB
37.7 + 1.9 KV
lossless
FP1616
72.4 GB
70.5 + 1.9 KV
lossless

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

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Community Ratings

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Benchmarks (7)

IFEval67.5
BigCodeBench37.1
BBH34.6
MMLU-PRO26.3
MUSR16.1
GPQA7.6
MATH3.5

Run this model

Easiest way to get started·Beginners
DOCS ↗
curl -fsSL https://ollama.com/install.sh | sh
$ollama run command-r:35b-v0.1-q4_K_M

Downloads and runs automatically. Add --verbose for speed stats.

▸ 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.

Apple M4 Pro (24GB)
24 GB VRAM • 273 GB/s
APPLE
$1399
NVIDIA L4 24GB
24 GB VRAM • 300 GB/s
NVIDIA
$2500
Apple M2 (24GB)
24 GB VRAM • 100 GB/s
APPLE
$999
Apple M3 (24GB)
24 GB VRAM • 100 GB/s
APPLE
$999
Apple M4 (24GB)
24 GB VRAM • 120 GB/s
APPLE
$699
NVIDIA Tesla M40 24 GB
24 GB VRAM • 288 GB/s
NVIDIA
NVIDIA Tesla P10
24 GB VRAM • 694 GB/s
NVIDIA
NVIDIA Tesla P40
24 GB VRAM • 347 GB/s
NVIDIA
NVIDIA RTX A5000
24 GB VRAM • 768 GB/s
NVIDIA
$2500
NVIDIA L40 CNX
24 GB VRAM • 864 GB/s
NVIDIA
$5000
NVIDIA L40G
24 GB VRAM • 864 GB/s
NVIDIA
$5000

Find the best GPU for c4ai-command-r-v01 35B

Build Hardware for c4ai-command-r-v01 35B

🚨 This model is non-quantized version of Cohere Labs Command-R. You can find the quantized version of Cohere Labs Command-R using bitsandbytes here.

▸ SPEC SHEET

c4ai-command-r-v01 35B35B Dense.

▸ SPECIFICATIONS
PARAMETERS
35B
ARCHITECTURE
Dense Transformer
CONTEXT LENGTH
128K tokens
CAPABILITIES
chat
RELEASE DATE
2024-03-11
PROVIDER
Cohere
FAMILY
command
▸ VRAM REQUIREMENTS
QUANTBPWVRAMQUALITY
IQ3_XXS3.2514.7 GB82%
IQ3_XS3.515.8 GB84%
Q3_K_S3.6416.4 GB85%
IQ3_M3.7616.9 GB86%
Q3_K_M418.0 GB88%
Q3_K_L4.319.3 GB90%
IQ4_XS4.4620.0 GB92%
Q4_K_S4.6720.9 GB93%
Q4_K_M4.8921.9 GB94%
Q5_K_S5.5724.9 GB96%
Q5_K_M5.725.4 GB96%
Q6_K6.5629.2 GB97%
Q8_08.537.7 GB100%
FP161670.5 GB100%
§ 01BENCHMARK SCORES
MMLU-PRO26.3
MATH3.5
IFEval67.5
BBH34.6
GPQA7.6
MUSR16.1
BigCodeBench37.1
§ 02RUN COMMAND

Run c4ai-command-r-v01 35B locally with Ollama — needs 21.9 GB VRAM at Q4_K_M:

$ollama run command-r:35b
§ 03COMPATIBLE GPUs
30 @ Q4_K_M