Command R
Cohere · dense · 35B parameters · 131,072 context
Parameters
35B
Context Window
128K tokens
Architecture
Dense
Best GPU
H20
Cheapest API
$0.50/M
Quality Score
68/100
Intelligence Brief
Command R is a 35B parameter DENSE model from Cohere, featuring Grouped Query Attention (GQA) with 40 layers and 8,192 hidden dimensions. With a 131,072 token context window, it supports tools, structured output, code, multilingual. On standardized benchmarks, it achieves MMLU 73, HumanEval 42, GSM8K 75. The most cost-effective API deployment is via together at $0.50/M output tokens. For self-hosted inference, H20 delivers optimal throughput at $940/month.
Architecture Details
Memory Requirements
BF16 Weights
70.0 GB
FP8 Weights
35.0 GB
INT4 Weights
17.5 GB
GPU Compatibility Matrix
Command R is compatible with 57% of GPU configurations across 41 GPUs at 3 precision levels.
GPU Recommendations
FP8 · 1 GPU · tensorrt-llm
100/100
score
Throughput
956.1 tok/s
Latency (ITL)
1.0ms
Est. TTFT
0ms
Cost/Month
$940
Cost/M Tokens
$0.37
FP8 · 1 GPU · tensorrt-llm
98/100
score
Throughput
1.1K tok/s
Latency (ITL)
1.0ms
Est. TTFT
0ms
Cost/Month
$4261
Cost/M Tokens
$1.54
FP8 · 1 GPU · tensorrt-llm
95/100
score
Throughput
1.1K tok/s
Latency (ITL)
1.0ms
Est. TTFT
0ms
Cost/Month
$2553
Cost/M Tokens
$0.93
Deployment Options
API Deployment
together
$0.50/M
output tokens
Single GPU
H20
$940/mo
Min VRAM: 35 GB
Multi-GPU
RTX A6000 x2
104.7 tok/s
TP· $930/mo
API Pricing Comparison
| Provider | Input $/M | Output $/M | Badges |
|---|---|---|---|
| together | $0.50 | $0.50 | Cheapest |
| cohere | $0.50 | $1.50 |
Cost Analysis
| Provider | Input $/M | Output $/M | ~Monthly Cost |
|---|---|---|---|
| togetherBest Value | $0.50 | $0.50 | $5 |
| cohere | $0.50 | $1.50 | $10 |
Cost per 1,000 Requests
Short (500 tok)
$0.35
via together
Medium (2K tok)
$1.40
via together
Long (8K tok)
$5.00
via together
Performance Estimates
Throughput by GPU
VRAM Breakdown (H20, FP8)
Precision Impact
bf16
70.0 GB
weights/GPU
fp8
35.0 GB
weights/GPU
~956.1 tok/s
int4
17.5 GB
weights/GPU
Quality Benchmarks
Capabilities
Features
Supported Frameworks
Supported Precisions
Where to Deploy Command R
Self-Hosted Infrastructure
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Yi 1.5 34B
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Frequently Asked Questions
How much VRAM does Command R need for inference?
Command R requires approximately 70.0 GB of VRAM at BF16 precision, 35.0 GB at FP8, or 17.5 GB at INT4 quantization. Additional VRAM is needed for KV-cache (163840 bytes per token) and activations (~2.00 GB).
What is the best GPU for Command R?
The top recommended GPU for Command R is the H20 using FP8 precision. It achieves approximately 956.1 tokens/sec at an estimated cost of $940/month ($0.37/M tokens). Score: 100/100.
How much does Command R inference cost?
Command R API inference starts from $0.50/M input tokens and $0.50/M output tokens. Self-hosted inference costs depend on your GPU configuration — use our ROI calculator for a detailed breakdown.