Updated minutes ago
Mistral 7B
Mistral AI · dense · 7.3B parameters · 32,768 context
Quality56.0
Architecture Details
TypeDENSE
Total Parameters7.3B
Active Parameters7.3B
Layers32
Hidden Dimension4,096
Attention Heads32
KV Heads8
Head Dimension128
Vocab Size32,000
Memory Requirements
BF16 Weights
14.6 GB
FP8 Weights
7.3 GB
INT4 Weights
3.6 GB
KV-Cache per Token131072 bytes
Activation Estimate1.00 GB
Fits on (single-node)
B200 SXM BF16B100 SXM BF16GB200 NVL72 (per GPU) BF16GB300 NVL72 (per GPU) BF16H200 SXM BF16H100 SXM BF16H100 PCIe BF16H100 NVL BF16
GPU Recommendations
A10Goptimal
BF16 · 1 GPU · vllm
100/100
score
Throughput
221.9 tok/s
Cost/Month
$285
Cost/M Tokens
$0.49
A30optimal
BF16 · 1 GPU · vllm
100/100
score
Throughput
345.1 tok/s
Cost/Month
$332
Cost/M Tokens
$0.37
RTX 4090optimal
BF16 · 1 GPU · vllm
100/100
score
Throughput
372.8 tok/s
Cost/Month
$370
Cost/M Tokens
$0.38
API Pricing Comparison
| Provider | Input $/M | Output $/M | Badges |
|---|---|---|---|
| deepinfra | $0.07 | $0.07 | Cheapest |
| together | $0.20 | $0.20 |
Quality Benchmarks
MMLU62.5
HumanEval32.0
GSM8K52.2
MT-Bench71.0
Capabilities
Features
✗ Tool Use✗ Vision✓ Code✓ Math✗ Reasoning✓ Multilingual✗ Structured Output
Supported Frameworks
vllmsglangtgitensorrt-llmollama
Supported Precisions
BF16 (default)FP8INT4