Mistral Small 24B
Mistral AI · dense · 24B parameters · 32,768 context
Parameters
24B
Context Window
32K tokens
Architecture
Dense
Best GPU
H20
Cheapest API
$0.30/M
Quality Score
68/100
Intelligence Brief
Mistral Small 24B is a 24B parameter DENSE model from Mistral AI, featuring Grouped Query Attention (GQA) with 40 layers and 6,144 hidden dimensions. With a 32,768 token context window, it supports tools, structured output, code, math, multilingual. On standardized benchmarks, it achieves MMLU 72, HumanEval 45, GSM8K 70. The most cost-effective API deployment is via mistral at $0.30/M output tokens. For self-hosted inference, H20 delivers optimal throughput at $940/month.
Architecture Details
Memory Requirements
BF16 Weights
48.0 GB
FP8 Weights
24.0 GB
INT4 Weights
12.0 GB
GPU Compatibility Matrix
Mistral Small 24B is compatible with 62% of GPU configurations across 41 GPUs at 3 precision levels.
GPU Recommendations
FP8 · 1 GPU · tensorrt-llm
100/100
score
Throughput
1.1K tok/s
Latency (ITL)
1.0ms
Est. TTFT
0ms
Cost/Month
$940
Cost/M Tokens
$0.34
FP8 · 1 GPU · tensorrt-llm
95/100
score
Throughput
1.1K tok/s
Latency (ITL)
1.0ms
Est. TTFT
0ms
Cost/Month
$1794
Cost/M Tokens
$0.65
FP8 · 1 GPU · tensorrt-llm
95/100
score
Throughput
697.1 tok/s
Latency (ITL)
1.4ms
Est. TTFT
0ms
Cost/Month
$1794
Cost/M Tokens
$0.98
Deployment Options
API Deployment
mistral
$0.30/M
output tokens
Single GPU
H20
$940/mo
Min VRAM: 24 GB
Multi-GPU
A100 40GB SXM x2
344.0 tok/s
TP· $1613/mo
API Pricing Comparison
| Provider | Input $/M | Output $/M | Badges |
|---|---|---|---|
| mistral | $0.10 | $0.30 | Cheapest |
| together | $0.30 | $0.30 |
Cost Analysis
| Provider | Input $/M | Output $/M | ~Monthly Cost |
|---|---|---|---|
| mistralBest Value | $0.10 | $0.30 | $2 |
| together | $0.30 | $0.30 | $3 |
Cost per 1,000 Requests
Short (500 tok)
$0.11
via mistral
Medium (2K tok)
$0.44
via mistral
Long (8K tok)
$1.40
via mistral
Performance Estimates
Throughput by GPU
VRAM Breakdown (H20, FP8)
Precision Impact
bf16
48.0 GB
weights/GPU
fp8
24.0 GB
weights/GPU
~1.1K tok/s
int4
12.0 GB
weights/GPU
Quality Benchmarks
Capabilities
Features
Supported Frameworks
Supported Precisions
Where to Deploy Mistral Small 24B
Self-Hosted Infrastructure
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Frequently Asked Questions
How much VRAM does Mistral Small 24B need for inference?
Mistral Small 24B requires approximately 48.0 GB of VRAM at BF16 precision, 24.0 GB at FP8, or 12.0 GB at INT4 quantization. Additional VRAM is needed for KV-cache (163840 bytes per token) and activations (~1.50 GB).
What is the best GPU for Mistral Small 24B?
The top recommended GPU for Mistral Small 24B is the H20 using FP8 precision. It achieves approximately 1.1K tokens/sec at an estimated cost of $940/month ($0.34/M tokens). Score: 100/100.
How much does Mistral Small 24B inference cost?
Mistral Small 24B API inference starts from $0.10/M input tokens and $0.30/M output tokens. Self-hosted inference costs depend on your GPU configuration — use our ROI calculator for a detailed breakdown.