Mistral Large 2
Mistral AI · dense · 123B parameters · 131,072 context
Parameters
123B
Context Window
128K tokens
Architecture
Dense
Best GPU
B200 SXM
Cheapest API
$2.50/M
Quality Score
75/100
Intelligence Brief
Mistral Large 2 is a 123B parameter DENSE model from Mistral AI, featuring Grouped Query Attention (GQA) with 88 layers and 12,288 hidden dimensions. With a 131,072 token context window, it supports tools, structured output, code, math, multilingual. On standardized benchmarks, it achieves MMLU 84, HumanEval 53, GSM8K 91.2. The most cost-effective API deployment is via together at $2.50/M output tokens. For self-hosted inference, B200 SXM delivers optimal throughput at $4261/month.
Architecture Details
Memory Requirements
BF16 Weights
246.0 GB
FP8 Weights
123.0 GB
INT4 Weights
61.5 GB
GPU Compatibility Matrix
Mistral Large 2 is compatible with 21% of GPU configurations across 41 GPUs at 3 precision levels.
GPU Recommendations
FP8 · 1 GPU · tensorrt-llm
100/100
score
Throughput
280.0 tok/s
Latency (ITL)
3.6ms
Est. TTFT
1ms
Cost/Month
$4261
Cost/M Tokens
$5.79
FP8 · 1 GPU · tensorrt-llm
100/100
score
Throughput
280.0 tok/s
Latency (ITL)
3.6ms
Est. TTFT
1ms
Cost/Month
$4271
Cost/M Tokens
$5.80
FP8 · 1 GPU · tensorrt-llm
100/100
score
Throughput
280.0 tok/s
Latency (ITL)
3.6ms
Est. TTFT
1ms
Cost/Month
$6169
Cost/M Tokens
$8.38
Deployment Options
API Deployment
together
$2.50/M
output tokens
Single GPU
B200 SXM
$4261/mo
Min VRAM: 123 GB
Multi-GPU
H20 x2
280.0 tok/s
TP· $1879/mo
API Pricing Comparison
| Provider | Input $/M | Output $/M | Badges |
|---|---|---|---|
| together | $2.50 | $2.50 | Cheapest |
| mistral | $2.00 | $6.00 | Low Input |
Cost Analysis
| Provider | Input $/M | Output $/M | ~Monthly Cost |
|---|---|---|---|
| togetherBest Value | $2.50 | $2.50 | $25 |
| mistral | $2.00 | $6.00 | $40 |
Cost per 1,000 Requests
Short (500 tok)
$1.75
via together
Medium (2K tok)
$7.00
via together
Long (8K tok)
$25.00
via together
Performance Estimates
Throughput by GPU
VRAM Breakdown (B200 SXM, FP8)
Precision Impact
bf16
246.0 GB
weights/GPU
fp8
123.0 GB
weights/GPU
~280.0 tok/s
int4
61.5 GB
weights/GPU
Quality Benchmarks
Capabilities
Features
Supported Frameworks
Supported Precisions
Where to Deploy Mistral Large 2
Self-Hosted Infrastructure
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Frequently Asked Questions
How much VRAM does Mistral Large 2 need for inference?
Mistral Large 2 requires approximately 246.0 GB of VRAM at BF16 precision, 123.0 GB at FP8, or 61.5 GB at INT4 quantization. Additional VRAM is needed for KV-cache (360448 bytes per token) and activations (~3.50 GB).
What is the best GPU for Mistral Large 2?
The top recommended GPU for Mistral Large 2 is the B200 SXM using FP8 precision. It achieves approximately 280.0 tokens/sec at an estimated cost of $4261/month ($5.79/M tokens). Score: 100/100.
How much does Mistral Large 2 inference cost?
Mistral Large 2 API inference starts from $2.50/M input tokens and $2.50/M output tokens. Self-hosted inference costs depend on your GPU configuration — use our ROI calculator for a detailed breakdown.