huihui-devstral-small-2-24b-instruct-2512-abliterated-i1

mistral3 · 24B · Q4_K_S

System manufacturer (AMD Ryzen 7 5800X 8-Core Processor)

32 GB · Microsoft Windows 11 Home 10.0.26200

Tested on September 19, 2026

Top 47% Compare

Global Score

78 /100

Good

Hardware Fit

74/100

Quality

79/100

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Hardware

Machine: System manufacturer
CPU: AMD Ryzen 7 5800X 8-Core Processor
Cores: 16 total (16 perf)
Frequency: 3.8 GHz
RAM: 32 GB DDR4
GPU: AMD Radeon RX 9060 XT
OS: Microsoft Windows 11 Home 10.0.26200
Arch: x64
Power Mode: balanced

Performance

Tokens/sec Tokens generated per second — higher is better: 17.5
Standard deviation Variation of speed across benchmark runs — lower means more consistent: ±0.1
First chunk latency Delay before the first streamed chunk arrives from runtime: 20 ms
Time to first token How fast the model starts responding — lower is better: 279 ms
Load time Time to load the model into memory before inference: N/A
Memory usage RAM consumed during inference — percentage of total system memory: 15.6 GB (49%)
Total tokens Total number of tokens generated across all benchmark prompts: 981

Score breakdown

Speed

29/50

Time to first token

20/20

Memory

25/30

Quality

Reasoning

16/20

Coding

17/20

Instruction following

15/20

Structured output

14/15

Math

7/15

Multilingual

10/10

Category levels

Reasoning: Strong Coding: Strong Instruction Following: Strong Structured Output: Strong Math: Weak Multilingual: Strong

Metadata

Spec version: 0.2.1
Runtime: LM Studio
Model format: GGUF
Hardware profile: BALANCED
Result hash: 6cf9ef69b6da7bcd91ff02d8828b10993ad9e3a987172a78b0cf37a939c0b1d3

Interpretation

Hardware fit: 74/100. Overall suitability: GOOD (Global 78/100). Category profile: Reasoning: Strong, Coding: Strong, Instruction Following: Strong, Structured Output: Strong, Math: Weak, Multilingual: Strong.

Warnings

Model memory footprint is estimated via LM Studio CLI rather than measured from a fresh load.

Bench Environment

CPU load: avg 11% (peak 21%)

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$ npm install -g metrillm@latest

$ metrillm

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Or run without installing: npx metrillm@latest