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Meta · open-weights

Llama 3.2 90B

Llama 3.2 90B is an open-weights language model from Meta, released September 24, 2024. It has 90B parameters (dense). On the Epoch Capabilities Index it scores 122.8, rank 178 of 256 rated models.

Check it against your own PC →

Lab
Meta
Released
September 24, 2024
Weights
Open restricted use
Parameters
90B dense
Context
–
Price / M tokens
–
Epoch ECI
122.8 #178

Can you run Llama 3.2 90B locally?

The smallest common GPU it runs on is 32 GB of VRAM (with 32 GB of RAM): IQ2_XXS, fully on GPU, roughly 44 tokens per second. Estimates assume 32 GB of DDR5 RAM, a 16K context and llama.cpp-style GGUF quantization. Enter your exact hardware in the live app.

GPU memoryVerdictBest quantVRAMRAMtok/s
6 GB VRAMVery slowIQ2_XXS (heavy loss)5.2 GB22.9 GB~1
8 GB VRAMVery slowIQ2_XXS (heavy loss)7.2 GB20.9 GB~1
12 GB VRAMVery slowIQ2_XXS (heavy loss)11.2 GB16.9 GB~2
16 GB VRAMVery slowIQ2_XXS (heavy loss)15.2 GB12.9 GB~2
24 GB VRAMVery slowIQ2_XXS (heavy loss)23.2 GB4.9 GB~5
32 GB VRAMRuns on GPUIQ2_XXS (heavy loss)28.1 GB–~44

Strengths by category

ModelTape's 0–100 fit on Epoch AI benchmark results.

CategoryScoreBased on
Math11.6MATH level 5, OTIS Mock AIME 2024-2025
Knowledge29.0GPQA diamond, MMLU

Benchmark results

Benchmark (Epoch AI)Score
MMLU73.7%
MATH level 539.4%
Balrog27.3%
GPQA Diamond22.3%
OTIS Mock AIME2.5%

Links

Epoch AI benchmarks

Models with a similar score

GPT-4.1 nano · Mistral Large 2 (Jul 2024) · Llama 3.3 70B · GPT-4 Turbo (Apr 2024) · Claude 3 Opus · Gemini 1.5 Pro (May 2024) · Claude 3.5 Haiku · GPT-4 Turbo (Nov 2023)

More from Meta

Muse Ember · Muse Spark Open · Muse Wonder · Muse Wonder Full · Muse Spark 1.3 · Muse Spark 1.3 Contributor · Muse Glimmer · Muse Glimmer 30B assistant

Data updated Thu, 12 Nov 2026 09:42 UTC. Benchmarks, Capabilities Index and model announcements: Epoch AI, “Capabilities & benchmarking” and “Data on AI models” (CC BY 4.0); category scores are ModelTape's own fit on Epoch's results. Releases and prices: models.dev (MIT). Sizes and quantizations: Hugging Face. “Runs on” figures are estimates from model size, quantization and memory bandwidth (±30–50%).
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