whatmodel.sh / Granite 34B Code Base (8K)

Granite 34B Code Base (8K)

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Granite 34B Code Base (8K) is an open-weights language model from IBM, released April 2024. Its published size is 33.72B parameters. Licence: apache-2.0.

Catalogue identity notes

Comparison-only. Maker repo ibm-granite/granite-34b-code-base-8k @291a64f: licence tag apache-2.0. 33,715,603,456 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2024-04: month the maker's Hugging Face repo was created (createdAt 2024-04-21). May 2024 Granite Code release.

41.7±1.2#504 of 719 LLMsfrom 7 benchmark results across 2 sources

$ how it compares

The LLMs ranked either side of Granite 34B Code Base (8K), out of 828 with a score, then more from IBM.

#modelscorewhereRAMreleased
593Hermes 3 Llama 3.1 8B Nous Research42±1local~6.6GB2024-07
594Falcon2 11B (base) TII42±3local~8.2GB2024-05
595Granite 34B Code Base (8K) IBM42±1local~24GB2024-04
596speechless-starcoder2-7b uukuguy42±1local~6.1GB2024-03
597Command R (03-2024) Cohere42±1local~24GB2024-03
more from IBM
192Granite 4.2 30B IBM63±1api $0.28/M +local~19GB2026-08
239Granite 4.2 8B IBM60±1api $0.11/M +local~6.6GB2026-08
276Granite 4.2 3B IBM57±1api $0.052/M +local~3.7GB2026-08

see all LLMs · find the ones that fit your machine

$ what it's good at

coding▁▁▁▃▆██▇▆▄▃▁ #352 of 458 · 85% of its score · 6 tests

Beats LFM2.5 8B-A1B, Qwen3-VL 2B Thinking, DeepSeek R1 Distill Qwen 7B here, though they rank higher overall. Trails Phi-3-mini-128k-instruct (Apr 2024), Code Tulu 2 13B, DeepSeek LLM 67B Chat here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
HumanEvalSynthesize (HumanEvalPack, 6-language average) lab49.1% ✎▁·▂▁▂▆▅█▄▄▁▂ beats 28/35medium · avg 64%▇▇▇▇▇▇▇▇▇ 23%at its level
HumanEvalExplain (HumanEvalPack, 6-language average) lab34.1% ✎▄▇█▃▄▇▄▃▇▃·▁ beats 27/35medium · avg 49%▇▇▇▇▇▇ 16%at its level
HumanEvalFix (HumanEvalPack, 6-language average) lab29.5% ✎▁·▁▁▄██▄█▁▅▅ beats 25/35medium · avg 56%▇▇▇▇▇▇ 15%sharp · at its level
MBPP lab47.2% ✎█·▅▃▃▃▅▃▃▅·▅ beats 12/15easy · avg 70%▇▇▇▇▇ 13%at its level
CRUXEval-I lab43.3% ✎▃▅█▃▃▅·▃▃▅·▅ beats 13/15medium · avg 59%▇▇▇▇ 10%at its level
CRUXEval-O lab44.8% ✎▃·▃██▅▃·▃▃▅▃ beats 14/15medium · avg 55%▇▇▇ 8.3%blunt · at its level
other tests1 not in a task · 14% of its score
testresultvs the others in its rundifficultycountswhy
BigCodeBench (Complete)38.5% ✎▂▁▁▄▂▅▇█▆▂▂▂ beats 24/58medium · avg 50%▇▇▇▇▇▇ 14%at its level

$ where to run it

llama.cpp · recommended

  1. Install llama.cpp · Homebrew and winget installs name the commands llama-cli and llama-server instead.
    macOS / Linux
    curl -LsSf https://llama.app/install.sh | sh
    Windows
    irm https://llama.app/install.ps1 | iex

    other ways to install ↗

  2. Run Granite 34B Code Base (8K) · needs ~21.38 GB · from huggingface.co/ibm-granite
    chat in the terminal
    llama cli -hf ibm-granite/granite-34b-code-base-8k-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf ibm-granite/granite-34b-code-base-8k-GGUF:Q4_K_M

LM Studio

  1. Install LM Studio · Open the app once before using the lms command.

    download it (macOS / Windows / Linux) ↗

  2. Run Granite 34B Code Base (8K) · needs ~21.38 GB · from huggingface.co/ibm-granite

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/ibm-granite/granite-34b-code-base-8k-GGUF@Q4_K_M -y
  4. Then run lms ls to see its name, and lms chat followed by that name

Ollama

  1. Install Ollama

    download it (macOS / Windows) ↗

    Linux
    curl -fsSL https://ollama.com/install.sh | sh
  2. Run Granite 34B Code Base (8K) · needs ~21.38 GB · from huggingface.co/ibm-granite
    chat in the terminal
    ollama run hf.co/ibm-granite/granite-34b-code-base-8k-GGUF:Q4_K_M

Weights and documentation: huggingface.co/ibm-granite ↗

$ how the score was worked out · Granite Code Models: A Family… 85%, BigCodeBench paper (Zhuo et a… 14%

Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its counts column above; here it's added up by who ran the tests. How it works

sourceresultsshare of the scorelicence
Granite Code Models: A Family of Open Foundation Models for Code Intelligence — authors' own runs · lab-reported6▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 85%CC BY 4.0
BigCodeBench paper (Zhuo et al., ICLR 2025), arXiv 2406.15877v41▇▇▇▇ 14%CC BY 4.0
prior · pull toward the middle▇ 1%
weighted average= 41.7

$ improve this page

Update model informationAdd a place to run itAdd a test resultPropose a benchmarkCorrect a benchmark scoreEdit data on GitHub

Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.

data updated · sources and licences · json