LLMs ·

Granite Code 34B Instruct

Parameters
34B
Architecture
Dense
Max context
8K tokens
License
apache-2.0
Released
2024-04

How to run it

Quickest · Ollama

One command downloads a ready-to-run version and starts a chat.

Get Ollama at ollama.com/download (opens in a new tab) · model page (opens in a new tab)

ollama run granite-code:34b

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

huggingface.co/ibm-granite/granite-34b-code-instruct-8k-GGUF (opens in a new tab)

ollama run hf.co/ibm-granite/granite-34b-code-instruct-8k-GGUF

Original weights · Hugging Face

Full-precision files from IBM, for fine-tuning, vLLM, Transformers.

huggingface.co/ibm-granite/granite-34b-code-instruct-8k (opens in a new tab)

No sign-in needed.

Links checked 2 days ago against Hugging Face, Ollama and GitHub.

Apps that run Granite Code 34B Instruct

Our pick for each kind of computer, assuming the model fits in its memory. Pick your own GPU or device in the Model Finder for an exact answer.

Mac (Apple silicon)

Ollama

One command with Ollama’s own build of this model.

Also good:

Windows PC, NVIDIA GPU

Ollama

One command with Ollama’s own build of this model.

Also good:

Windows PC, AMD Radeon

Lemonade

AMD’s own app: llama.cpp tuned for Radeon on Vulkan and ROCm.

Also good:

Linux, NVIDIA GPU

Ollama

One command with Ollama’s own build of this model.

Also good:

Linux server, 80 GB+ GPUs

vLLM

The full model fits in GPU memory at 8-bit, so it can batch many requests: best for serving a team or an app.

Also good:

App Type Opens it as Systems Engine Start
LM Studio Desktop app GGUF macOSWindowsLinux llama.cpp, MLX Open in LM Studio
Ollama CLI + API Ollama library macOSWindowsLinux llama.cpp, MLX 
ollama run granite-code:34b
llama.cpp CLI + API GGUF macOSWindowsLinux llama.cpp 
llama-server -hf ibm-granite/granite-34b-code-instruct-8k-GGUF:Q4_K_M
Jan Desktop app GGUF macOSWindowsLinux llama.cpp, MLX Open in Jan
vLLM Server Original weights WSLWindowsLinux vLLM 
vllm serve ibm-granite/granite-34b-code-instruct-8k
SGLang Server Original weights WSLWindowsLinux SGLang 
python3 -m sglang.launch_server --model-path ibm-granite/granite-34b-code-instruct-8k
Docker Model Runner CLI + API GGUF macOSWindowsLinux llama.cpp, vLLM 
docker model run hf.co/ibm-granite/granite-34b-code-instruct-8k-GGUF:Q4_K_M
Lemonade Desktop app GGUF macOSWindowsLinux llama.cpp, ONNX Runtime GenAI 
lemonade pull ibm-granite/granite-34b-code-instruct-8k-GGUF:Q4_K_M
Unsloth Desktop Desktop app GGUF macOSWindowsLinux llama.cpp, MLX, stable-diffusion.cpp Open in Unsloth Desktop
Atomic Chat Desktop app GGUF macOSWindowsLinux llama.cpp, MLX-VLM Open in Atomic Chat
Pi Coding agent GGUF macOSWindowsLinux uses llama.cpp or MLX LM Point it at a llama.cpp or MLX LM server
Hermes Agent Assistant GGUF macOSWSLWindowsLinux uses llama.cpp or MLX LM Point it at a llama.cpp or MLX LM server
OpenClaw Assistant GGUF macOSWSLWindowsLinux uses llama.cpp or MLX LM Point it at a llama.cpp or MLX LM server
OpenCode Coding agent GGUF macOSWindowsLinux uses llama.cpp, Ollama or LM Studio Point it at a llama.cpp or MLX LM server
Codex CLI Coding agent GGUF macOSWSLWindowsLinux uses Ollama or any OpenAI-compatible server Point it at a llama.cpp or MLX LM server
OpenHands Coding agent GGUF macOSWSLWindowsLinux uses llama.cpp, Ollama or LM Studio Point it at a llama.cpp or MLX LM server
Zed Coding agent GGUF macOSWindowsLinux uses llama.cpp, Ollama or LM Studio Point it at a llama.cpp or MLX LM server
Cline Coding agent GGUF macOSWindowsLinux uses llama.cpp, Ollama or LM Studio Point it at a llama.cpp or MLX LM server
Open Interpreter Coding agent GGUF macOSWindowsLinux uses llama.cpp, Ollama or LM Studio Point it at a llama.cpp or MLX LM server
Goose Coding agent GGUF macOSWindowsLinux uses llama.cpp, Ollama or LM Studio Point it at a llama.cpp or MLX LM server
Aider Coding agent GGUF macOSWindowsLinux uses llama.cpp, Ollama or LM Studio Point it at a llama.cpp or MLX LM server
Crush Coding agent GGUF macOSWindowsLinux uses llama.cpp, Ollama or LM Studio Point it at a llama.cpp or MLX LM server
Qwen Code Coding agent GGUF macOSWindowsLinux uses llama.cpp, Ollama or LM Studio Point it at a llama.cpp or MLX LM server
Kilo Code Coding agent GGUF macOSWindowsLinux uses llama.cpp, Ollama or LM Studio Point it at a llama.cpp or MLX LM server
Open WebUI Assistant GGUF macOSWindowsLinux uses llama.cpp, Ollama or LM Studio Point it at a llama.cpp or MLX LM server
Khoj Assistant GGUF macOSWindowsLinux uses llama.cpp, Ollama or LM Studio Point it at a llama.cpp or MLX LM server
Letta Assistant GGUF macOSWindowsLinux uses llama.cpp, Ollama or LM Studio Point it at a llama.cpp or MLX LM server
GPT4All Desktop app GGUF macOSWindowsLinux llama.cpp Get GPT4All
KoboldCpp Desktop app GGUF macOSWindowsLinux llama.cpp Get KoboldCpp
Text Generation WebUI Desktop app GGUF macOSWindowsLinux llama.cpp, Transformers, ExLlamaV3 Get Text Generation WebUI
llamafile CLI + API GGUF macOSWindowsLinux llama.cpp Get llamafile
Msty Studio Desktop app GGUF macOSWindowsLinux Ollama, llama.cpp, MLX Open in Msty Studio
Llama (macOS) Desktop app GGUF macOS llama.cpp Open in Llama (macOS)
RecurseChat Desktop app GGUF macOS llama.cpp Open in RecurseChat
node-llama-cpp Library GGUF macOSWindowsLinux llama.cpp Get node-llama-cpp
Continue Coding agent GGUF macOSWindowsLinux uses Ollama, LM Studio or llama.cpp Point it at a llama.cpp or MLX LM server
SillyTavern Assistant GGUF macOSWindowsLinux uses Ollama, LM Studio or llama.cpp Point it at a llama.cpp or MLX LM server
LibreChat Assistant GGUF macOSWindowsLinux uses Ollama, LM Studio or llama.cpp Point it at a llama.cpp or MLX LM server
Cherry Studio Assistant GGUF macOSWindowsLinux uses Ollama, LM Studio or llama.cpp Point it at a llama.cpp or MLX LM server
Chatbox Assistant GGUF macOSWindowsLinux uses Ollama, LM Studio or llama.cpp Point it at a llama.cpp or MLX LM server
PrivateGPT Assistant GGUF macOSWindowsLinux uses Ollama, LM Studio or llama.cpp Point it at a llama.cpp or MLX LM server
PocketPal AI Mobile app GGUF iOSAndroid llama.cpp App Store Google Play
ChatterUI Mobile app GGUF Android llama.cpp Download the APK
LLM Farm Mobile app GGUF iOS llama.cpp Not on the App Store or TestFlight for now (checked 2026-11-09).

Which apps can open a model depends on the files published for it: GGUF for llama.cpp-based apps, MLX for Apple’s engine, the original weights for vLLM and SGLang. Compare every app.

Memory needed

Weights plus KV cache and runtime overhead. Leave a little extra free for the operating system and other apps. Try another context length.

KV cache: multi-query attention: one shared KV head.

Quantization Quality Weights Total at 8K context Download
Q2_K Low 13.5 GB 14.5 GB
Q3_K_M Low 16.5 GB 17.5 GB
Q4_K_M Good 20.6 GB 21.5 GB GGUF (opens in a new tab)
Q5_K_M High 23.9 GB 24.9 GB
Q6_K High 27.7 GB 28.6 GB
Q8_0 Very high 35.7 GB 36.7 GB
F16 Full 67.4 GB 68.4 GB Original weights (opens in a new tab)

Best hardware for Granite Code 34B Instruct

The least expensive GPU in our catalog that runs it without offloading is the AMD Radeon RX 7600 XT (launch price $329) at Q2_K.

Quality Hardware Quant Memory Speed Runs
38Basic quality (D) · 38 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5090 Founders EditionNVIDIA GeForce RTX 5090 Q5_K_M 26.5 GB / 32.0 GB 53.9 tok/s Runs great
38Basic quality (D) · 38 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 70/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4090 Founders EditionNVIDIA GeForce RTX 4090 Q4_K_M 22.7 GB / 24.0 GB 36.7 tok/s Runs welltight fit
33Basic quality (D) · 33 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 70/100 (quality, fit, speed and memory use) sets the ranking. ASUS ROG Strix GeForce RTX 3090 OC Edition 24GB GDDR6XNVIDIA GeForce RTX 3090 Q3_K_M 18.7 GB / 24.0 GB 44.1 tok/s Runs great
33Basic quality (D) · 33 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 69/100 (quality, fit, speed and memory use) sets the ranking. Sapphire NITRO+ AMD Radeon RX 7900 XTX Vapor-X 24GBAMD Radeon RX 7900 XTX Q3_K_M 18.7 GB / 24.0 GB 38.9 tok/s Runs great
28Basic quality (D) · 28 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 66/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4070 Ti SUPERNVIDIA GeForce RTX 4070 Ti SUPER Q2_K 15.3 GB / 16.0 GB 40.2 tok/s Runs welltight fit
28Basic quality (D) · 28 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 66/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4080 SUPERNVIDIA GeForce RTX 4080 SUPER Q2_K 15.3 GB / 16.0 GB 44.0 tok/s Runs welltight fit
28Basic quality (D) · 28 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 66/100 (quality, fit, speed and memory use) sets the ranking. ASUS TUF Gaming GeForce RTX 5070 Ti 16GB GDDR7 OC EditionNVIDIA GeForce RTX 5070 Ti Q2_K 15.3 GB / 16.0 GB 53.6 tok/s Runs welltight fit
28Basic quality (D) · 28 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 66/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5080 Founders EditionNVIDIA GeForce RTX 5080 Q2_K 15.3 GB / 16.0 GB 57.4 tok/s Runs welltight fit
39Basic quality (D) · 39 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 65/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA RTX PRO 6000 Blackwell Workstation EditionNVIDIA RTX PRO 6000 Blackwell Q6_K 33.4 GB / 96.0 GB 45.3 tok/s Runs great
28Basic quality (D) · 28 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 65/100 (quality, fit, speed and memory use) sets the ranking. Sapphire NITRO+ AMD Radeon RX 9070 XT OCAMD Radeon RX 9070 XT Q2_K 15.3 GB / 16.0 GB 32.9 tok/s Runs welltight fit

Check it on your own hardware

Run it on a cluster

Devices that can’t run Granite Code 34B Instruct alone can run it together. These example clusters do:

Try it with your own devices

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.

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