LocalFit

granite-4.0-micro

ibm-granite/granite-4.0-micro

Released2025-10-02Size3.4B tensors · MoERoleAssistantLicenseapache-2.0Context128K config max

Where it runs 8K context · ≥ 5 t/s floor

SetupBest planMemoryGenerate
8 GB GPU + 32 GB RAMQ8_0llama.cpp CUDAFull GPUFitsUnknown
12 GB GPU + 32 GB RAMnative BF16vLLMFull GPUFitsUnknown
16 GB GPU + 64 GB RAMnative BF16vLLMFull GPUFitsUnknown
24 GB GPU + 64 GB RAMnative BF16vLLMFull GPUFitsUnknown
32 GB GPU + 128 GB RAMnative BF16vLLMFull GPUFitsUnknown
2× 24 GB GPUs + 128 GB RAMnative BF16vLLMFull GPUFitsUnknown
96 GB workstation GPU + 256 GB RAMnative BF16vLLMFull GPUFitsUnknown
Mac, 24 GBQ8_0llama.cpp MetalUnified memoryFitsUnknown
Mac, 48 GBQ8_0llama.cpp MetalUnified memoryFitsUnknown
Mac, 128 GBQ8_0llama.cpp MetalUnified memoryFitsUnknown
Mac, 512 GBQ8_0llama.cpp MetalUnified memoryFitsUnknown
DGX Spark 128 GBnative BF16vLLMUnified memoryFitsUnknown
Ryzen AI Max+ 395, 128 GBnative BF16vLLMUnified memoryFitsUnknown
CPU only, 64 GB DDR5-5600 dual channelQ8_0llama.cpp CPUCPU onlyFitsUnknown

Same plan engine as the planner, with its default reserves and RAM. How plans are chosen.

Files exact Hugging Face bytes

ArtifactSizeFilesRevisionSource
Q8_0
ibm-granite/granite-4.0-micro-GGUF
3.37 GiB1ec48475f0cHF files
Q5_K_M
ibm-granite/granite-4.0-micro-GGUF
2.27 GiB1ec48475f0cHF files
Q4_K_M
ibm-granite/granite-4.0-micro-GGUF
1.96 GiB1ec48475f0cHF files
Native safetensors BF16
ibm-granite/granite-4.0-micro
6.34 GiB256111ae135HF files

Not found in the representative quant search (not proven unavailable): GGUF FP16, MLX 8-bit, MLX 4-bit, EXL3, AWQ, GPTQ. Complete selected file sets; projectors, MTP/draft and lookup files are listed separately and budgeted only by plans that use them. File bytes are not allocated memory.

Quality

AA evaluated modeIndexLiveCodeBenchTerminal-Bench 2.1GPQAHLESource
Granite 4.0 Micro AA estimate5.17.0%—33.6%5.0%source

Arena text rating

Unknown

Publisher-reported benchmarks

None extracted from the model card.

AA and Arena describe the hosted model in the stated mode, not each quantized file; scores are never inherited by base, pruned, abliterated or quantized variants and never averaged together.

How to run it

No model-specific recipe in the research. The planner binds the backend’s documented command template (llama.cpp, Ollama, LM Studio, MLX, vLLM) to your plan: open a plan’s details.

Recipes are source examples (settings are the publisher’s or author’s, not measured optima) and were not executed by LocalFit. A downloadable file does not prove your backend build supports the architecture.

Reported speeds 0

No reported measurement yet. Submit one.