Run MiniMax H4 Locally: GPU, VRAM & Hardware Checker

Yes. MiniMax H4-Base can run locally. MiniMax officially releases separate BF16 FL2VA and Ref2VA checkpoints and documents local deployment through frameworks including SGLang, vLLM, Diffusers and ComfyUI. MiniMax does not publish one universal consumer VRAM minimum for every H3 workflow. Local H3-Base is used to reproduce the base 768p generation stage, while the complete official 2K workflow also uses H3-Context-IR and H3-Regenerate-2K. See what parts of MiniMax H4 are actually open.

These numbers are related but not interchangeable. A MiniMax H4 checkpoint larger than available VRAM can sometimes run through offloading, while a checkpoint smaller than the GPU's VRAM can still exceed the final runtime memory budget once activations, VAE components and other generation data are included.

Optimized INT8 and GGUF routes can make MiniMax H4 more practical on consumer hardware, but they are not the precision of MiniMax's original released BF16 checkpoints. Always distinguish the official H3 release from optimized ecosystem variants.

This matrix is deliberately conservative. “Possible” and “recommended” are not the same thing. MiniMaxH4 only labels a hardware route as measured when a reproducible configuration exists in the current evidence set.

Do not convert one benchmark into a universal MiniMax H4 requirement. A measured memory peak only describes the specific hardware, model files, software stack, resolution, duration and optimization settings used in that run.

MiniMax does not publish one universal minimum VRAM requirement for MiniMax H4. Current consumer hardware evidence shows that an optimized H3 workflow can run on a measured 16GB RTX 5070 Ti setup, but that result depends on specific optimized model files, GPU architecture, software, operating system, offloading and storage. Treat measured configurations as evidence for specific setups rather than official minimum specifications.

Two GPUs with the same VRAM capacity can behave differently with MiniMax H4. Low-precision kernels, software support, memory bandwidth and optimization paths differ between Blackwell, Ada and Ampere generations. The strongest current 16GB measurement comes from a Blackwell RTX 5070 Ti, so that result should not be treated as proof that every 16GB NVIDIA GPU will behave identically.

There is not enough reproducible evidence to recommend 8GB VRAM as a practical MiniMax H4 target today. The current detailed low-memory evidence leaves 8GB unverified rather than demonstrating the tested H3 workflow on a physical 8GB GPU. Low-memory techniques may continue to improve, but MiniMaxH4 should not present 8GB as a verified or official H3 requirement until a reproducible end-to-end configuration exists.

Yes. MiniMax H4 has been reproducibly measured on a 16GB RTX 5070 Ti using an optimized ComfyUI workflow. In the published test, GPU memory peaked at roughly 12.2–15.3 GiB for a 30-second 640×480 job, while a 5-second 1344×768 run reached about 14.1 GiB. This proves that a specific 16GB setup can work, but it does not make 16GB an official or universal MiniMax H4 minimum.

A 24GB RTX 4090 is a strong practical candidate for optimized MiniMax H4 workflows, but MiniMax does not designate the RTX 4090 or 24GB as an official minimum. Reproducible H3 measurements already exist below a 24GB VRAM budget on another GPU architecture, so a 4090 provides considerably more memory headroom than the measured 16GB case. Actual behavior still depends on precision, RAM, workflow, resolution and offloading.

A 32GB RTX 5090 offers strong VRAM headroom for current optimized MiniMax H4 consumer workflows. Its memory capacity is substantially above the reproducibly measured 16GB configuration, but MiniMax does not publish 32GB or the RTX 5090 as an official requirement. System RAM, workflow complexity, model precision and offloading can still affect the final result even when GPU VRAM is plentiful.

MiniMax H4 does not have one official system-RAM minimum for every local workflow. In one measured 16GB-GPU configuration, application memory reached about 45.4 GiB before more aggressive fast-disk behavior was used. Offloading can shift pressure between GPU VRAM, host memory and storage, which is why system RAM should be evaluated together with the complete deployment strategy.

SSD performance can matter when a MiniMax H4 workflow relies heavily on offloading. Low-memory configurations may move model components between storage, system RAM and GPU memory instead of keeping everything resident on the GPU. The measured 16GB setup used NVMe storage and documented a fast-disk approach that shifted more pressure toward file cache and storage. This does not mean every H3 workflow requires NVMe, but storage can become part of the memory strategy.

FL2VA is the better first local validation workflow for most MiniMax H4 users. Ref2VA accepts richer multimodal reference inputs including images, video and audio, which increases workflow complexity. Validate your local H3-Base installation, memory configuration and decoding path with FL2VA before assuming a borderline machine will handle a demanding Ref2VA workflow.

MiniMax H4 INT8 refers to optimized lower-precision H3 components used to reduce deployment memory compared with the original BF16 release. Optimized INT8 and INT8-ConvRot style files are used in the broader H3 ecosystem to make consumer-GPU deployment more practical. They should not be described as the precision of MiniMax's original released H3-Base checkpoints.

GGUF is a community quantization route for running MiniMax H4 with flexible memory and offloading strategies. GGUF can be useful for memory-constrained local experiments, but it is not the format of MiniMax's original BF16 H3-Base release. Compatibility depends on the exact conversion, loader, ComfyUI implementation and GPU architecture, so do not assume that every MiniMax H4 GGUF file works with every standard workflow.

Choose the workflow with the strongest evidence for your actual hardware rather than selecting INT8 or GGUF from VRAM alone. GPU architecture, loader compatibility, system RAM, storage and operating system can matter as much as the quantization label.

The complete official MiniMax H4 2K workflow is not a fully local open-weight pipeline today. MiniMax defines the full system as H3-Context-IR → H3-Base → H3-Regenerate-2K. H3-Base can be deployed locally and is used for the base 768p generation stage. The official complete 2K workflow also depends on H3-Context-IR and H3-Regenerate-2K platform stages.

MiniMax H4 has emerging Apple-device deployment paths, but Mac hardware should not be evaluated using NVIDIA VRAM tables. Apple unified memory behaves differently from discrete CUDA GPU memory, and actual compatibility depends on the exact runtime, model format and operator support. Do not directly translate a 16GB or 24GB NVIDIA recommendation into a Mac unified-memory requirement.

AMD MiniMax H4 deployment should currently be treated as configuration-dependent rather than assigned a universal compatibility rating. The official H3 documentation focuses on inference frameworks rather than publishing a consumer AMD GPU requirements table. Until a reproducible benchmark exists for a specific AMD configuration, do not invent an RX-series VRAM recommendation.

No local GPU is required when MiniMax H4 generation runs on hosted infrastructure. Your browser supplies the supported prompt and reference inputs, while the hosted service supplies model storage, GPU memory and inference compute. A local GPU is therefore relevant to local deployment decisions, not to using the hosted MiniMax H4 generator on this page.