Z.aiGLM 5Glm5NextForConditionalGeneration

GLM 5.3 Flash BF16 — Hardware Requirements & GPU Compatibility

VisionFunctions

GLM 5.3 Flash BF16 is a 321.3B-parameter open language model from Z.ai in the GLM 5 family. It supports a context window of up to 1,048,576 tokens. At Q4_K_M it needs about 194.60 GB of VRAM — see which GPUs and Macs can run it below.

321.3B params · MoE18B active1049K contextMITAug 2026120.3K downloads81 likes31.3K quant downloads

Your hardware

Verdicts below are for the reference RTX 4090. Set yours to personalise the page.

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GLM 5.3 Flash BF16 runs on a 256 GB+ Mac or a datacenter GPU

Best local pick → Mac Studio (M3 Ultra, 256GB) · Q4_K_M · 205.2 GB · ~35 tok/s

Macs from 192 GB (Q3_K_M · ~40 tok/s)

MIT

Not set up for it?

Reference GPU · RTX 4090 24 GB

Estimates, ±20% · how we estimate

Run GLM 5.3 Flash BF16

One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.

Recommended

Ollama

one command · needs Ollama installed

$ollama run hf.co/vcruz305/GLM-5.3-Flash-GGUF:Q4_K_M

Q4_K_M runs on one Instinct MI325X 256 GB · 16K context

Download: GLM-5.3-Flash-Q4_K_M.gguf192.88 GBvcruz305

install, OS, other quants

Ways to runOther ways

each opens step-by-step

Run in cloud

Runs on B300 (288 GB) (82 GB headroom) · Q4_K_M

Generation speed: ~217 tok/s; generation speed
Cost per 1M output tokens: $8.89; per 1M output tokens

Deployon RunPodBest valuefrom $6.94/hr

$5+ bonus for new users

Compare GPUs →

How Much VRAM Does GLM 5.3 Flash BF16 Need?

Select a quantization level; the listed hardware adheres to it.

Quantization	VRAM	+32K	+128K	File	Quality	Runs on
	138.4 GB	161 GB	233.5 GB	116.7 GB	2-bit · K-quant — 2-bit quantization with K-quant improvements	Runs on B200 192 GB
	158.4 GB	181.1 GB	253.6 GB	~156.6 GB	3-bit medium — 3-bit medium quantization	Runs on B200 192 GB
	194.6 GB	217.3 GB	289.7 GB	192.9 GB	4-bit medium — 4-bit medium quantization — most popular sweet spot	Runs on Instinct MI325X 256 GB
	230.8 GB	253.4 GB	325.9 GB	~228.9 GB	5-bit medium — 5-bit medium quantization — good quality/size tradeoff	Runs on Instinct MI325X 256 GB
	266.9 GB	289.6 GB	362 GB	~265.1 GB	6-bit, very good — 6-bit quantization, very good quality	Runs on Instinct MI350X 288 GB
	323.1 GB	345.8 GB	418.3 GB	~321.3 GB	8-bit, near-lossless — 8-bit quantization, near-lossless	Runs on 5× H100 SXM 80 GB
	644.5 GB	667.1 GB	739.6 GB	~642.7 GB	16-bit · bfloat — Brain floating point 16 — preferred for training	Runs on 6× H200 SXM 141 GB

est.calculated, no published file yet

Selected Q4_K_M

4-bit medium quantization

205.2GBat 16K context

Weights: 192.8 GB
KV cache: 12.1 GB
Overhead: 0.3 GB

Server-class: ~205.2 GB at 16K context · one Instinct MI325X 256 GB

Or .

Which Hardware Can Run GLM 5.3 Flash BF16?

Q4_K_M · 194.6 GB

GLM 5.3 Flash BF16 (Q4_K_M) requires 194.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 253+ GB is recommended. Using the full 1049K context window can add up to 771.6 GB, bringing total usage to 966.2 GB. No consumer GPU has enough memory. 2 devices with unified memory can run GLM 5.3 Flash BF16, including Mac Studio (M3 Ultra, 512GB), Mac Studio (M3 Ultra, 256GB).

By VRAM tier · typical GPUs and Macs at Q4_K_M

Consumer GPUs (up to 32 GB) and Macs up to 128 GB: too small for this model (needs ≥ 148.9 GB)8 GB tier →12 GB tier →16 GB tier →24 GB tier →Macs →

Alternatives to GLM 5.3 Flash BF16

GLM 5 family · 7 models →

Similar models

6

Model	Score	VRAM	tok/s	Why it is similar
IQuest Q1IQuestLab	—	195.4 GB	—needs > 24 GB	same size
MiMo V2.6 Flash MOPDXiaomi	—	186.5 GB	—needs > 24 GB	same size
DeepSeek V4 Flash Vision ExpDeepSeek	—	183.3 GB	—needs > 24 GB	same size
Hy3Tencent	—	182.3 GB	—needs > 24 GB	same size
Step3StepFun	—	219.1 GB	—needs > 24 GB	same size
Motif 3Motif-Technologies	—	632.8 GB	—needs > 24 GB	same size

Specifications

Publisher: Z.ai
Family: GLM 5
Parameters: 321.3B
Active parameters: 18.3B per token
Architecture: Glm5NextForConditionalGeneration
Context Length: 1,048,576 tokens
Vocabulary Size: 154,880
Release Date: 2026-08-25
License: MIT

Resources

Frequently Asked Questions

How much VRAM does GLM 5.3 Flash BF16 need?

GLM 5.3 Flash BF16 requires 194.6 GB of VRAM at Q4_K_M, or 644.5 GB at BF16. Full 1049K context adds up to 771.6 GB (966.2 GB total).

VRAM = Weights + KV Cache + Overhead

Weights = 321.3B × 4.8 bits ÷ 8 = 192.8 GB

KV Cache + Overhead ≈ 1.8 GB (at 2K context + ~0.3 GB framework)

Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.

KV Cache + Overhead ≈ 773.4 GB (at full 1049K context)

VRAM usage by quantization

Q4_K_M

194.6 GB

Q4_K_M + full context

966.2 GB

Learn more about VRAM estimation →

Can NVIDIA GeForce RTX 5090 run GLM 5.3 Flash BF16?

No — GLM 5.3 Flash BF16 requires at least 138.4 GB at Q2_K, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.

What's the best quantization for GLM 5.3 Flash BF16?

For GLM 5.3 Flash BF16, Q4_K_M (194.6 GB) offers the best balance of quality and VRAM usage. Q5_K_M (230.8 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 138.4 GB.

VRAM requirement by quantization

Q2_K

138.4 GB~75%

Q4_K_M ★

194.6 GB~89%

Q5_K_M

230.8 GB~92%

Q6_K

266.9 GB~95%

Q8_0

323.1 GB~99%

BF16

644.5 GB~100%

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run GLM 5.3 Flash BF16 on a Mac?

Yes — Mac Pro M2 Ultra (192 GB) and 3 other Macs can run GLM 5.3 Flash BF16, the smallest at Q3_K_M. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 194.6 GB of usable unified memory (RAM minus macOS overhead), from Mac Studio (M3 Ultra, 256GB).

Can I run GLM 5.3 Flash BF16 locally?

Yes — GLM 5.3 Flash BF16 can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is GLM 5.3 Flash BF16?

At Q4_K_M, GLM 5.3 Flash BF16 can reach ~88 tok/s on AMD Instinct MI350X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.

tok/s = 1000 ÷ (active GB ÷ (bandwidth GB/s × efficiency) × 1000 + layers × routing ms)

Mixture-of-Experts: just the active experts get read for each token, along with a fixed routing cost per layer.

Example: NVIDIA B300 → 11.1 GB active ÷ (8000 × 0.65) = 2.13 ms, plus 45 layers × 0.055 ms = 2.48 ms, so 1000 ÷ 4.61 ms = ~217 tok/s

Estimated speed at Q4_K_M (194.6 GB)

NVIDIA B300

~217 tok/s

AMD Instinct MI350X

~88 tok/s

AMD Instinct MI355X

~88 tok/s

Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.

Learn more about tok/s estimation →

What's the download size of GLM 5.3 Flash BF16?

At Q4_K_M, the download is about 192.79 GB. The full-precision BF16 version is 642.65 GB. The smallest option (Q2_K) is 136.56 GB.

Which GPUs can run GLM 5.3 Flash BF16?

No single consumer GPU has enough VRAM to run GLM 5.3 Flash BF16 at Q4_K_M (194.6 GB). Multi-GPU or professional hardware is required.

Which devices can run GLM 5.3 Flash BF16?

2 devices with unified memory can run GLM 5.3 Flash BF16 at Q4_K_M (194.6 GB), including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.

Run GLM 5.3 Flash BF16

205.2 GB VRAM at 16K context · runs on one Instinct MI325X 256 GB

Works on macOS, Windows and Linux.

Install Ollama (Linux)
$curl -fsSL https://ollama.com/install.sh | sh
Install Ollama (macOS / Windows)
Download and run the app; it installs the ollama command.
Download Ollama ↗
Run Q4_K_M
$ollama run hf.co/vcruz305/GLM-5.3-Flash-GGUF:Q4_K_M
Raise the context window
Type it inside the chat. For the API server instead, start it with OLLAMA_CONTEXT_LENGTH=16384 ollama serve.
$/set parameter num_ctx 16384
A larger context needs more memory than the weights alone.
Use the API
Ollama serves its API on http://localhost:11434.

Works on macOS, Windows and Linux.

Install LM Studio
Desktop app for macOS (Apple silicon), Windows and Linux.
lmstudio.ai ↗
Download the model
In the app press Ctrl/Cmd + Shift + M and paste https://huggingface.co/vcruz305/GLM-5.3-Flash-GGUF, then pick Q4_K_M. Or use the CLI (run LM Studio once first):
$lms get https://huggingface.co/vcruz305/GLM-5.3-Flash-GGUF@Q4_K_M
Open on Hugging Face ↗
Load and chat
Open the model loader, select the model and set Context Length to 16384. Leave GPU offload at maximum if it fits.
Start the local server (optional)
$lms server start

Works on macOS, Windows and Linux.

Install llama.cpp (macOS)
MacPorts, Nix and conda-forge also work.
$brew install llama.cpp
Install options ↗
Install llama.cpp (Windows)
$winget install llama.cpp
Install llama.cpp (Linux)
Download the prebuilt llama-*-bin-ubuntu-* archive for your GPU backend (CPU, Vulkan) from the releases page, unpack it and run the binaries from that folder. Homebrew (brew install llama.cpp) and conda-forge also work on Linux.
llama.cpp releases ↗
Start the server
Downloads the model on first run. The chat UI and an OpenAI-compatible API are at http://localhost:8080.
$llama-server -hf vcruz305/GLM-5.3-Flash-GGUF:Q4_K_M -c 16384 -ngl 99 --port 8080

Works on macOS, Windows and Linux.

Option A: Docker Model Runner
Docker Desktop: Settings > AI > Enable Docker Model Runner. Docker Engine on Linux: install the docker-model-plugin package.
$docker model run hf.co/vcruz305/GLM-5.3-Flash-GGUF:Q4_K_M
Model Runner docs ↗
The quant tag after the repo is how Hugging Face cards write it; if your Docker version rejects it, drop the tag and pick the quant by repo.
Option B: llama.cpp server image (NVIDIA)
Needs the NVIDIA Container Toolkit. Models are cached in the llama-cache volume. Open http://localhost:8080.
$docker run --gpus all -p 8080:8080 -v llama-cache:/root/.cache ghcr.io/ggml-org/llama.cpp:server-cuda -hf vcruz305/GLM-5.3-Flash-GGUF:Q4_K_M -c 16384 -ngl 99 --host 0.0.0.0 --port 8080
Image tags (cuda13, vulkan, rocm) ↗
Cache path inside the image is not in the docs; remove the -v flag if the volume complains.

Linux only.

Install vLLM
Linux only; on Windows run it inside WSL2. NVIDIA GPU shown; AMD ROCm, Intel and CPU builds have their own install pages.
$uv venv --python 3.12 --seed
$source .venv/bin/activate
$uv pip install vllm --torch-backend=auto
vLLM installation ↗
Serve the model
OpenAI-compatible API at http://localhost:8000/v1. Splits the model over 8 GPUs of one server (tensor parallel).
$vllm serve zai-org/GLM-5.3-Flash-BF16 --max-model-len 16384 --tensor-parallel-size 8
Runs the original weights, not the Q4_K_M GGUF; needs ~707 GB of GPU memory.

Works on macOS, Windows and Linux.

Install Jan
jan.ai ↗
Find the model
Open the Hub tab, paste this model ID in Search, and choose Q4_K_M if offered.
$vcruz305/GLM-5.3-Flash-GGUF
Use the local API (optional)
Settings > Local API Server > Start Server. It listens on http://127.0.0.1:1337.

Rent GPUs hourly to run Q4_K_M right there. Pricing comes from partner marketplaces and varies.

Available GPU rentals for Q4_K_M, prioritized by value
GPU	Speed	$/1M tokens	Rent
B300 (288 GB)CheapestCheapest82 GB free	~217 tok/s	$8.89	Deploy on RunPodfrom $6.94/hr (opens in new tab)	288 GB · 82 GB free · ~217 tok/s · $8.89 / 1M tokens

Compare GPUs →