Z.aiGLM 5Glm5NextForConditionalGeneration

GLM 5.3 Flash — Hardware Requirements & GPU Compatibility

VisionFunctions

GLM 5.3 Flash is a vision-language model from Z.ai in the GLM 5 family, with roughly 321 billion parameters. The Flash designation points to a variant tuned for faster inference within the family, and the model accepts image input alongside text, making it usable for tasks that combine visual and textual understanding. It supports a 1M token context window, useful for long documents or multi-image conversations, and ships under the MIT license. At its parameter scale, local deployment needs multi-GPU or server-class hardware; most users will reach it through a hosted endpoint rather than running it on a single machine.

321.3B params · MoE18B active1049K contextMITAug 20266.5M downloads2.9K likes1.0M quant downloads

Your hardware

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

Detection reads your browser's GPU name with no data sent anywhere.

GLM 5.3 Flash 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 128 GB (IQ2_S · ~52 tok/s)

Score 124.2#9 of 100 open#4 of 84 that fit ≤256 GBMIT

Not set up for it?

Reference GPU · RTX 4090 24 GB

Estimates, ±20% · how we estimate

llmrun Composite Score →

124.2

90% range 122.4–125.9
#9 of 100 open models
#4 of 84 that fit ≤256 GB

Benchmarks · tier chart ↓

Among 100 open modelsHow the Composite Score is built

Coding117.490% range 113–122
Agents127.490% range 124–131
Math121.490% range 118–125
Science123.790% range 112–135
Reasoning127.490% range 124–130

Run GLM 5.3 Flash

One recommended way. Every other method, OS and quant is in the step-by-step guide.

Other ways

each opens step-by-step

Run in cloud

Fits 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 optionfrom $6.94/hr

Compare GPUs →

Run GLM 5.3 Flash

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/batiai/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/batiai/GLM-5.3-Flash-GGUF, then pick Q4_K_M. Or use the CLI (run LM Studio once first):
$lms get https://huggingface.co/batiai/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 batiai/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/batiai/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 batiai/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 --max-model-len 16384 --tensor-parallel-size 8
Runs the original weights, not the Q4_K_M GGUF; needs ~707 GB of GPU memory.

macOS only.

MLX supports only macOS, so follow these steps for that setup.

Install mlx-lm
$pip install mlx-lm
Chat in the terminal
$mlx_lm.chat --model orcarouter/GLM-5.3-Flash-MLX
Downloads the model on first run. The MLX repo has its own quantisation; it may not match the quant selected above.
Or start a local server
Listens on http://localhost:8080.
$mlx_lm.server --model orcarouter/GLM-5.3-Flash-MLX

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.
$batiai/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 →

How Much VRAM Does GLM 5.3 Flash Need?

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

VRAM	+32K	+128K	File	Quality	Runs on
90.2 GB	112.8 GB	185.3 GB	101.8 GB	2-bit · i-quant extreme compression — Importance-weighted 2-bit, extreme compression — significant quality loss	Runs on Instinct MI250X 128 GB
102.2 GB	124.9 GB	197.4 GB	113.6 GB	2-bit · i-quant small — Importance-weighted 2-bit, small	Runs on Instinct MI250X 128 GB
126.3 GB	149 GB	221.5 GB	120.4 GB	3-bit · i-quant — Importance-weighted 3-bit	Runs on H200 NVL 141 GB
138.4 GB	161 GB	233.5 GB	114.2 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	149.1 GB	3-bit medium — 3-bit medium quantization	Runs on B200 192 GB
174.5 GB	197.2 GB	269.7 GB	156.8 GB	4-bit · i-quant compact — Importance-weighted 4-bit, compact	Runs on B200 192 GB
194.6 GB	217.3 GB	289.7 GB	188.7 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	341 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	641.6 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 rent B300 from $6.94/hr.

Which Hardware Can Run GLM 5.3 Flash?

Q4_K_M · 194.6 GB

GLM 5.3 Flash (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, 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 96 GB: too small for this model (needs ≥ 100.7 GB)8 GB tier →12 GB tier →16 GB tier →24 GB tier →Macs →

How good is GLM 5.3 Flash?

The llmrun Score merges 24 public benchmark boards into a single figure, where 100 represents the average for open models.

Score vs VRAM in the ≤256 GB tier

GLM 5.3 Flash
Other models
Best Score for its VRAM
x: VRAM (GB, log scale) · y: llmrun Score

#4 of 84 that fit ≤256 GB

Each dot is a model at the best quant that fits ≤256 GB. The line joins the models with the best Score for their VRAM; a dot below it is beaten by a model that needs no more memory.

Full ≤256 GB ranking →

Benchmarks

GPQA Diamond
Epoch AI · Aug 2026Independent
90.1%
#9/98 open
Independent
LMArena Text
LMArena · Oct 2026Arena
1473.8
#6/187 open
Arena
LiveBench Coding
LiveBench · Jun 2026Independent
79.0
#5/19 open
Independent
LiveBench Reasoning
LiveBench · Jun 2026Independent
77.6
#15/19 open
Independent
AIME 2024/2025
Epoch AI · Aug 2026Independent
93.9%
#9/86 open
Independent
FrontierMath
Epoch AI · Aug 2026Independent
55.8%
#9/17 open
Independent

All 30 GLM 5.3 Flash benchmarks

In the ≤256 GB class

84 models fit ≤256 GB

Same Score, only models that fit ≤256 GB at their best quant: the ranking a ≤256 GB owner actually chooses from.

#1
MiMo V2.6 Flash RLXiaomi
131.9
~83 tok/s · 186.5 GB
#2
DeepSeek V4.1 FlashDeepSeek
129.5
~45 tok/s · 210.4 GB
#3
DeepSeek V4 Flash 0731DeepSeek
127.6
~76 tok/s · 183.0 GB
#4
GLM 5.3 FlashZ.ai
124.2
~83 tok/s · 205.2 GB
#5
GLM 5.2Z.ai
122.9
~52 tok/s · 227.8 GB
#6
Qwen3.8 27BAlibaba
122.1
~207 tok/s · 17.9 GB

Full ≤256 GB ranking →AMD Instinct MI325X page →

Alternatives to GLM 5.3 Flash

GLM 5 family · 6 models →

Official models with an llmrun Score, each answering one question; sizes are each model's default quant (usually Q4_K_M) with a 16K-token context.

Best alternative at this size
MiMo V2.6 Flash RL
Xiaomi
- 7.7 higher Score
- 18.7 GB less VRAM
Score 131.9 · 186.5 GB
Compare with GLM 5.3 Flash Open MiMo V2.6 Flash RL
Fits in less VRAM
Inkling Small
Thinking Machines
- 4.6 lower Score
- 42.5 GB less VRAM
Score 119.6 · 162.7 GB
Compare with GLM 5.3 Flash Open Inkling Small
Other vendor
DeepSeek V4 Flash 0731
DeepSeek
- 3.4 higher Score
- 22.2 GB less VRAM
Score 127.6 · 183 GB
Compare with GLM 5.3 Flash Open DeepSeek V4 Flash 0731

Similar models

Model	Score	VRAM	tok/s	Why it is similar
Qwen3.8 27BAlibaba	122.1	17.9 GB	~38	similar score
Qwen3.5 397B A17BAlibaba	113.0	242.6 GB	—needs > 24 GB	similar score
MiniMax M3MiniMax	112.5	258 GB	—needs > 24 GB	similar score
MiniMax M2.5MiniMax	111.8	139.6 GB	—needs > 24 GB	similar score
Qwen3.6 27BAlibaba	111.2	17.9 GB	~38	similar score
NVIDIA Nemotron 3 Ultra 550B A55B BF16NVIDIA	109.9	378.4 GB	—needs > 24 GB	similar score
DeepSeek V4 FlashDeepSeek	109.3	175.1 GB	—needs > 24 GB	similar score
GLM 4.7Z.ai	109.1	217.9 GB	—needs > 24 GB	same size
MiMo V2.5Xiaomi	108.3	187.6 GB	—needs > 24 GB	same size
GLM 4.6Z.ai	103.1	216.9 GB	—needs > 24 GB	same size

Fine-tunes & variants(3)community builds of GLM 5.3 Flash

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 need?

GLM 5.3 Flash 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?

No — GLM 5.3 Flash requires at least 90.2 GB at IQ2_XXS, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.

What's the best quantization for GLM 5.3 Flash?

For GLM 5.3 Flash, 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 IQ2_XXS at 90.2 GB.

VRAM requirement by quantization

IQ2_XXS

90.2 GB~53%

Q2_K

138.4 GB~75%

IQ4_XS

174.5 GB~87%

Q4_K_M ★

194.6 GB~89%

Q6_K

266.9 GB~95%

BF16

644.5 GB~100%

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run GLM 5.3 Flash on a Mac?

Yes — Mac Pro M2 Ultra (128 GB) and 14 other Macs can run GLM 5.3 Flash, the smallest at IQ2_S. 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 locally?

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

How fast is GLM 5.3 Flash?

At Q4_K_M, GLM 5.3 Flash 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?

At Q4_K_M, the download is about 192.79 GB. The full-precision BF16 version is 642.65 GB. The smallest option (IQ2_XXS) is 88.36 GB.

Which GPUs can run GLM 5.3 Flash?

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

Which devices can run GLM 5.3 Flash?

2 devices with unified memory can run GLM 5.3 Flash 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.

GLM 5.3 Flash — Hardware Requirements & GPU Compatibility

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

llmrun Composite Score →

Run GLM 5.3 Flash

Recommended

Other ways

Run in cloud

How Much VRAM Does GLM 5.3 Flash Need?

Selected Q4_K_M

Which Hardware Can Run GLM 5.3 Flash?

How good is GLM 5.3 Flash?

Score vs VRAM in the ≤256 GB tier

Benchmarks

In the ≤256 GB class

Alternatives to GLM 5.3 Flash

MiMo V2.6 Flash RL

Inkling Small

DeepSeek V4 Flash 0731

Similar models

Specifications

Resources

Frequently Asked Questions