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.
GLM 5.3 Flash runs on a 256 GB+ Mac or a datacenter GPU
Best local pick → Mac Studio M5 Ultra (256 GB) · Q4_K_M · 205.2 GB · ~46 tok/s
Macs from 128 GB (IQ2_S · ~52 tok/s)
Score 124#13 of 107 open#7 of 90 that fit ≤256 GBMIT
Not set up for it?
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
llmrun Composite Score →
124.0
- 90% range 122.2–125.9
- #13 of 107 open models
- #7 of 90 that fit ≤256 GB
Run GLM 5.3 Flash
Best fit for your NVIDIA DGX Station (GB300). 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 for your NVIDIA DGX Station (GB300)
ollama run hf.co/vultr/GLM-5.3-Flash-GGUF:Q4_K_MQ4_K_M fits fully · 46.8 GB free · 16K context
Download: GLM-5.3-Flash-Q4_K_M-00001-of-00002.gguf208.16 GB + 1 partvultr
Ways to runOther ways
each opens step-by-stepRun 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
How Much VRAM Does GLM 5.3 Flash Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On your device | |
|---|---|---|---|---|
| 90.2 GB | 112.8 GB | 2-bit · i-quant extreme compression — Importance-weighted 2-bit, extreme compression — significant quality loss | Fits: 128K context · ~279 tok/s | |
| 98.2 GB | 120.9 GB | 2-bit · i-quant extra small — Importance-weighted 2-bit, extra small | Fits: 128K context · ~271 tok/s | |
| 138.4 GB | 161 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 128K context · ~239 tok/s | |
| 158.4 GB | 181.1 GB | 3-bit medium — 3-bit medium quantization | Fits: 64K context · ~226 tok/s | |
| 194.6 GB | 217.3 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 64K context · ~205 tok/s | |
| 230.8 GB | 253.4 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 16K context · ~188 tok/s | |
| 266.9 GB | 289.6 GB | 6-bit, very good — 6-bit quantization, very good quality | Does not fit: needs 277.5 GB | |
| 323.1 GB | 345.8 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Does not fit: needs 333.7 GB |
Selected Q4_K_M
4-bit medium quantization205.2GBat 16K context
- Weights
- 192.8 GB
- KV cache
- 12.1 GB
- Overhead
- 0.3 GB
Fits NVIDIA DGX Station (GB300) · 46.8 GB free · ~205 tok/s
Or .
Which Hardware Can Run GLM 5.3 Flash?
Q4_K_M · 194.6 GBGLM 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. 4 devices with unified memory can run GLM 5.3 Flash, including Mac Studio M5 Ultra (512 GB), Mac Studio (M3 Ultra, 512GB), Mac Studio M5 Ultra (256 GB).
128–144 GB
Q4_K_M · best fit IQ2_S (112.8 GB)
4 GPUs · 2 Mac classes · 2 device families
GH200 Grace Hopper Superchip~232Simulate output on GH200 Grace Hopper SuperchipH200 NVL~230Simulate output on H200 NVLH200 SXM~230Simulate output on H200 SXMM2 Ultra*128 GB~52Simulate output on Mac Pro M2 Ultra (128 GB)M5 Max128 GB~44Simulate output on Mac Studio M5 Max (128 GB)GB10~28Simulate output on NVIDIA DGX SparkJetson T5000 (Thor)~28Simulate output on NVIDIA Jetson AGX Thor Developer Kit
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
#7 of 90 that fit ≤256 GB88 of 90 fit your NVIDIA DGX Station (GB300) (252 GB)
Each dot is a model at the best quant that fits ≤256 GB. The line steps up to the best Score available at each amount of VRAM; a dot below it is beaten by a model that needs no more memory.
Full ≤256 GB ranking →Benchmarks
The 6 boards that weigh most in the llmrun Score, of 31 with a result for this model; badges show who ran the test.
- GPQA Diamond
Epoch AI · Aug 2026Independent
Independent90.1%
#11/100 open
- LMArena Text
LMArena · Nov 2026Arena
Arena1475.3
#5/191 open
- LiveBench Coding
LiveBench · Jun 2026Independent
Independent79.0
#5/20 open
- LiveBench Reasoning
LiveBench · Jun 2026Independent
Independent77.6
#16/20 open
- AIME 2024/2025
Epoch AI · Aug 2026Independent
Independent93.9%
#11/88 open
- FrontierMath
Epoch AI · Aug 2026Independent
Independent55.8%
#11/19 open
In the ≤256 GB class
90 models fit ≤256 GBSame Score, only models that fit ≤256 GB at their best quant: the ranking a ≤256 GB owner actually chooses from.
- #1
Inkling 2 SmallThinking Machines
134.7
~86 tok/s · 255.2 GB
- #2
DeepSeek V4.1 Flash 1017DeepSeek
134.5
~107 tok/s · 229.5 GB
- #3
MiMo V2.6 Flash RLXiaomi
131.9
~83 tok/s · 186.5 GB
- #4
DeepSeek V4.1 FlashDeepSeek
129.4
~108 tok/s · 210.4 GB
- #5
Step 5 PreviewStepFun
128.0
~48 tok/s · 191.4 GB
- #6
DeepSeek V4 Flash 0731DeepSeek
127.6
~76 tok/s · 183.0 GB
Alternatives to GLM 5.3 Flash
GLM 5 family · 7 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
- 7.9 higher Score
- 18.7 GB less VRAM
Score 131.9 · 186.5 GB
Fits in less VRAM
Inkling Small
Thinking Machines
- 4.4 lower Score
- 42.5 GB less VRAM
Score 119.6 · 162.7 GB
Other vendor
Inkling 2 Small
Thinking Machines
- 10.7 higher Score
- 153.9 GB more VRAM
Score 134.7 · 359.1 GB
Similar models
10| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Step 5 PreviewStepFun | 128.0 | 407.5 GB | —needs > 24 GB | similar score |
| DeepSeek V4 Flash 0731DeepSeek | 127.6 | 183 GB | —needs > 24 GB | similar score |
| Qwen3.8 27BAlibaba | 121.9 | 17.9 GB | ~38 | similar score |
| Beam 1.0 501B A23Breflection | 119.5 | 304 GB | —needs > 24 GB | similar score |
| DeepSeek V4 FlashDeepSeek | 114.6 | 175.1 GB | —needs > 24 GB | similar score |
| MiniMax M3MiniMax | 112.2 | 258 GB | —needs > 24 GB | similar score |
| Qwen3.5 397B A17BAlibaba | 112.2 | 242.6 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 | 109.1 | 378.4 GB | —needs > 24 GB | similar score |
Fine-tunes & variants(3)
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
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
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
★ Recommended — best balance of quality and VRAM usage.
Can I run GLM 5.3 Flash on a Mac?
Yes — Mac Pro M2 Ultra (128 GB) and 16 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 M5 Ultra (256 GB).
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)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
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?
4 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), Mac Studio M5 Ultra (256 GB), Mac Studio M5 Ultra (512 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.