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 (M3 Ultra, 256GB) · Q4_K_M · 205.2 GB · ~35 tok/s
Macs from 128 GB (IQ2_S · ~52 tok/s)
Score 124.1#12 of 106 open#6 of 89 that fit ≤256 GBMIT
Not set up for it?
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
llmrun Composite Score →
124.1
- 90% range 122.3–125.9
- #12 of 106 open models
- #6 of 89 that fit ≤256 GB
Run GLM 5.3 Flash
Best fit for your Instinct MI350X. 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 Instinct MI350X
ollama run hf.co/batiai/GLM-5.3-Flash-GGUF:Q4_K_MQ4_K_M fits fully · 82.8 GB free · 16K context
Download: GLM-5.3-Flash-Q4_K_M-00001-of-00004.gguf188.67 GB + 3 partsbatiai
Ways to runOther ways
each opens step-by-stepRun in cloud
Rent 3× H100 SXM · Q4_K_M
- Estimated hourly price
- ~$8.07/hr
- est., whole server
- GPUs
- 3 GPUs
- 240 GB of VRAM
$5+ bonus for new users
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: 256K context · ~99 tok/s | |
| 98.2 GB | 120.9 GB | 2-bit · i-quant extra small — Importance-weighted 2-bit, extra small | Fits: 128K context · ~98 tok/s | |
| 138.4 GB | 161 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 128K context · ~94 tok/s | |
| 158.4 GB | 181.1 GB | 3-bit medium — 3-bit medium quantization | Fits: 128K context · ~92 tok/s | |
| 194.6 GB | 217.3 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 64K context · ~88 tok/s | |
| 230.8 GB | 253.4 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 64K context · ~85 tok/s | |
| 266.9 GB | 289.6 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 16K context · ~82 tok/s | |
| 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 Instinct MI350X · 82.8 GB free · ~88 tok/s
≥30 tok/s needs ≥552 GB/s · M3 Ultra ~35
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. 2 devices with unified memory can run GLM 5.3 Flash, including Mac Studio (M3 Ultra, 512GB), Mac Studio (M3 Ultra, 256GB).
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
#6 of 89 that fit ≤256 GBEvery model here fits your Instinct MI350X (288 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 · Oct 2026Arena
Arena1476.5
#4/188 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
89 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
DeepSeek V4.1 Flash 1017DeepSeek
134.4
~107 tok/s · 229.5 GB
- #2
MiMo V2.6 Flash RLXiaomi
131.9
~83 tok/s · 186.5 GB
- #3
DeepSeek V4.1 FlashDeepSeek
129.4
~108 tok/s · 210.4 GB
- #4
Step 5 PreviewStepFun
128.0
~48 tok/s · 191.4 GB
- #5
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
- 7.8 higher Score
- 18.7 GB less VRAM
Score 131.9 · 186.5 GB
Fits in less VRAM
Inkling Small
Thinking Machines
- 4.5 lower Score
- 42.5 GB less VRAM
Score 119.6 · 162.7 GB
Other vendor
Step 5 Preview
StepFun
- 3.9 higher Score
- 202.3 GB more VRAM
Score 128.0 · 407.5 GB
Similar models
10| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| DeepSeek V4 Flash 0731DeepSeek | 127.6 | 183 GB | —needs > 24 GB | similar score |
| Qwen3.8 27BAlibaba | 122.0 | 17.9 GB | ~38 | similar score |
| Beam 1.0 501B A23Breflection | 119.5 | 304 GB | —needs > 24 GB | similar score |
| DeepSeek V4 FlashDeepSeek | 115.3 | 175.1 GB | —needs > 24 GB | similar score |
| Qwen3.5 397B A17BAlibaba | 112.8 | 242.6 GB | —needs > 24 GB | similar score |
| MiniMax M3MiniMax | 112.6 | 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 | 109.7 | 378.4 GB | —needs > 24 GB | similar score |
| GLM 4.7Z.ai | 109.1 | 217.9 GB | —needs > 24 GB | same size |
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 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)
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?
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.