GLM 5.3 Flash DFlash2 — Hardware Requirements & GPU Compatibility
Chat
GLM 5.3 Flash DFlash2 is a 1.2B-parameter open language model from incoai in the GLM 5 family. It supports a context window of up to 1,048,576 tokens. At Q4_K_M it needs about 1.04 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 256K)Best on 24 GB (Q4_K_M · ~630 tok/s · 1M)Macs from 8 GB (Q4_K_M · ~69 tok/s)
cc-by-nc-nd-4.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run GLM 5.3 Flash DFlash2
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve incoai/GLM-5.3-Flash-DFlash2 --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (10 GB headroom) · Q4_K_M
- Generation speed
- ~225 tok/s
- generation speed
- Cost per 1M output tokens
- $0.10
- per 1M output tokens
How Much VRAM Does GLM 5.3 Flash DFlash2 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 0.8 GB | 1.5 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 1M context · ~780 tok/s | |
| 0.9 GB | 1.5 GB | 3-bit medium — 3-bit medium quantization | Fits: 1M context · ~720 tok/s | |
| 1.0 GB | 1.7 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 1M context · ~630 tok/s | |
| 1.2 GB | 1.8 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 1M context · ~555 tok/s | |
| 1.3 GB | 1.9 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 1M context · ~500 tok/s | |
| 1.5 GB | 2.1 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 1M context · ~434 tok/s | |
| 2.7 GB | 3.3 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 512K context · ~245 tok/s |
Selected Q4_K_M
4-bit medium quantization1.3GBat 16K context
- Weights
- 0.7 GB
- KV cache
- 0.3 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 22.7 GB free · ~630 tok/s
≥50 tok/s needs ≥80 GB/s · RTX 5090 ~1120, RTX 3090 Ti ~630
Which Hardware Can Run GLM 5.3 Flash DFlash2?
Q4_K_M · 1.0 GBGLM 5.3 Flash DFlash2 (Q4_K_M) requires 1.0 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 2+ GB is recommended. Using the full 1049K context window can add up to 21.4 GB, bringing total usage to 22.5 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run GLM 5.3 Flash DFlash2, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 6.7 GB free · up to 256K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~475Simulate output on RTX 3080RTX 2080 Ti~385Simulate output on RTX 2080 TiRTX 3070 Ti~380Simulate output on RTX 3070 TiM3*8 GB~69Simulate output on iMac 24" M3 (8 GB)Orin Nano~64Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~46Simulate output on iPhone 1720 GB
fits · 18.7 GB free · up to 512K ctx
1 GPU
RX 7900 XT~462Simulate output on RX 7900 XT32–40 GB
fits · 30.7 GB free · up to 1M ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~1120Simulate output on RTX 5090M5 Max36 GB~310Simulate output on Mac Studio M5 Max (36 GB)M532 GB~103Simulate output on MacBook Air 13" M5 (32 GB)Orin~128Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~84Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to GLM 5.3 Flash DFlash2
GLM 5 family · 6 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| MinerU2.5 Pro 2605 1.2Bopendatalab | — | 1.1 GB | ~642 | same size |
| MinerU2.5 Pro 2604 1.2Bopendatalab | — | 1.1 GB | ~642 | same size |
| Llama 3.2 1B InstructMeta | — | 2.3 GB | ~799 | same size |
| TinyLlama 1.1B Chat v1.0TinyLlama | — | 1 GB | ~649 | same size |
| HRM Text 1Bsapientinc | — | 3.1 GB | ~228 | same size |
| LFM2.5 1.2B ThinkingLiquid AI | — | 1.2 GB | ~612 | same size |
Fine-tunes & variants(1)
Specifications
- Publisher
- incoai
- Family
- GLM 5
- Parameters
- 1.2B
- Architecture
- DFlash2DraftModel
- Context Length
- 1,048,576 tokens
- Vocabulary Size
- 154,880
- Release Date
- 2026-08-27
- License
- cc-by-nc-nd-4.0
Frequently Asked Questions
How much VRAM does GLM 5.3 Flash DFlash2 need?
GLM 5.3 Flash DFlash2 requires 1.0 GB of VRAM at Q4_K_M, or 2.7 GB at BF16. Full 1049K context adds up to 21.4 GB (22.5 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 1.2B × 4.8 bits ÷ 8 = 0.7 GB
KV Cache + Overhead ≈ 0.3 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 ≈ 21.8 GB (at full 1049K context)
VRAM usage by quantization
What's the best quantization for GLM 5.3 Flash DFlash2?
For GLM 5.3 Flash DFlash2, Q4_K_M (1.0 GB) offers the best balance of quality and VRAM usage. Q5_K_M (1.2 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 0.8 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run GLM 5.3 Flash DFlash2 on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run GLM 5.3 Flash DFlash2. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 1.0 GB of usable unified memory (RAM minus macOS overhead).
Can I run GLM 5.3 Flash DFlash2 locally?
Yes — GLM 5.3 Flash DFlash2 can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is GLM 5.3 Flash DFlash2?
At Q4_K_M, GLM 5.3 Flash DFlash2 can reach ~4615 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~630 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 1.04 × 0.65 = ~5000 tok/s
Estimated speed at Q4_K_M (1.0 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 DFlash2?
At Q4_K_M, the download is about 0.70 GB. The full-precision BF16 version is 2.34 GB. The smallest option (Q2_K) is 0.50 GB.
Which GPUs can run GLM 5.3 Flash DFlash2?
52 consumer GPUs can run GLM 5.3 Flash DFlash2 at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
Which devices can run GLM 5.3 Flash DFlash2?
163 devices with unified memory can run GLM 5.3 Flash DFlash2 at Q4_K_M (1.0 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.