Qwen3.8 Flash Next DFlash2 — Hardware Requirements & GPU Compatibility
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Qwen3.8 Flash Next DFlash2 is a 603M-parameter open language model from incoai in the Qwen 3.8 family. It supports a context window of up to 262,144 tokens. At Q4_K_M it needs about 0.69 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 · ~950 tok/s · 256K)Macs from 8 GB (Q4_K_M · ~104 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Qwen3.8 Flash Next DFlash2
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve incoai/Qwen3.8-Flash-Next-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 (11 GB headroom) · Q4_K_M
- Generation speed
- ~339 tok/s
- generation speed
- Cost per 1M output tokens
- $0.058
- per 1M output tokens
How Much VRAM Does Qwen3.8 Flash Next DFlash2 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 0.6 GB | 1 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 256K context · ~1130 tok/s | |
| 0.6 GB | 1 GB | 3-bit medium — 3-bit medium quantization | Fits: 256K context · ~1057 tok/s | |
| 0.7 GB | 1.1 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 256K context · ~950 tok/s | |
| 0.8 GB | 1.2 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 256K context · ~862 tok/s | |
| 0.8 GB | 1.2 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 256K context · ~799 tok/s | |
| 0.9 GB | 1.3 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 256K context · ~705 tok/s | |
| 1.5 GB | 1.9 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 256K context · ~428 tok/s |
Selected Q4_K_M
4-bit medium quantization0.9GBat 16K context
- Weights
- 0.4 GB
- KV cache
- 0.2 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 23.1 GB free · ~950 tok/s
≥50 tok/s needs ≥53 GB/s · RTX 5090 ~1688, RTX 3090 Ti ~950
Which Hardware Can Run Qwen3.8 Flash Next DFlash2?
Q4_K_M · 0.7 GBQwen3.8 Flash Next DFlash2 (Q4_K_M) requires 0.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 1+ GB is recommended. Using the full 262K context window can add up to 3.3 GB, bringing total usage to 4.0 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run Qwen3.8 Flash Next DFlash2, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 7.1 GB free · up to 256K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~716Simulate output on RTX 3080RTX 2080 Ti~580Simulate output on RTX 2080 TiRTX 3070 Ti~573Simulate output on RTX 3070 TiM3*8 GB~104Simulate output on iMac 24" M3 (8 GB)Orin Nano~96Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~69Simulate output on iPhone 1720 GB
fits · 19.1 GB free · up to 256K ctx
1 GPU
RX 7900 XT~696Simulate output on RX 7900 XT32–40 GB
fits · 31.1 GB free · up to 256K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~1688Simulate output on RTX 5090M5 Max36 GB~467Simulate output on Mac Studio M5 Max (36 GB)M532 GB~156Simulate output on MacBook Air 13" M5 (32 GB)Orin~193Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~127Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Qwen3.8 Flash Next DFlash2
Qwen 3.8 family · 12 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| D1 Omni 600MLiquid AI | — | 1.2 GB | ~910 | same size |
| T5gemma B B Ul2 ITGoogle | — | 2.5 GB | ~504 | same size |
| Qwen3 0.6B BaseAlibaba | — | 1.6 GB | ~840 | same size |
| Qwen1.5 0.5B ChatAlibaba | — | 2.3 GB | ~753 | same size |
| Bloomz 560MBigScience | — | 0.4 GB | ~1771 | same size |
| Bloom 560MBigScience | — | 1.6 GB | ~1771 | same size |
Specifications
- Publisher
- incoai
- Family
- Qwen 3.8
- Parameters
- 603M
- Architecture
- DFlash2DraftModel
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-10-19
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does Qwen3.8 Flash Next DFlash2 need?
Qwen3.8 Flash Next DFlash2 requires 0.7 GB of VRAM at Q4_K_M, or 1.5 GB at BF16. Full 262K context adds up to 3.3 GB (4.0 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 603M × 4.8 bits ÷ 8 = 0.4 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 ≈ 3.6 GB (at full 262K context)
VRAM usage by quantization
What's the best quantization for Qwen3.8 Flash Next DFlash2?
For Qwen3.8 Flash Next DFlash2, Q4_K_M (0.7 GB) offers the best balance of quality and VRAM usage. Q5_K_M (0.8 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 0.6 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Qwen3.8 Flash Next DFlash2 on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Qwen3.8 Flash Next DFlash2. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 0.7 GB of usable unified memory (RAM minus macOS overhead).
Can I run Qwen3.8 Flash Next DFlash2 locally?
Yes — Qwen3.8 Flash Next DFlash2 can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Qwen3.8 Flash Next DFlash2?
At Q4_K_M, Qwen3.8 Flash Next DFlash2 can reach ~6957 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~950 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 ÷ 0.69 × 0.65 = ~7536 tok/s
Estimated speed at Q4_K_M (0.7 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Qwen3.8 Flash Next DFlash2?
At Q4_K_M, the download is about 0.36 GB. The full-precision BF16 version is 1.21 GB. The smallest option (Q2_K) is 0.26 GB.
Which GPUs can run Qwen3.8 Flash Next DFlash2?
52 consumer GPUs can run Qwen3.8 Flash Next 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 Qwen3.8 Flash Next DFlash2?
163 devices with unified memory can run Qwen3.8 Flash Next DFlash2 at Q4_K_M (0.7 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.