Qwen3.8 Flash Next 40B Prune Research — Hardware Requirements & GPU Compatibility
Chat
Qwen3.8 Flash Next 40B Prune Research is a 40.7B-parameter open language model from Davd-b01 in the Qwen 3.8 family. It supports a context window of up to 262,144 tokens. At Q4_K_M it needs about 24.79 GB of VRAM — see which GPUs and Macs can run it below.
1.2K downloads
2 likesSpecifications
- Publisher
- Davd-b01
- Family
- Qwen 3.8
- Parameters
- 40.7B
- Architecture
- Qwen4ExpForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-09-19
- License
- Apache 2.0
Get Started
How Much VRAM Does Qwen3.8 Flash Next 40B Prune Research Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 17.7 GB | 28.3 GB | 17.28 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 20.2 GB | 30.9 GB | 19.83 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 24.8 GB | 35.4 GB | 24.40 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 29.4 GB | 40.0 GB | 28.98 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 33.9 GB | 44.6 GB | 33.55 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 41.0 GB | 51.7 GB | 40.67 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 81.7 GB | 92.4 GB | 81.34 GB | Brain floating point 16 — preferred for training |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Qwen3.8 Flash Next 40B Prune Research?
Q4_K_M · 24.8 GBQwen3.8 Flash Next 40B Prune Research (Q4_K_M) requires 24.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 33+ GB is recommended. Using the full 262K context window can add up to 10.6 GB, bringing total usage to 35.4 GB. 1 GPU can run it, including NVIDIA GeForce RTX 5090.
All compatible consumer-level GPUs are running near their VRAM limit. You may also want to consider professional GPUs (e.g., NVIDIA A100, H100) which offer significantly more VRAM. For more headroom and better throughput, consider a multi-GPU configuration with tensor parallelism (supported by tools like vLLM, llama.cpp, or text-generation-inference).
Decent
— Enough VRAM, may be tightWhich Devices Can Run Qwen3.8 Flash Next 40B Prune Research?
Q4_K_M · 24.8 GB32 devices with unified memory can run Qwen3.8 Flash Next 40B Prune Research, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M4 Max (36 GB).
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Qwen3.8 Flash Next 40B Prune Research need?
Qwen3.8 Flash Next 40B Prune Research requires 24.8 GB of VRAM at Q4_K_M, or 81.7 GB at BF16. Full 262K context adds up to 10.6 GB (35.4 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 40.7B × 4.8 bits ÷ 8 = 24.4 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 11 GB (at full 262K context)
VRAM usage by quantization
Q4_K_M24.8 GBQ4_K_M + full context35.4 GB- Can NVIDIA GeForce RTX 4090 run Qwen3.8 Flash Next 40B Prune Research?
Yes, at Q3_K_M (20.2 GB) or lower. Higher quantizations like Q4_K_M (24.8 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Qwen3.8 Flash Next 40B Prune Research?
For Qwen3.8 Flash Next 40B Prune Research, Q4_K_M (24.8 GB) offers the best balance of quality and VRAM usage. Q5_K_M (29.4 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 17.7 GB.
VRAM requirement by quantization
Q2_K17.7 GBQ4_K_M ★24.8 GBQ5_K_M29.4 GBQ6_K33.9 GBQ8_041.0 GBBF1681.7 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen3.8 Flash Next 40B Prune Research on a Mac?
Qwen3.8 Flash Next 40B Prune Research requires at least 17.7 GB at Q2_K, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Qwen3.8 Flash Next 40B Prune Research locally?
Yes — Qwen3.8 Flash Next 40B Prune Research can run locally on consumer hardware. At Q4_K_M quantization it needs 24.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen3.8 Flash Next 40B Prune Research?
At Q4_K_M, Qwen3.8 Flash Next 40B Prune Research can reach ~89 tok/s on AMD Instinct MI350X. 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 ÷ 24.8 × 0.65 = ~241 tok/s
Estimated speed at Q4_K_M (24.8 GB)
~241 tok/s~241 tok/s~196 tok/sReal-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 40B Prune Research?
At Q4_K_M, the download is about 24.40 GB. The full-precision BF16 version is 81.34 GB. The smallest option (Q2_K) is 17.28 GB.
- Which GPUs can run Qwen3.8 Flash Next 40B Prune Research?
1 consumer GPU can run Qwen3.8 Flash Next 40B Prune Research at Q4_K_M (24.8 GB). Top options include NVIDIA GeForce RTX 5090.
- Which devices can run Qwen3.8 Flash Next 40B Prune Research?
35 devices with unified memory can run Qwen3.8 Flash Next 40B Prune Research at Q4_K_M (24.8 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (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.