How Much VRAM Does Qwen3.8 Flash Next Need to Run Locally?
See devices that run itQwen3.8 Flash Next is a MoE model with 176B total parameters and 6B active per token. At Q4 it needs 100GB of VRAM to sit entirely on the GPU; with experts offloaded to system RAM it needs only 4GB of VRAM plus 97GB of RAM. The cheapest device that holds it is the DGX Spark 128GB, with no used-price data yet, at roughly 44.7 tokens/s. In offload mode the cheapest card that runs it is the GeForce RTX 2080 Ti, at roughly 21 tokens/s.
Qwen3.8 Flash Next details
VRAM needed for Qwen3.8 Flash Next by quantization and context
Total at 8KMoE: all 176B parameters must stay resident in memory, while speed is set by the 6B active per token.
| Quantization | WeightsGB | Total at 8KGB | Total at 32KGB | Total at 128KGB |
|---|---|---|---|---|
| 1-bit | 74.5GGUF | 42.5 | 43.1 | 45.3 |
| 2-bit | 78.9GGUF | 66.6 | 67.2 | 69.4 |
| 3-bit | 90GGUF | 85.5 | 86.1 | 88.3 |
| 4-bit | 111.3GGUF | 99.3 | 99.9 | 102.1 |
| 5-bit | 158.3GGUF | 119.9 | 120.5 | 122.8 |
| 8-bit | 188.2GGUF | 183.6 | 184.2 | 186.4 |
| FP16 | 354GGUF | 345.4 | 346 | 348.2 |
Total = weights + KV cache (FP16) + 1GB runtime overhead. Weights marked GGUF are measured file sizes from the repository; the rest are estimated from bytes per parameter. Contexts beyond this model's 256K limit show a dash. Each tier takes whichever file of that bit width the repository has, preferring unsloth dynamic quants, so the exact file name varies by model.
Which GPUs can run Qwen3.8 Flash Next?
Q4_K_M · 8K context · sorted by eBay used price| Device | VRAMGB | Used price | Est. t/s | Suggested context |
|---|---|---|---|---|
 DGX Spark 128GB | 128 | —No listings | 44.7 | 128K |
| Mac Studio M4 Max 128GB | 128 | —No listings | 63.7 | 128K |
| Mac Studio M5 Max 128GB | 128 | —No listings | 69.4 | 128K |
| MacBook Pro M4 Max 128GB | 128 | —No listings | 63.7 | 128K |
| MacBook Pro M5 Max 128GB | 128 | —No listings | 69.4 | 128K |
 Ryzen AI Max+ 395 128GB | 128 | —No listings | 31.8 | 128K |
| Mac Studio M3 Ultra 512GB | 512 | —No listings | 84.7 | 128K |
Fits when weights + KV cache + runtime overhead is at or below usable memory, single card. "Offload" on MoE models means it runs with expert weights in system RAM (only attention layers and KV cache stay in VRAM); that speed assumes 70 GB/s RAM bandwidth. The context column is the largest tier that fits.
GPUs that run Qwen3.8 Flash Next with experts offloaded to RAM
Expert weights live in system RAM (needs at least 97 GB); VRAM holds only attention layers, shared experts and the KV cache, at least 4 GB at Q4. Speed is bound by RAM bandwidth (estimated at 70 GB/s) and is far slower than a full-VRAM setup.
| Device | VRAMGB | Used price | RAM neededGB | Est. t/s |
|---|---|---|---|---|
| GeForce RTX 2080 Ti | 11 | — | 97 | 21.2 |
| GeForce RTX 4080 | 16 | — | 97 | 21.5 |
| GeForce RTX 5070 Ti | 16 | — | 97 | 21.8 |
| GeForce RTX 5080 | 16 | — | 97 | 21.9 |
| Radeon RX 7800 XT | 16 | — | 97 | 20.6 |
| Radeon RX 9070 XT | 16 | — | 97 | 20.7 |
| Tesla V100 16GB | 16 | — | 97 | 21.8 |
| Radeon RX 7900 XT | 20 | — | 97 | 21.1 |
 Arc Pro B60 | 24 | — | 97 | 19.6 |
| GeForce RTX 3090 | 24 | — | 97 | 21.8 |
| GeForce RTX 4090 | 24 | — | 97 | 21.9 |
| GeForce RTX 5090 D V2 | 24 | — | 97 | 22.2 |
| Radeon RX 7900 XTX | 24 | — | 97 | 21.4 |
| RTX PRO 4000 Blackwell | 24 | — | 97 | 21.4 |
| Arc Pro B65 | 32 | — | 97 | 20.3 |
| Arc Pro B70 | 32 | — | 97 | 20.3 |
| GeForce RTX 4080 SUPER 32GB (modded) | 32 | — | 97 | 21.5 |
| GeForce RTX 5090 | 32 | — | 97 | 22.4 |
| GeForce RTX 5090 D | 32 | — | 97 | 22.4 |
| Instinct MI100 | 32 | — | 97 | 21.8 |
| Radeon AI PRO R9700 | 32 | — | 97 | 20.7 |
| Radeon PRO W7800 | 32 | — | 97 | 20.5 |
| RTX PRO 4500 Blackwell | 32 | — | 97 | 21.8 |
| Tesla V100 32GB | 32 | — | 97 | 21.8 |
| A100 40GB PCIe | 40 | — | 97 | 22.3 |
| CMP 170HX 40GB (modded) | 40 | — | 97 | 22.3 |
| GeForce RTX 4090 48GB (modded) | 48 | — | 97 | 21.9 |
| Radeon PRO W7900 | 48 | — | 97 | 21.2 |
| RTX PRO 5000 Blackwell 48GB | 48 | — | 97 | 22.2 |
| CMP 170HX 64GB (modded) | 64 | — | 97 | 22.3 |
| Instinct MI210 | 64 | — | 97 | 22.1 |
| RTX PRO 5000 Blackwell 72GB | 72 | — | 97 | 22.2 |
| RTX PRO 6000D | 84 | — | 97 | 22.3 |
| RTX PRO 6000 Blackwell | 96 | — | 97 | 22.4 |
Run Qwen3.8 Flash Next with Ollama, llama.cpp or vLLM
The Ollama library has no official tag for it yet.
llama.cpp pulls the GGUF straight from Hugging Face; -c sets the context length.
llama-server -hf unsloth/Qwen3.8-Flash-Next-GGUF:Q4_K_M -c 8192
vLLM serves the original-precision weights, which needs far more memory than GGUF and usually more than one GPU.
vllm serve Qwen/Qwen3.8-Flash-Next
File names follow that week's model-library snapshot; the definitions are in the methodology. Methodology
FAQ
How much VRAM does Qwen3.8 Flash Next need?
At Q4_K_M with an 8K context Qwen3.8 Flash Next needs about 100GB of VRAM; 120GB leaves comfortable headroom.
What is the cheapest GPU that runs Qwen3.8 Flash Next?
The DGX Spark 128GB: 128GB of VRAM at roughly 44.7 tokens/s. We have no used-price data for it yet.
Can you run Qwen3.8 Flash Next with Ollama?
Not yet: the Ollama library has no official tag for it. You can load the GGUF with llama.cpp instead.