ToolSite Qwen3.8 Flash Next VRAM requirements
Qwen3.8 Flash Next has 180.0B parameters, of which about 6B are used per token; all 512 experts still have to be in memory. With an 8K-token context and one request it needs about 112 GB of GPU memory at Q4_K_M, 185 GB at FP8 and 370 GB at FP16/BF16. The published weights take 335 GB (BF16). It does not fit on a 24 GB card at Q4_K_M; the smallest single GPU that holds it with an 8K context is the 141 GB H200.
Open Qwen3.8 Flash Next in the calculator
VRAM by quantization
Weights plus the KV cache for 8,192 tokens in FP16 and the runtime overhead (0.5 GB plus 10%). Each row opens the calculator with that setting. What the GGUF names mean.
| Precision | Weights | Total | Smallest setup |
|---|---|---|---|
| As published (BF16) | 335 GB | 370 GB | 4 × H200 |
| FP16 / BF16 | 335 GB | 370 GB | 4 × H200 |
| FP8 / INT8 | 168 GB | 185 GB | 2 × H200 |
| INT4 (AWQ / GPTQ) | 89.1 GB | 98.7 GB | H200 |
| GGUF Q8_0 | 178 GB | 197 GB | 2 × H200 |
| GGUF Q6_K | 137 GB | 152 GB | B200 |
| GGUF Q5_K_M | 119 GB | 131 GB | H200 |
| GGUF Q4_K_M | 101 GB | 112 GB | H200 |
| GGUF Q3_K_M | 81.9 GB | 90.8 GB | H200 |
| GGUF Q2_K | 70.2 GB | 77.9 GB | A100 / H100 80GB |
KV cache at long context
12 of its 48 layers use full attention and 36 are linear-attention layers with no growing cache. Each extra token of context adds 24 KB of FP16 cache per request. How the KV cache works.
| Context | KV cache, FP16 | KV cache, FP8 | Total at Q4_K_M |
|---|---|---|---|
| 4K tokens | 96 MB | 48 MB | 112 GB |
| 32K tokens | 768 MB | 384 MB | 113 GB |
| 128K tokens | 3.00 GB | 1.50 GB | 115 GB |
| 256K tokens | 6.00 GB | 3.00 GB | 119 GB |
Which GPUs can run Qwen3.8 Flash Next
With 8,192 tokens of context. Several GPUs means one tensor-parallel group of 2, 4 or 8 cards.
| GPU | Memory | Q4_K_M | FP8 |
|---|---|---|---|
| RTX 3060 | 12 GB | Needs more than 8 | Needs more than 8 |
| RTX 4060 Ti 16GB | 16 GB | Needs 8 | Needs more than 8 |
| RTX 3090 / 4090 | 24 GB | Needs 8 | Needs 8 |
| RTX 5090 | 32 GB | Needs 4 | Needs 8 |
| A100 40GB | 40 GB | Needs 4 | Needs 8 |
| Mac, 64 GB unified memory about 75% of it is usable by the GPU by default | 48 GB | Needs 4 | Needs 4 |
| L40S / RTX 6000 Ada | 48 GB | Needs 4 | Needs 4 |
| A100 / H100 80GB | 80 GB | Needs 2 | Needs 4 |
| Mac, 128 GB unified memory about 75% of it is usable by the GPU by default | 96 GB | Needs 2 | Needs 2 |
| H200 | 141 GB | Fits on one | Needs 2 |
| B200 | 180 GB | Fits on one | Needs 2 |
How fast Qwen3.8 Flash Next writes
Tokens per second for one request with 8,192 tokens of context, estimated from memory bandwidth and the parameters read per token. A dash means it does not fit on one card. Try other settings in the speed calculator.
| Hardware | Bandwidth | Q4_K_M | FP8 |
|---|---|---|---|
| RTX 3060 12GB | 360 GB/s | — | — |
| RTX 4090 | 1,008 GB/s | — | — |
| RTX 5090 | 1,792 GB/s | — | — |
| M4 Max Mac (128 GB) | 546 GB/s | — | — |
| M3 Ultra Mac Studio (512 GB) | 819 GB/s | 59–101 | 37–64 |
| H100 SXM | 3,350 GB/s | — | — |
| H200 | 4,800 GB/s | 240–477 | — |
Longest context on one GPU
How many tokens of context fit on a single card with one request and an FP16 KV cache. "Full" means the model's whole context window fits.
| GPU | Memory | Q4_K_M | Q8_0 | FP8 |
|---|---|---|---|---|
| RTX 3060 | 12 GB | No | No | No |
| RTX 4060 Ti 16GB | 16 GB | No | No | No |
| RTX 3090 / 4090 | 24 GB | No | No | No |
| RTX 5090 | 32 GB | No | No | No |
| A100 40GB | 40 GB | No | No | No |
| Mac, 64 GB unified memory | 48 GB | No | No | No |
| L40S / RTX 6000 Ada | 48 GB | No | No | No |
| A100 / H100 80GB | 80 GB | No | No | No |
| Mac, 128 GB unified memory | 96 GB | No | No | No |
| H200 | 141 GB | 256K (full) | No | No |
| B200 | 180 GB | 256K (full) | No | No |
Model details
- Parameters
- 180.0B (179,999,981,459)
- Experts
- 512 routed experts, all loaded
- Active per token
- 6B
- Layers
- 12 of its 48 layers use full attention and 36 are linear-attention layers with no growing cache
- Attention cache
- 2 KV heads × 256
- Context length
- 262,144 tokens
- Published weights
- 335 GB (BF16)
- On Hugging Face
- Qwen/Qwen3.8-Flash-Next
Other models
- DeepSeek V4 Flash 0731
- Qwen3.6 35B-A3B
- Qwen3.6 27B
- Qwen3.8 27B
- Qwen3.8 2.4T-A95B
- Qwen3.5 9B
- Qwen3.5 122B-A10B
- Qwen3-Coder-Next
- DeepSeek V4.1 Flash
- DeepSeek V4 Flash
- DeepSeek V4 Pro
- DeepSeek V3.2
- GLM-5.3
- GLM-5.3 Flash
- GLM-5.2
- GLM-4.7 Flash
- Gemma 4 31B
- Gemma 4 26B-A4B
- Gemma 4 12B
- Gemma 4 E4B
- Kimi K3
- MiniMax M3
- MiniMax M2.7
- MiMo V2.6 Flash
- MiMo V2.6 Pro
- Mistral Medium 3.5 128B
- Nemotron 3 Nano 4B
- Nemotron 3 Nano 30B-A3B
- Nemotron 3 Super 120B-A12B
- Xing 4.0 29B-A4B
- Muse Glimmer 30B
- MiniCPM5 2B
- Llama 3.1 8B
- Llama 3.1 70B
- Qwen3 8B
- Qwen3 30B-A3B
- gpt-oss-20b
- gpt-oss-120b
- DeepSeek V3 / R1
Numbers read from the model files on Hugging Face on .