ToolSite Qwen3.8 2.4T-A95B VRAM requirements
Qwen3.8 2.4T-A95B has 2.45T parameters, of which about 95B are used per token; all 512 experts still have to be in memory. With an 8K-token context and one request it needs about 1,517 GB of GPU memory at Q4_K_M, 2,507 GB at FP8 and 5,013 GB at FP16/BF16. The published weights take 4,556 GB (BF16). No single GPU in the table holds it at Q4_K_M; it needs a multi-GPU setup.
Open Qwen3.8 2.4T-A95B 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) | 4,556 GB | 5,013 GB | More than 8 GPUs |
| FP16 / BF16 | 4,556 GB | 5,013 GB | More than 8 GPUs |
| FP8 / INT8 | 2,278 GB | 2,507 GB | More than 8 GPUs |
| INT4 (AWQ / GPTQ) | 1,210 GB | 1,333 GB | 8 × B200 |
| GGUF Q8_0 | 2,421 GB | 2,664 GB | More than 8 GPUs |
| GGUF Q6_K | 1,868 GB | 2,056 GB | More than 8 GPUs |
| GGUF Q5_K_M | 1,615 GB | 1,777 GB | More than 8 GPUs |
| GGUF Q4_K_M | 1,378 GB | 1,517 GB | More than 8 GPUs |
| GGUF Q3_K_M | 1,113 GB | 1,226 GB | 8 × B200 |
| GGUF Q2_K | 954 GB | 1,051 GB | 8 × H200 |
KV cache at long context
23 of its 92 layers use full attention and 69 are linear-attention layers with no growing cache. Each extra token of context adds 92 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 | 368 MB | 184 MB | 1,517 GB |
| 32K tokens | 2.88 GB | 1.44 GB | 1,520 GB |
| 128K tokens | 11.5 GB | 5.75 GB | 1,529 GB |
| 256K tokens | 23.0 GB | 11.5 GB | 1,542 GB |
Which GPUs can run Qwen3.8 2.4T-A95B
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 more than 8 | Needs more than 8 |
| RTX 3090 / 4090 | 24 GB | Needs more than 8 | Needs more than 8 |
| RTX 5090 | 32 GB | Needs more than 8 | Needs more than 8 |
| A100 40GB | 40 GB | Needs more than 8 | Needs more than 8 |
| Mac, 64 GB unified memory about 75% of it is usable by the GPU by default | 48 GB | Needs more than 8 | Needs more than 8 |
| L40S / RTX 6000 Ada | 48 GB | Needs more than 8 | Needs more than 8 |
| A100 / H100 80GB | 80 GB | Needs more than 8 | Needs more than 8 |
| Mac, 128 GB unified memory about 75% of it is usable by the GPU by default | 96 GB | Needs more than 8 | Needs more than 8 |
| H200 | 141 GB | Needs more than 8 | Needs more than 8 |
| B200 | 180 GB | Needs more than 8 | Needs more than 8 |
How fast Qwen3.8 2.4T-A95B 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 | — | — |
| H100 SXM | 3,350 GB/s | — | — |
| H200 | 4,800 GB/s | — | — |
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 | No | No | No |
| B200 | 180 GB | No | No | No |
Model details
- Parameters
- 2.45T (2,446,182,725,504)
- Experts
- 512 routed experts, all loaded
- Active per token
- 95B
- Layers
- 23 of its 92 layers use full attention and 69 are linear-attention layers with no growing cache
- Attention cache
- 4 KV heads × 256
- Context length
- 262,144 tokens
- Published weights
- 4,556 GB (BF16)
- On Hugging Face
- Qwen/Qwen3.8-2.4T-A95B
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Numbers read from the model files on Hugging Face on .