NeuroGenesysCan I run Qwen3 30B-A3B?
Last updated • By Will Schulz
Qwen3 30B-A3B needs about 21.0 GB of memory at 4-bit (Q4_K_M) with an 8K-token context, so it fits on a single 24 GB GPU such as the RTX 3090, RTX 4090 or L4, or runs on a Mac with 32 GB of unified memory. At 16-bit it needs 64.6 GB, so it fits on a single 80 GB GPU such as the A100 80GB, H100 PCIe or H100 SXM, or runs on a Mac with 96 GB of unified memory.
How much memory does Qwen3 30B-A3B need?
| Precision | Weights | Total, 8K context | Total, 32K context | Total, 128K context |
|---|---|---|---|---|
| 16-bit (BF16 / FP16) | 61.0 GB | 64.6 GB | 67.1 GB | 76.7 GB |
| 8-bit (Q8_0) | 32.4 GB | 35.2 GB | 37.6 GB | 47.3 GB |
| 4-bit (Q4_K_M) | 18.6 GB | 21.0 GB | 23.4 GB | 33.1 GB |
Total = weights + FP16 KV cache + 3% of the weights + 1 GB of runtime overhead, as in the VRAM calculator. Longer contexts are only usable up to the model's own context window; check the model card.
Which GPUs can run Qwen3 30B-A3B?
| GPU memory | 16-bit (BF16 / FP16) | 8-bit (Q8_0) | 4-bit (Q4_K_M) |
|---|---|---|---|
| 8 GB | more than 8 | 8 cards | 4 cards |
| 12 GB | 8 cards | 4 cards | 2 cards |
| 16 GB | 8 cards | 4 cards | 2 cards |
| 24 GBRTX 3090, RTX 4090, L4 | 4 cards | 2 cards | fits on 1 |
| 32 GBRTX 5090 | 4 cards | 2 cards | fits on 1 |
| 40 GBA100 40GB | 2 cards | fits on 1 | fits on 1 |
| 48 GBRTX A6000, RTX 6000 Ada, L40S | 2 cards | fits on 1 | fits on 1 |
| 80 GBA100 80GB, H100 PCIe, H100 SXM | fits on 1 | fits on 1 | fits on 1 |
| 141 GBH200 | fits on 1 | fits on 1 | fits on 1 |
| 180 GBB200 | fits on 1 | fits on 1 | fits on 1 |
| 192 GBAMD MI300X | fits on 1 | fits on 1 | fits on 1 |
Assumes the runtime can allocate 95% of each card and every card pays the fixed overhead. Vendor memory sizes are binary (a 24 GB card holds 25.8 × 10⁹ bytes), and that is accounted for.
Can I run Qwen3 30B-A3B on a Mac?
| Unified memory | 16-bit (BF16 / FP16) | 8-bit (Q8_0) | 4-bit (Q4_K_M) |
|---|---|---|---|
| 24 GBM4 Pro | no | no | no |
| 32 GBM2 Max | no | no | fits |
| 48 GBM3 Max (40-core GPU), M4 Pro, M4 Max (40-core GPU) | no | fits | fits |
| 64 GBM2 Max, M2 Ultra, M3 Max (40-core GPU), M4 Pro, M4 Max (40-core GPU) | no | fits | fits |
| 96 GBM2 Max, M3 Ultra | fits | fits | fits |
| 128 GBM2 Ultra, M3 Max (40-core GPU), M4 Max (40-core GPU) | fits | fits | fits |
| 192 GBM2 Ultra | fits | fits | fits |
| 256 GBM3 Ultra | fits | fits | fits |
| 512 GBM3 Ultra | fits | fits | fits |
macOS lets the GPU use about 75% of unified memory by default; you can raise the limit with sudo sysctl iogpu.wired_limit_mb.
How fast does Qwen3 30B-A3B run at 4-bit?
| Hardware | Memory bandwidth | Tokens / s | |
|---|---|---|---|
| RTX 309024 GB | 936 GB/s | 271 | estimator |
| RTX 409024 GB | 1,008 GB/s | 292 | estimator |
| RTX 509032 GB | 1,792 GB/s | 518 | estimator |
| RTX A600048 GB | 768 GB/s | 222 | estimator |
| RTX 6000 Ada48 GB | 960 GB/s | 278 | estimator |
| L424 GB | 300 GB/s | 86.8 | estimator |
| L40S48 GB | 864 GB/s | 250 | estimator |
| A100 40GB40 GB | 1,555 GB/s | 450 | estimator |
| A100 80GB80 GB | 2,039 GB/s | 590 | estimator |
| H100 PCIe80 GB | 2,000 GB/s | 579 | estimator |
| H100 SXM80 GB | 3,350 GB/s | 969 | estimator |
| H200141 GB | 4,800 GB/s | 1389 | estimator |
| B200180 GB | 8,000 GB/s | 2314 | estimator |
| AMD MI300X192 GB | 5,300 GB/s | 1533 | estimator |
| Apple M2 Max32 GB config or larger | 400 GB/s | 107 | estimator |
| Apple M2 Ultra64 GB config or larger | 800 GB/s | 215 | estimator |
| Apple M3 Max (40-core GPU)48 GB config or larger | 400 GB/s | 107 | estimator |
| Apple M3 Ultra96 GB config or larger | 819 GB/s | 220 | estimator |
| Apple M4 Pro48 GB config or larger | 273 GB/s | 73.3 | estimator |
| Apple M4 Max (40-core GPU)48 GB config or larger | 546 GB/s | 147 | estimator |
Decoding reads the active expert weights and the KV cache once per token, so speed is roughly memory bandwidth ÷ bytes per token. Assumes 70% of peak bandwidth on GPUs and 65% on Macs, one GPU or Mac holding the whole model. Only hardware that holds it alone is listed.
Questions
How much VRAM does Qwen3 30B-A3B need?
About 21.0 GB at 4-bit (Q4_K_M), 35.2 GB at 8-bit (Q8_0) and 64.6 GB at 16-bit (BF16), for one request with an 8K-token context. At 4-bit the weights are 18.6 GB; the rest is the KV cache and runtime overhead. With 128K tokens of context the 4-bit total rises to 33.1 GB.
Can I run Qwen3 30B-A3B on a 24 GB GPU like the RTX 4090?
Yes, at 4-bit (Q4_K_M) with an 8K context; 16-bit does not fit on one card. A 24 GB card such as the RTX 3090, RTX 4090 or L4 holds it with the runtime's usual headroom.
Can I run Qwen3 30B-A3B on a Mac?
Yes: at 4-bit it needs a Mac with at least 32 GB of unified memory, and at 16-bit 96 GB. By default macOS lets the GPU use about 75% of unified memory, which these figures allow for.
How fast does Qwen3 30B-A3B run?
At 4-bit, one request generates about 292 tokens per second on a RTX 4090 and 147 tokens per second on an Apple M4 Max (40-core GPU). These are estimates from memory bandwidth (70% of peak on GPUs, 65% on Macs) with 4K tokens in context; real runtimes vary.
Why does Qwen3 30B-A3B need so much memory if only 3.3B parameters are active?
Qwen3 30B-A3B is a mixture-of-experts model: each token uses about 3.3B of its 30.5B parameters, but any token can be routed to any expert, so all 30.5B must be in memory. Memory follows the total; speed follows the active count, which is why it runs faster than a dense model of the same size.
Work it out for your setup
- Free toolVRAM calculator: Qwen3 30B-A3B at Q4_K_MChange the quantization, context, KV-cache precision and batch size.
- Free toolLLM inference speed estimatorTokens per second, time to first token and batching on any GPU or Mac.
- GuideCan I run it? All open-weight modelsMemory needed for every model we track, and what each GPU or Mac can run.
Sources
- Qwen3 30B-A3B model card and config.json — Hugging Face
