Qwen3-VL 30B A3B Instruct needs ~35.4 GB VRAM. MacBook Pro M4 Max 128GB has 92.2 GB. With Q4_K_M quantization, expect ~54 tok/s.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Fit status
Runs well
Decode
53.8 tok/s
TTFT
3599 ms
Safe context
256K
Memory
35.4 GB / 92.2 GB
This setup is broadly balanced for this model.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | S | Runs well | 53.8 tok/s | 1963 ms | 256K |
| Coding | S | Runs well | 53.8 tok/s | 3599 ms | 256K |
| Agentic Coding | S | Runs well | 53.8 tok/s | 5235 ms | 256K |
| Reasoning | S | Runs well | 53.8 tok/s | 4253 ms | 256K |
| RAG | S | Runs well | 53.8 tok/s | 6544 ms | 256K |
Inference speed
Estimated decode speed (tokens/sec) for Qwen3-VL 30B A3B Instruct at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~143 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q4_K_M | 142.7 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 119.8 | Offloads |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 108.1 | Offloads |
| RTX 3090 24GB | 24 GB | Q4_K_M | 102.5 | Offloads |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 87.1 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 72.6 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 68.8 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 53.8 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 53.8 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 37.5 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 34.4 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 32.9 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 23.7 | Too big |
| RTX 4070 12GB | 12 GB | Q4_K_M | 8.3 | Too big |
| RTX 3060 12GB | 12 GB | Q4_K_M | 5.2 | Too big |
| RTX 4060 8GB | 8 GB | Q4_K_M | 3.5 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How Qwen3-VL 30B A3B Instruct (30B params) fits at each quantization level on MacBook Pro M4 Max 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 11.7 GB | Low | A82 |
Q3_K_S | 3 | 14.7 GB | Low | A82 |
NVFP4 | 4 | 16.8 GB | Medium | A83 |
Q4_K_M | 4 | 18.3 GB | Medium | A83 |
Q5_K_M | 5 | 21.6 GB | High | A83 |
Q6_K | 6 | 24.6 GB | High | A84 |
Q8_0 | 8 | 32.1 GB | Very High | S85 |
F16Best for your GPU | 16 | 61.5 GB | Maximum | S90 |
Copy-paste commands to run Qwen3-VL 30B A3B Instruct on your machine.
Run
lms load Qwen3-VL-30B-A3B-Instruct && lms server startYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Devstral 2 123B Instruct | 123B | S | 7.5 tok/s | |
 Qwen3-Coder 30B A3B Instruct | 30.5B | S | 52 tok/s | |
| Qwen 3.5 122B A10B | 122B | S | 12.5 tok/s | |
| Qwen 3.6 35B A3B | 35B | S | 43.7 tok/s |
Yes, MacBook Pro M4 Max 128GB can run Qwen3-VL 30B A3B Instruct with a S grade (Runs well). Expected decode speed: 53.8 tok/s.
Qwen3-VL 30B A3B Instruct (30B parameters) requires approximately 35.4 GB of memory with Q4_K_M quantization.
The recommended quantization for Qwen3-VL 30B A3B Instruct is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M4 Max 128GB, Qwen3-VL 30B A3B Instruct achieves approximately 53.8 tokens per second decode speed with a time-to-first-token of 3599ms using Q4_K_M quantization.
For coding workloads, Qwen3-VL 30B A3B Instruct on MacBook Pro M4 Max 128GB receives a S grade with 53.8 tok/s and 256K context.
On MacBook Pro M4 Max 128GB, Qwen3-VL 30B A3B Instruct can safely use up to 256K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.
Not always. MacBook Pro M4 Max 128GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.
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