Qwen3.5 35B A3B needs ~28.0 GB but Radeon RX 7900M 16GB only has 16.0 GB. Try a smaller quantization or lighter model.
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
12.0 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
3.7 tok/s
TTFT
52478 ms
Safe context
4K
Memory
28.0 GB / 16.0 GB
Offload
40%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 28.0 GB, but this setup only exposes 16.0 GB of usable VRAM.
Add more VRAM headroom
The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 4.3 tok/s | 24382 ms | 4K |
| Coding | F | Too heavy | 3.7 tok/s | 52478 ms | 4K |
| Agentic Coding | F | Too heavy | 2.8 tok/s | 101831 ms | 4K |
| Reasoning | F | Too heavy | 3.7 tok/s | 62020 ms | 4K |
| RAG | F | Too heavy | 2.8 tok/s | 127289 ms | 4K |
Inference speed
Estimated decode speed (tokens/sec) for Qwen3.5 35B A3B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~56 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 | 56.2 | Tight |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 28.1 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 28.1 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 26.1 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 21.7 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 20.6 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 17.7 | Tight |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 16.6 | Heavy offload |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 11.2 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 10.6 | Heavy offload |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 10.3 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 9.7 | Heavy offload |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 6.5 | Too big |
| RTX 4070 12GB | 12 GB | Q4_K_M | 2.7 | Too big |
| RTX 3060 12GB | 12 GB | Q4_K_M | 2.0 | Too big |
| RTX 4060 8GB | 8 GB | Q4_K_M | 2.0 | 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 Qwen3.5 35B A3B (35B params) fits at each quantization level on Radeon RX 7900M 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 13.7 GB | Low | F0 |
Q3_K_S | 3 | 17.2 GB | Low | F0 |
NVFP4 | 4 | 19.6 GB | Medium | F0 |
Q4_K_M | 4 | 21.3 GB | Medium | F0 |
Q5_K_M | 5 | 25.2 GB | High | F0 |
Q6_K | 6 | 28.7 GB | High | F0 |
Q8_0 | 8 | 37.5 GB | Very High | F0 |
F16 | 16 | 71.8 GB | Maximum | F0 |
Upgrade options
Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 349%.
~$999 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$1,899 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$2,249 MSRP
NVIDIA A100 40GBBiggest leapMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$10,000 MSRP
No, Qwen3.5 35B A3B requires more memory than Radeon RX 7900M 16GB provides.
Qwen3.5 35B A3B (35B parameters) requires approximately 28.0 GB of memory with Q4_K_M quantization.
The recommended quantization for Qwen3.5 35B A3B is Q4_K_M, which balances quality and memory efficiency.
On Radeon RX 7900M 16GB, Qwen3.5 35B A3B achieves approximately 3.7 tokens per second decode speed with a time-to-first-token of 52478ms using Q4_K_M quantization.
For coding workloads, Qwen3.5 35B A3B on Radeon RX 7900M 16GB receives a F grade with 3.7 tok/s and 4K context.
On Radeon RX 7900M 16GB, Qwen3.5 35B A3B can safely use up to 4K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-lmstudio-community--qwen3-5-35b-a3b-gguf-on-rx-7900m-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
